AI4Science Updates

This is normal bad work

Tue, 13 Oct 2026 03:14:00 +0000

[This is a guest post by Sophie Huiberts , being an expanded version of a post on her own blog . This blog post was initially written in a different file format and converted using AI. — T.] When you prove a theorem, you are imparted a responsibility to the field of mathematics. At minimum you need to explain what you did, including which steps required new ideas and which steps are already well-known. More substantively, you are expected to nurture the literature. If an important idea was poorly explained in its first incarnation, then you have a duty to explain it better in your work. The responsibility is yours because you have the opportunity to publish a paper on the subject. These demands are broadly accepted and are neither new nor controversial. Any time you exceed the demands, the resulting paper is highly appreciated and valued. Any time you fail the demands, you will get a “revise and resubmit” for a journal submission, or a straight rejection for a conference. Until you fix your exposition, your ideas will not enter the canon. If you post unpolished manuscripts to the arXiv, you are embarrassing yourself. Compare this to OpenAI’s behavior. They do not care about their “preprints”. They don’t bother putting in appropriate attribution of ideas, they salami-slice 372 results over 719 pdfs, their work contains retraction-worthy errors , they promise code but neglect to include it , and their proofs are fiendishly hard to parse. Their pdfs actively set back the state of the literature. This is poor quality work and these pdfs are all easy rejects. We all know that OpenAI is not doing this “for the good of science”. These pdfs are exhaust fumes from their internal benchmarking effort. We do not need them to release this unprocessed exhaust into the atmosphere. OpenAI’s new internal model may have produced these texts with the press of a button. But similar results would have been found soon by experts, with or without LLM prompting. Some results were already found by ChatGPT users and were in the process of being written up properly. Even without alleging theft, it is a bad look for a supplier of scientific software to actively scoop their customers. If an expert prompts a result, they take up the mantle of responsibility I describe above. OpenAI not only refuses to take their responsibility, but they prevent their own customers from doing so. October…

Import AI 476: Humanity’s Sixth Sense benchmark shows AI visual reasoning lags humans

Mon, 12 Oct 2026 12:30:39 +0000

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now New benchmark shows how AI models lack intuitive visual reasoning: …Humanity’s Sixth Sense highlights some shortcomings of current AI systems… AI startups Elorian and Scale AI have built and released Humanity’s Sixth Sense, a benchmark that “measures how a model interprets and reasons through information a visual scene implies but doesn’t explicitly show.” Some examples of the types of task in HSS are a picture of a bookcase and being asked whether it can fit more books, or a photo of horses pulling a plough and a question of whether the chains bear the plough’s drag weight. HSS contains 522 questions that cover 288 images and 234 short video clips (totalling 17.6 hours of video). The questions fall into four categories: Temporal & causal dynamics: “What happened before the scene, and what happens next.” Physical & spatial logic: “What fits, what’s in reach, and what someone else can see from where they are standing.” Social understanding : “Intent, attention, belonging.” Abstract & contextual inference : “The patterns and rules a scene implies.” Results: The human baseline for this is not perfect, but is very high, with people scoring aggregate pass@1 of 93.1, broken down across four categories: Image (94.1), video (91.9), time (97), and space (94.9). By comparison, the best scoring AI system, GPT 6 Astra, gets pass@1 of 53.6, across image (55.6), video (51.1), time (58.2), and space (53.6). Interestingly, Chinese open weight models do poorly here, with GLM-5.3-flash getting a pass@1 of 26.8, and Kimi K3 getting pass@1 of 25.5. “The two most common single mistakes were (1) missing a detail that decides the answer (21% of failures) and (2) misidentifying an object, person, or role (20%),” they write. Why this matters - HSS tells us whether world models are emergent: One way of looking at HSS is highlighting deficiencies in current systems, but a more interesting way is highlighting how adept today’s frontier LLMs are at doing tasks which many people have long assumed would require sophisticated, different AI systems to solve. For instance, many people have claimed that true visual reasoning will rely on developing spatially aware 2D and 3D world models, then using that for reaso…

Generative AI has changed mathematics forever. Where to from here?

Mon, 12 Oct 2026 07:52:41 +0000

By Melissa Lee , Monash University In August, artificial intelligence company OpenAI announced ten advances in mathematics and computer science made with their as-yet unreleased model Astra. The discoveries cover a wide range of mathematical fields, including geometry, cryptography and coding theory. These ten are just the latest in a rapidly growing list of mathematical breakthroughs made by generative artificial intelligence (AI) systems. OpenAI accompanied the announcement with a statement on “responsibility to the mathematical community”. Here, the company acknowledges concerns about attribution, accountability for the correctness of AI-assisted results, and the changing nature of mathematical discovery. The statement reflects a growing understanding that the remarkable technical advances due to AI are only part of a bigger story about what mathematics means, and what its future will look like. AI is raising big questions Large language models (LLMs) such as ChatGPT and Claude are creating disruptions in all parts of the mathematical community. University departments are grappling with the ethical implications of these tools in their research and teaching. Editorial boards of many peer-reviewed journals are being inundated with AI-written papers and must decide how to assess and disclose the use of generative AI. The arXiv preprint repository has seen a sharp increase in mathematical submissions in recent months. Math papers uploaded to @arxiv per month. Jan 1992 to Jul 2026. Source: https://t.co/nrqjhJBtij pic.twitter.com/pmHvtbYK2X — Pablo Groisman (@pgroisma) August 3, 2026 Funding agencies are starting to develop policies governing acceptable use of AI in applications and assessment. Broader philosophical questions about how and why mathematics is done in the first place are hard to avoid. If an LLM can discover a proof, construct an example, or formulate a new question, is there anything uniquely valuable about human mathematical creativity? Is mathematics mostly about producing new theorems, or is it about developing understanding? When a machine contributes to a discovery, who should get the credit? Growing concern – but little consensus Among working mathematicians, there is little consensus about the answers. Some have genuine anxiety about the future of the discipline. One mathematician wrote that the advance of AI-assisted mathematics had tri…

Why AlphaFold Didn't Solve Protein Folding — Pushmeet Kohli, Google DeepMind & Sal Candido, Biohub

Sat, 10 Oct 2026 00:31:26 +0000

From the Bitter Lesson of AI scaling to the unsolved mysteries of protein folding , Google DeepMind’s Pushmeet Kohli and Biohub’s Sal Candido are rethinking what it takes to build AI that truly understands biology. In this special panel moderated by Brandon Anderson , they explore why AlphaFold’s breakthrough was only the beginning , why scaling compute and data alone won’t solve biology, and how the next generation of AI models could transform our understanding of proteins, cells, and human disease. We go deep on the future of AI-driven biology : finding scaling laws in biological data, the tradeoffs between scientific intuition and general-purpose architectures, why protein structure prediction is far from solved, and what it would take to build predictive models of living systems. Pushmeet reflects on the lessons behind AlphaFold , the limits of human interpretability, and whether future frontier models could understand other AI systems better than we can. Sal explains why protein language models may already contain scientific knowledge we haven’t unlocked, how biological modeling must move beyond individual proteins, and why achieving Biohub’s mission to cure all disease requires thinking in terms of 10x breakthroughs rather than incremental improvements. We discuss: The Bitter Lesson for biology : why scaling compute and data isn’t enough Why finding the right scaling law matters more than blindly increasing model size How low-quality metagenomic data can improve protein language models Why AI researchers optimize for available data instead of the most important scientific problems Lessons from DeepMind on balancing modeling, data generation, and scientific expertise Why building a virtual cell requires fundamentally different datasets AlphaFold’s handcrafted architecture and the role of scientific intuition Why good data matters more than simply having more data Inductive biases, scaling laws, and the future of specialized AI architectures Why we aren’t in a post-Transformer world , but architectures are evolving Why AlphaFold didn’t actually solve all of protein folding Protein dynamics, disorder , and the limitations of static structure prediction How cryo-EM micrographs could unlock richer biological representations Moving from models of individual proteins to whole biological systems Feynman’s famous principle and why AI can now create things we d…

Pointing AI at archives found a forgotten meteorite, lost rhinos, and more

Fri, 09 Oct 2026 11:36:20 +0000

How I used AI to investigate millions of historical records and surfaced a forgotten meteorite report, three lost rhinos, and unrecorded volcano eruptions. Last week, on October 1st, 2026, the historian Benjamin Breen published a post called Using Opus 5.5 to discover a new eyewitness account of the dodo. He used AI to search digitized records from the Dutch East India Company. The Company dominated the spice trade and ran a string of ports across Asia from 1602 until it collapsed under its debts and was dissolved in 1799. A project called GLOBALISE has turned millions of the Company’s handwritten pages into searchable text. Searching those transcriptions with AI, Breen found a 1615 ship’s journal in which sailors on Mauritius “caught many tortoises, dodos”. A new eyewitness record of the extinct dodo, sitting in plain sight for four hundred years. I read the article and immediately thought, I can do this. Not because I’m a historian, but because I’m a software engineer who happens to be very adept at piloting AI agents and creating agentic workflows. Breen’s post was the inspiration, and I wanted to extend the approach: I could search for many different historical mysteries in parallel, and I could chain together a few different AI models to filter results and automate a few of the cumbersome, time consuming manual steps. But the appeal went beyond the technical challenge. I think there’s something honorable about adding to what we know. Adding even one new data point to a couple of niche fields felt like a small but worthwhile contribution to make. I thought it would be really cool if I could do that. I powered on my small but capable home AI lab and got to work. The Plan I started by asking an AI “Deep Research” assistant a question: which open historical questions are most likely to be solvable with data that already exists online? It came back with thirteen candidates, ranked by how complete and accessible the data was, how much AI could help, whether anyone had already done it, and, most importantly, whether an answer could be checked against an original page. That last one matters more than it sounds. AI models can be confidently wrong, so every claim had to end at a real document: a scan of the actual handwritten letter or the actual printed newspaper, with an archive reference that anyone can look up and read for themselves. The list included anima…

MaRN: a PyTorch library for training neural networks through low-dimensional parameter mappings

Fri, 09 Oct 2026 08:05:23 +0000

I built MaRN (Mapping Networks) , a PyTorch library that lets you optimize a compact latent representation instead of directly training every model parameter. Some results from my current benchmarks: MNIST CNN: Reduced a 537,748-parameter CNN to 4,080 trainable parameters ( 131.8× reduction ), with accuracy decreasing from 99.07% → 98.10% ( −0.97 pp ). A smaller 107,998-parameter CNN was reduced to 1,872 trainable parameters ( 57.7× reduction ), with accuracy decreasing from 98.83% → 97.18% ( −1.65 pp ). These results come with trade-offs: mapped models can train substantially slower, and performance varies by task. The benchmarks are exploratory, using synthetic data for some tasks, and aren't evidence of general superiority over direct training. The library includes global and layer-wise mappings, regularization options, and pruning/LRD integrations. Code: https://github.com/arjunmnath/MaRN Docs: https://marn.readthedocs.io/ I'd value feedback on the approach, benchmark design, and where this kind of parameter-efficient optimization could be useful. submitted by /u/Less_Dream_6331 [link] [comments]

Ai2 at COLM 2026: Open research, from models to agents for science

Fri, 09 Oct 2026 08:00:00 +0000

Ai2 at COLM 2026: Open research, from models to agents for science October 9, 2026 Ai2 This week Ai2 was at COLM 2026 in San Francisco. The work we shared reflects our wider mission: building AI research artifacts anyone can study and extend, and developing AI for science tools in collaboration with domain experts. Our evening gathering gave colleagues from across the COLM and wider open-source community another setting to connect. Those exchanges are central to how we approach research. As a fully nonprofit lab, we pursue fundamental scientific questions while sharing the models, data, code, and methods that let others investigate them too. COLM brings us together with the people who use our resources we release, challenge our assumptions, and contribute their own ideas. Sharing the work in open collaboration with others isn’t an afterthought for us—it’s a fundamental part of how we approach research. From Olmo Hybrid to the next Olmo Our paper “Olmo Hybrid: From Theory to Practice and Back”, presented at the conference, examines what happens when a language model combines transformer attention with linear recurrent layers. Attention can retrieve specific details from earlier text; recurrent layers maintain a compact state that updates as the model processes a sequence of text tokens. The research connects theory about what these architectures can represent with controlled training experiments. Olmo Hybrid provides evidence that combining attention and recurrence can improve training efficiency: in the study, it reached the same accuracy as Olmo 3 7B on MMLU, a benchmark covering knowledge across many subjects, using 49% fewer training tokens. That research is helping inform the next Olmo model, now in pre-training with a hybrid mixture-of-experts architecture. The hybrid design combines attention and recurrence. Mixture-of-experts offers another way to improve efficiency, routing each token through a subset of the model’s components. We are also opening up the infrastructure behind this direction. Released ahead of COLM, Olmo-core 3 adds a redesigned system for training large mixture-of-experts models. Researchers can use the code to develop their own models and investigate training decisions, alongside the checkpoints and technical reports available through the broader Olmo ecosystem. And Google recently announced that they recreated our Olmo 3 7B traini…

Advancing chemical reaction prediction in data-scarce drug discovery with active and geometric deep learning

Fri, 09 Oct 2026 00:00:00 +0000

Nature Computational Science, Published online: 09 October 2026; doi:10.1038/s43588-026-01063-0 Researchers pair active learning with geometric deep learning to predict the outcome and site selectivity of chemical reactions from scarce data, guiding experiments that accelerate drug discovery and expand accessible chemical space.

Dataset of DInSAR wrapped phase signals for AI-based automated detection and classification of mass movements

Fri, 09 Oct 2026 00:00:00 +0000

Abstract With the growing use of Artificial Intelligence (AI) in remote sensing of mass movements, open and suitable datasets for model training and validation are increasingly needed. Although Differential Synthetic Aperture Radar Interferometry (DInSAR) is a widely used technique for studying mass movements, wrapped interferograms remain less exploited, and the importance of geomorphological expertise in their interpretation is not usually emphasised. In this work, we introduce a dataset of DInSAR wrapped phase signals designed to support the development of Deep Learning (DL) models for the automated detection and classification of active slow-moving mass movements. The dataset covers two selected wide areas in the Central European Alps and the Northern Apennines. It contains 4910 expert-annotated DInSAR wrapped phase signals derived from 92 Sentinel-1 interferograms with temporal baselines ranging from 6 days to 1 year, classified into nine distinct landslide and periglacial landform classes after careful geomorphological interpretation. This dataset is expected to support the scientific community in AI-based applications for mass movement research, while also serving as a benchmark for the generation of comparable datasets.

Photonic circuits become trainable AI hardware

Fri, 09 Oct 2026 00:00:00 +0000

Nature Computational Science, Published online: 09 October 2026; doi:10.1038/s43588-026-01071-0 A physics-based in situ learning method is demonstrated that directly measures optical gradients with photonic integrated circuits, allowing diverse circuit topologies to be trained after fabrication. This hardware achieves matrix design, multiwavelength computation and rapid meta-learning without requiring a full digital model of the circuit.

The missing map of the sky: the first complete ultraviolet view

Thu, 08 Oct 2026 20:59:00 +0000

Astronomers have mapped the entire sky in visible light, infrared, radio, X-rays, and gamma rays. No one, however, had created a complete map of the sky in ultraviolet (UV) light. Here, Brice Ménard, an astrophysicist at Johns Hopkins University and a researcher at Anthropic, explains how he worked with Claude Science to produce the first complete map of the sky in UV light.

Periodic Labs on "Synthesis Superintelligence": Training AI Scientists on Real Experiments

Thu, 08 Oct 2026 16:27:54 +0000

It’s hard to believe that Periodic was only launched last September : One year later, it is considered one of the pre-eminent AI scientist labs, with dizzying talent density and astonishing progress in the autonomous lab buildout: Most people are familiar with the standard credentials of Liam and Dogus , but we found an incredible “talent slope” while learning more about Periodic, where each successive employee seems more impressive than the last: From building AI systems that reason over noisy physical experiments to creating laboratories where every instrument can become intelligent, Periodic Labs is betting that the next frontier of AI won’t come from simply training on more internet data , it will come from letting models experiment with the real world. In this episode, Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk join swyx and Brandon to explain why scientific discovery is fundamentally different from math and coding, and what it takes to build AI scientists that can actually discover new materials. We go deep on Periodic’s vision for “synthesis superintelligence” : reinforcement learning grounded in physical experiments, AI-powered materials characterization, simulations and density functional theory, high-throughput labs, and systems that learn from the entire process of doing science rather than only its published results. Liam and Dogus also explain why frontier models will still need experiments , why failed experiments may be some of the most valuable training data, what it means to give every piece of lab equipment “140 IQ,” and how autonomous experimentation could compress decades of scientific trial-and-error into months. We discuss: Why intelligence alone isn’t enough for scientific discovery How reinforcement learning changes when the environment is the physical world Why science requires reasoning under uncertainty, noise, and missing information Prediction, synthesis, and characterization in the materials discovery loop Why physics and materials science are still far from “solved” The “matter compiler” and Periodic’s goal of synthesis superintelligence Phase transitions, X-ray diffraction, and AI-powered materials characterization DFT, simulations, and why experiments remain the ultimate ground truth Room-temperature superconductors, new magnets, batteries, and more efficient compute Why quantum computing may not automatically solve mate…

Why is Anthropic running a biology lab?

Thu, 08 Oct 2026 07:00:00 +0000

Why is Anthropic running a biology lab? Anthropic is best known for Claude, the artificial intelligence chatbot that competes with OpenAI’s ChatGPT and Google’s Gemini. But the AI company now employs molecular biologists, studies DNA, and operates a laboratory where scientists conduct physical experiments. The San Francisco Bay Area facility is what researchers call a “wet lab”: a laboratory where scientists work directly with biological materials, rather than limiting their research to computer models and simulations. For an AI company, that may sound like an unusual expansion. Anthropic says there is a straightforward reason for it: if Claude is going to help scientists make discoveries about biology, those discoveries eventually have to be tested in the physical world. The company formed its life sciences research group in the spring of 2026 to investigate whether general-purpose AI models could systematically speed up biological discovery. Anthropic says building its own laboratory allowed the same team to work across the entire process, from improving Claude’s biological reasoning to testing its hypotheses experimentally. The project has already produced an early test case. Anthropic said on September 23 that Claude had identified a previously uncharacterised enzyme system associated with unusual repeating DNA sequences. The company says the pattern has some similarities to CRISPR systems, although researchers do not yet know what the newly identified system does. The finding has been released as a preprint and still requires further scientific investigation. Why does an AI company need a physical lab? AI can search scientific literature, analyse enormous datasets, write computer code, and propose explanations for patterns it finds. But biology ultimately depends on what happens inside cells, proteins, DNA, and other physical systems. A model might predict that a particular protein performs a certain function, for example, but scientists still need laboratory experiments to establish whether that prediction is correct. That creates a bottleneck for companies trying to use AI for science: computers can generate hypotheses far more rapidly than researchers can experimentally verify them. Anthropic’s lab is intended partly to close that gap. Its scientists use Claude to search large collections of genetic information for proteins or biological systems tha…

China's XtalPi debuts KodexiaTM as world's first closed-loop siRNA platform

Thu, 08 Oct 2026 06:35:19 +0000

image credit- shutterstock China's XtalPi Holdings, an AI and robotics-driven drug and materials discovery company, has debuted Kodexia™, the industry's first closed-loop small interfering RNA (siRNA) discovery platform combining generative AI, first-principles-driven biological modeling, and automated experimentation. Validating the rapid execution capabilities of this new infrastructure, the company simultaneously announced the advancement of six proprietary therapeutic programs, marking the successful expansion of XtalPi's technology into nucleic acid therapeutics. Kodexia™ also globally co-optimises dual-target siRNAs as single molecules, creating a strategy for disease biology that may not be adequately addressed through single-target intervention. The platform's lead programme in immunoglobulin A (IgA) nephropathy serves as an early empirical validation of this unified architecture. programme is on track for preclinical candidate selection within a nine-month project timeline, significantly accelerating the industry norm of 12 to 18 months. Kodexia™ currently drives six proprietary programmes across metabolic, renal, respiratory, and central nervous system diseases, with more than half having already completed in vivo efficacy evaluations. - China's InnoCare inks $3.5 B deal w...28 September 2026 - Novo Nordisk receives Singapore app...24 September 2026 - Ansell injects $60 M in India to es...21 September 2026 - Avantor expands RIM single-use port...17 September 2026

CircuitATLAS: Agentic reasoning over a systems neuroscience knowledge graph for target discovery in circuitopathies

Thu, 08 Oct 2026 04:00:00 +0000

Drug discovery for neurological disease has traditionally centered on the molecules altered by disease. But the molecules that cause pathology are not necessarily the best points from which to reverse it. Here, we ask which otherwise unaltered molecular control points can be engaged to restore pathological neural circuits toward functional states. We present CircuitATLAS, a provenance-grounded systems-neuroscience knowledge graph and agentic framework for target discovery in circuitopathies. It structures literature-derived relationships across diseases, phenotypes, electrophysiology, circuits, brain regions, cell types and molecular effectors, while deliberately excluding direct disease-gene and disease-protein edges to reduce shortcut reasoning. The graph contains 3.83 million nodes and 7.66 million edges, including 5.31 million LLM-extracted relations, and incorporates structured datasets such as the Human Cell Atlas and new multimodal in vivo measurements. We then introduce an agentic workflow that reasons from measurable disease phenotypes through their circuit and cellular substrates to molecular interventions, therapeutic feasibility and clinical constraints. Finally, we introduce a human-governed in vivo lab-in-the-loop linking hypothesis generation to experimental iteration. Within this framework an agent nominated ATP1A3, the neuronal alpha3 Na+/K+-ATPase, as a control point on cortical excitability; interneuron-restricted expression of ATP1A3 abolished the beta- and gamma-band response to a focal 4-aminopyridine challenge in vivo, and the validated target was then carried into a structure-guided small-molecule campaign terminating in a defined assay to resolve the direction of modulation. CircuitATLAS thus provides a framework for discovering therapeutics based not only on what is molecularly disrupted in disease, but on what can be controlled to restore circuit function.

Equivariant Graph Neural Networks Accelerate Dynamic Polarizability Calculations for Organic Molecules

Thu, 08 Oct 2026 04:00:00 +0000

Predicting the dynamic polarizability tensor of organic molecules is essential for simulating light-matter interactions in optoelectronic devices, yet conventional quantum-chemical methods such as time-dependent density functional theory (TD-DFT) are computationally expensive and limit large-scale screening. We present an equivariant graph neural network architecture that predicts frequency-dependent, complex-valued dynamic polarizability tensors directly from readily available molecular information, such as 3D geometry and UV-vis spectra. The model learns to approximate the shape and magnitude of the dynamic polarizability tensor, capturing both dispersive and absorptive features that define the molecular optical response. We evaluate the model on molecules from both the QM9 dataset and the Harvard organic photovoltaic dataset using various metrics, including the earth mover's distance, which is suitable for comparing spectral quantities. For both datasets, the model successfully reproduces the main features of the dynamic polarizability tensor and generalizes across diverse molecules. To assess the applicability, we further validated the model using a downstream workflow to calculate the optical properties of organic photovoltaic devices. We used the model-predicted polarizabilities to perform device-level optical simulations, yielding charge carrier generation rates in agreement with TD-DFT-based results ($R^2 = 0.94$). Our approach offers a promising complement, or even an alternative, to conventional methods such as TD-DFT, enabling faster and more efficient screening of photoactive organic molecules for optoelectronic applications.

Humanity's Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models

Thu, 08 Oct 2026 04:00:00 +0000

Humans perceive far more in a scene than what is explicitly depicted: a single glance captures past causes and future trajectories; a quick peek determines if a vehicle can fit between two parked cars; a few seconds of video reveals who holds authority in a room; and a fleeting clip highlights subtle abstract patterns like unwritten rules or hidden labels. This capacity reflects a form of humanity's sixth sense: an intuitive reasoning mechanism that recovers implicit information beyond raw sensory perception. Crucially, this rapid, zero-shot visual intuition underpins everyday navigation and social interaction, making it a vital capability for Multimodal Large Language Models (MLLMs) deployed alongside people. Existing visual benchmarks, however, target either deliberate expert-level analysis in academic and mathematical domains or low-level perception, leaving the intuitive reasoning that people perform largely untested. To bridge this gap, we introduce Humanity's Sixth Sense (HSS), a benchmark for intuitive visual reasoning. HSS spans diverse image and video inputs, organizes items under a structured taxonomy, and pairs each with human-written prompts probing the implicit temporal, spatial, social, and abstract structure that people infer at a glance. Frontier MLLMs fall short of human performance: participants reach 93.1% accuracy, while the strongest model, GPT-6-astra, reaches only 53.6% even at maximum reasoning effort. Despite excelling in many complex tasks that require advanced perception and knowledge, current models still struggle significantly on these visual tasks that are intuitive for humans. We further explore agentic setup that apply dynamic visual manipulation to HSS, which narrows but does not close the gap. HSS establishes intuitive visual reasoning as a measurable axis and directs attention to a capability that scaling on current benchmarks has so far left behind.

Deep learning driven framework for optimization of polycrystalline microstructures under competing strength requirements

Thu, 08 Oct 2026 04:00:00 +0000

Computational design of polycrystalline microstructures for enhanced mechanical performance requires repeated high-fidelity simulations over stochastic morphologies, rendering conventional crystal plasticity finite element (CPFE) approaches prohibitively expensive for optimization. Here, we develop a deep learning-driven framework for optimizing polycrystalline microstructures under competing quasi-static and dynamic performance requirements. A 3D U-Net surrogate maps polycrystalline copper microstructures directly to full-field velocity histories from plate-impact simulations, preserving the spatial and temporal resolution needed to evaluate dynamic performance and interrogate underlying wave interactions and failure mechanisms. The surrogate is coupled with stochastic microstructure generation and derivative-free optimization to efficiently navigate the design space. To monitor surrogate reliability during optimization, we introduce a distribution-consistent reliability region (DCRR) based on maximum mean discrepancy (MMD) to quantify distributional shift from the training data. The framework is demonstrated for multiple design problems, including constrained optimization of spall and yield strengths and inverse design. The optimal designs are validated using CPFE simulations with less than 3% prediction error across all optimization cases, while the surrogate-driven optimization reduces cumulative evaluation time by approximately four orders of magnitude compared with direct CPFE-based optimization. Although demonstrated here for the design of polycrystalline microstructures targeting spall and yield strengths, the framework is broadly applicable to optimization problems where high-fidelity full-field simulations are prohibitively expensive but spatially and temporally resolved response information is essential for evaluating, interpreting, and optimizing candidate designs.

Researchers fear being scooped by AI, as OpenAI's announcement timing draws questions

Thu, 08 Oct 2026 00:00:00 +0000

As AI agents’ ability to do science autonomously continues to improve, scientists are facing a once-unthinkable question: will I get scooped by a bot? At least twice in the past five weeks, researchers have said that they had been working for an extended period on a particular research question, only to learn that artificial-intelligence companies had either answered the question before them or announced results before they were ready to do the same. In response, researchers who are worried that AI tools might scrape their unpublished work and scoop them are limiting their use of such tools. Anthropic’s AI biolab finds ‘CRISPR-like’ DNA in viruses. What’s next? Nevertheless, some AI researchers, although not all, say that AI agents are increasingly likely to reach conclusions before scientists can do so. Researchers are “going to be scooped, but not because they’ve uploaded a manuscript”, says Geoffrey Irving, chief scientist at Resolution, an AI-safety research organization in Berkeley, California. “They’re going to get scooped because the AIs are very good at solving problems, and they’re going to get better and better.” Controversial findings On 7 September, Tristan Buckmaster, a mathematician at New York University in New York City, posted online that he and a collaborator had made progress on the Navier–Stokes problem, a long-standing open question in fluid dynamics. According to Buckmaster’s account, he and Levent Alpöge, a mathematician at the AI company Anthropic, based in San Francisco, California, whose work with Buckmaster was a personal project, had been pursuing the problem for a year. Buckmaster’s 7 September post also included a paper with the solution to a simpler version of the problem. The next day, 8 September, OpenAI, also in San Francisco, announced that its agents had solved the puzzle — a timing that Buckmaster has publicly questioned. Who gets credit in the AI era? OpenAI maths bombshell sparks debate Buckmaster raised the possibility that information that he and Alpöge had uploaded to an OpenAI tool could have been used to train the company’s models. OpenAI disputed this scenario. The company said in a statement that an investigation had confirmed that Buckmaster’s “prompts” in the two months before the 8 September announcement “could not have influenced the system in any way, including through training”. The statement also said tha…

A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization

Thu, 08 Oct 2026 00:00:00 +0000

Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations are expensive and limited. Recent large language model (LLM) agents offer a new way to approach BBO by combining task semantics, computation, optimization tools, and feedback-driven decision making, showing great potential due to the integration with mathematically rigorous tools. However, existing agentic BBO studies use different task domains and system configurations, making their results difficult to compare and the effects of individual design choices hard to isolate. We therefore introduce AgenticBBO-Bench, a cross-domain benchmark for agentic BBO spanning synthetic functions, hyperparameter optimization, database tuning, chip design, and molecular design under a unified finite-budget evaluation protocol. In our experiments, agentic BBO achieves higher family-averaged scores than direct LLM-based methods in all five domains and outperforms the best numerical optimizers in four. We further study three factors shaping agent performance: optimization tools, task information and prior knowledge, and the role of the LLM during search. Our results show that additional numerical tools do not consistently improve performance, task semantics are broadly useful while more specific priors are less reliable, and numerical optimizers can effectively absorb gains from search trajectories established by the agent. Finally, we introduce a five-task frontier challenge within AgenticBBO-Bench and evaluate seven LLMs under the Codex agent harness, where GPT-6 Astra and DeepSeek-V4.1-Flash lie on the Pareto frontier of performance and cost among the evaluated models. Our code is available at https://github.com/lamda-bbo/agentic-bbo.

Species Distribution, Antifungal Susceptibility, and Machine Learning-Based Prediction of Optimal Therapies Against Candida Species

Thu, 08 Oct 2026 00:00:00 +0000

Invasive candidiasis has become more common in recent decades, particularly within ICUs. Identifying specific Candida spp., and testing their sensitivity to antifungal drugs is crucial for effective treatment that helps healthcare providers to detect potential treatment challenges early. This study aimed to identify Candida spp. at their spp. level present in blood, assess their susceptibility to antifungal agents, and apply a random forest classifier to predict the best antifungal drug for a given species. The isolates were classified at the species complex level through MALDI-TOF MS. The strains' susceptibility to antifungal agents such as AMB, FLZ, VOZ, ITZ, POZ, CAS, MFN, and ANF was assessed using CLSI guidelines. Out of the 354 Candida spp. isolated from blood, C. utilis was found to be the predominant species, followed by C. tropicalis, and C. albicans. This study demonstrated that MFN and ANF exhibited high potency, with an impressive MIC90 of 0.12 {micro}g/mL against all isolates, surpassing CAS, which had a slightly higher overall MIC90 of 0.25 {micro}g/mL. FLZ exhibited the highest MIC90 value among the azoles at 16 {micro}g/mL, while VOZ, ITZ, and POZ all had MIC90 values of 0.5 {micro}g/mL. AMB MIC90 values were 2 {micro}g/mL against all isolated Candida species. The Random Forest AI/ML model showed perfect overall prediction, with the actual and predicted best antifungal drugs matching for all fungal species. This study is particularly essential because rare Candida spp. becoming more common and exhibiting higher MICs to AMB and FLZ and required suitable remedies for controlling the infection.

Thinking Inertia: LLMs Keep Thinking When Told Not To

Thu, 08 Oct 2026 00:00:00 +0000

Large Language Models (LLMs) increasingly ship with explicit "thinking modes", yet their counterpart, "no-thinking", has received far less attention. We study LLMs' no-thinking behavior along two axes. a. How to measure no-thinking? Prior work typically defines no-thinking through proxies such as a disabled thinking mode or the absence of long traces. These proxies are unreliable: disabled thinking modes may still emit reasoning, while long traces may contain filler rather than genuine inference. We instead normalize each response into a pre-answer trace and final answer, and evaluate it at three levels: (i) Empty-Thinking Rate for strict answer-only compliance; (ii) instruction-aware Question-Pre-answer Relevance for similarity between the question and pre-answer trace; and (iii) LLM-as-judge Explicit Inference Rate for visible explicit inference. Together, these metrics distinguish answer-only output, relevant but non-inferential text, and explicit inference. b. How does no-thinking vary across tasks and models? We evaluate six prompting interventions on six LLMs across Boolean, multiple-choice, and open-ended questions. We find that explicit no-think controls cannot reliably eliminate visible inference. Models instead exhibit "Thinking Inertia": explicit inference persists even under strict controls and becomes more prevalent as the answer space opens. Accuracy remains stable on Boolean and multiple-choice tasks, whereas open-ended tasks reveal a trade-off between answer-only compliance and task accuracy. Rewriting the same questions across answer spaces shows that supplying candidate answers makes answer-only responses easier to produce. These findings establish no-thinking as a non-trivial capability: stopping explicit reasoning cannot be assumed from model settings or instructions alone and deserves systematic evaluation alongside reasoning ability.

OpenAI paper on Kohn-Sham DFT: a Coulomb ground-state density without ensemble representation

Wed, 07 Oct 2026 22:24:17 +0000

A few days ago OpenAI released a paper generated by "an internal frontier model" of Navier-Stokes fame titled A Coulomb ground-state density without Kohn-Sham ensemble representation . The model considers a system with 2 nuclei and 3 electrons. It then spends 40 pages (seemingly) proving that for "sufficiently large" internuclear distance, restricted open-shell ensemble KS-DFT cannot describe the ground-state density of this system, no matter the choice of the effective potential. I tried to understand what "sufficiently large" means. Theorem 2.2 suggests that internuclear distance of 200 bohr is necessary but not sufficient. In theorem 1.1 and section 7, the internuclear distance is explicitly set to 2x10 200 bohr, which is 10 163 orders of magnitude larger than the diameter of the observable universe (???). If there are any people here working on analytical foundations of DFT, how significant is this for your field? Does the paper demonstrate any new useful techniques, e.g., for functional inequalities? Can the lower bound on the internuclear distance in this construction be sharpened so it does not involve intergalactic scales? submitted by /u/decelerated_dragon [link] [comments]

The Mathocalypse

Wed, 07 Oct 2026 19:33:40 +0000

The Mathocalypse Last night my 9-year-old son was taunting my wife, complexity theorist Dana Moshkovitz, as follows: “mommy, I heard you got cooked! I heard that a robot solved the math problem you worked on for your whole career! OOF!” While my son was being a brat, he also wasn’t wrong. Whether you’re thrilled, depressed, angry, or whatever else about it, yesterday was surely one of the biggest days in mathematical history. And yes, among the 372 huge results released yesterday by OpenAI, on the recommendation of its advisory group of Timothy Gowers, Edward Witten, and other distinguished mathematicians, was a proof of Subhash Khot’s Unique Games Conjecture (UGC), a statement that my wife has worked toward proving for the entire time I’ve known her. (The UGC implies that a whole slew of optimization problems really are NP-hard, even if you just want an approximation that’s slightly better than what you get from semidefinite programming relaxation, which is one of our main tools.) Or at least, we’re pretty sure that it’s a proof! There’s a Lean certificate, as there are for some of the other 372 breakthrough results (not all of them). But it also appears that no human has understood just about any of these proofs yet; the race to do so has just started. If you want an on-the-ground sense of what that race is going to be like, here’s some of what Dana texted me last night: It feels like something written by someone who’s on psychedelics. So much unclear and doesn’t make sense. Lots of name dropping of previous work without discussing why it can be used despite impossibility results Basically the paper is so horribly written that it’s impossible to read it without AI help I asked Astra for reasonable completeness and soundness claims of the noise gadget and it gave them by combining claims from all over the paper They also have direct optimal NP hardness of approximation proofs for the main applications of the UGC (Max Cut and all CSP) that bypass the UGC. The UGC proof invents a completely new bizarre code with a noise test. It’s some crazy recursive construction. It’s not the long code, not the short code – some alien craziness I still think that there maybe is a proof that uses the half space code (which is natural) The citations are often irrelevant and confusing A possible future is a math world that’s heavenly if you have vision/creative ideas that AI…

Complementary remarks from Gary Marcus and Terence Tao on OpenAI's giant math drop - Marcus on AI | Substack

Wed, 07 Oct 2026 17:16:44 +0000

OpenAI’s massive new math drop: [This essay was written in extreme haste before a very long wifi-less flight; please forgive typos.] Part One: My take The real news here isn’t the result; it’s not what we were told. 1. AI once tried to be a science. Now we get stuff like the completely vague report from OpenAI below: “Same procedure”? “Using an unreleased model”? This would never pass peer review. We don’t know what the procedure was. We know nothing about the architecture. For esxample, were the proofs generated in one shot, and then verified by the symbolic system Lean? Was there an iterative process?) We know nothing about the failure rate. We know nothing about the training/post training/data augmention. 2. As a result, we have zero idea of how generalizable the result is outside math. 3. A lot of the discussion on social media has been reduced to an ignorant cheering section that applauds without knowing what it is applauding or what it might mean— without ever asking basic scientific questions. The new system could be a legitimate step toward AGI. Or it could just be a clever leveraging of Lean and synthetic data in a verifiable domain with no generality whatsoever. From the initial report, we can tell almost nothing. §

Google DeepMind and Meta Join Biohub's $1.8 Billion AI Biology Push

Wed, 07 Oct 2026 15:11:36 +0000

Alphabet (GOOGL) and Meta Platforms (META) are buying into AI biology. On Wednesday, October 7, Google DeepMind, Alphabet’s drug discovery unit Isomorphic Labs, and Meta agreed to put a combined $300 million into Biohub’s Virtual Biology Initiative. Biohub is the science group backed by Mark Zuckerberg and Priscilla Chan. With the U.S. Department of Energy and the National Institutes of Health also in, total commitments now reach $1.8 billion. What the Money Buys The initiative will measure how cells respond across far more conditions than scientists have ever tested, then use that data to train AI models that predict cell behavior. The first dataset is due in about a year, and the goal is accurate predictive models within five. The funding breaks down like this: - Biohub: $500 million, committed in April - Department of Energy: more than $500 million over five years - NIH: more than $500 million in earlier federal funding, now being standardized for the project - Google DeepMind, Isomorphic Labs, and Meta: $300 million combined In return, the three companies see the data before the public does. Commercial funders get a one-year embargo on the data they help pay for, while government-funded data has none. “We have to have some incentive for commercial players to be a part of this, and the embargo period creates that,” Biohub head of science Alex Rives said. $300 Million Is a Rounding Error for Alphabet and Meta The check is tiny next to what these two already spend. Alphabet spent $68.97 billion on research and development over the last 12 months, and Meta spent $71.63 billion. Together that’s $140.6 billion, or about $385 million a day. So the entire $300 million commitment is less than one day of their combined research budgets. Both companies have more than doubled R&D since 2021, and Meta now outspends Alphabet. Capital spending makes the gap even wider. Alphabet spent $91.45 billion on capex in 2025 and Meta spent $69.69 billion, each roughly 3.7 times its 2021 level. Both companies have tied most of that spending to AI data centers. Biology is a small add-on to that spending. Nothing in this deal moves either company’s earnings. Why Early Access Is the Real Asset What the money really buys is time. A one-year embargo on commercially funded datasets gives participating companies a head start in training models before those datasets become public. That…

OpenAI releases 722 AI-generated mathematics papers as verification questions arise

Wed, 07 Oct 2026 09:42:16 +0000

OpenAI releases 722 AI-generated mathematics papers as researchers weigh what comes next OpenAI has published hundreds of mathematical manuscripts produced by an unreleased AI model, opening a much bigger debate over how AI-generated research should be checked, understood and incorporated into mathematics OpenAI has released a collection of 722 mathematical manuscripts generated by an internal frontier AI model, covering hundreds of research problems across mathematics and theoretical computer science. The collection is organized into 372 groups of related results and includes proofs, alternative arguments, consequences and supporting material. Many of the results also include formal proofs written in Lean, a programming language that allows mathematical arguments to be checked computationally. OpenAI says the work emerged from testing its internal models on open research problems after performance on its existing mathematics evaluations began to plateau. Around 4,000 problems were presented to the model during the evaluation process. OpenAI says the average result used computing resources equivalent to roughly three hours of ChatGPT Pro thinking. The scale of the release makes it considerably different from a typical AI benchmark. Rather than measuring whether a model can answer a fixed set of questions, OpenAI is publishing work that is intended to enter the mathematical research process itself. But the company is also explicit that publication does not mean every result has already been fully verified. The repository contains work at different stages of checking, and OpenAI says some manuscripts that have not yet been formalized in Lean could contain errors. Corrections and revisions will be recorded while earlier versions remain available. From solving problems to publishing research OpenAI first outlined its approach to releasing AI-generated mathematical results in September, when it said it was working with mathematicians on how such work should be presented responsibly. The company said it wanted to improve transparency around AI-generated mathematics by publishing supporting information alongside the results, including formal proofs where available, reasoning summaries and details about the computational resources used. Ten abridged summaries of the model’s reasoning have also been published for results spanning areas including number theory, compl…

Machine learning for clinical time-series forecasting: an interview with Mayra Elwes

Wed, 07 Oct 2026 07:43:27 +0000

In a series of interviews, we’re meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) . This time, we hear from Mayra Elwes about her work developing machine learning methods for clinical time-series data. Tell us a bit about your PhD – where are you studying, and what is the topic of your research? I am a second-year computer science PhD student at the Institute for Biomedical Informatics at the University Hospital Cologne. My research focuses on the generalisation and domain adaptation of machine learning for clinical time-series forecasting, especially for clinic-to-clinic generalization scenarios. I am working on practical strategies to evaluate methods in a more contextualised way and on improving existing domain adaptation methods specific to time-series forecasting. Could you give us an overview of the research you’ve carried out during your PhD? One major challenge in medical AI is performance degradation when models are deployed in clinics not represented in their training data. This performance degradation often stems from differences in patient demographics, clinical practice, or measuring devices, and it manifests as data distribution shifts. I address this problem for a use-case from the intensive care unit: forecasting blood oxygenation levels and arterial mean blood pressure based on observed vital parameters to improve hypotension and hypoxemia prediction. Since this application task is not covered by standard AI benchmarks, my clinical collaborators and I designed a new benchmark. Currently, we are collecting data at University Hospital Cologne. Tools and datasets I built to benchmark generalization for this vital parameter forecasting task are available as open-source software： WavePrep , Algorithm2Domain , MIMIC-III Waveform Database Matched Subset . While building this benchmark, I developed a strategy to benchmark generalisation and domain adaptation approaches for application tasks and domains in a more contextualised way. By reporting the shift relevant to the downstream task alongside the performance degradation, results become comparable even on private datasets and between different application tasks. Is there an aspect of your research that has been pa…

Predicting regional gray swans via translocation: AI weather models and Dubai’s unprecedented 2024 rainfall

Wed, 07 Oct 2026 07:00:00 +0000

Artificial intelligence (AI) models have transformed weather forecasting, but their skill for unprecedented weather extremes is unclear. Here, we analyze GraphCast, AIFS, and FuXi forecasts of the unprecedented 2024 Dubai storm, which had twice the training set’s highest rainfall in that region. GraphCast and AIFS accurately forecast this event up to 8 days ahead. FuXi forecasts the event but underestimates the rainfall. Fine-tuning and receptive field analyses suggest that these models’ success stems from “translocation”: learning from comparable/stronger dynamically similar events in other regions during training. Evidence of “extrapolation” (learning from weaker events) is not found. Even events within the global distribution’s tail are poorly forecasted, which is not only due to data imbalance (generalization error) but also spectral bias (optimization error). These findings demonstrate the potential of AI models to forecast “regional” gray swans and the opportunity to improve them through understanding the mechanisms behind their successes/limitations.

Vibe Building: Physics-Engine-in-the-Loop Adjudication for Code-Compliant Structural Design

Wed, 07 Oct 2026 04:00:00 +0000

Automated building design must comply with seismic and wind codes and satisfy structural mechanics constraints, yet most existing agents produce visually plausible models without verification grounded in mechanical analysis and code compliance. We introduce the Vibe Building task and propose PE-Loop (Physics-Engine-in-the-Loop), an agent in which a deterministic physics engine is the sole source of evaluation signals, mapping code constraints to a physics process reward, while the language model is confined to proposing discrete revisions (section menu, topology, and lateral system). Designs are verified by held-out seismic and wind time-history checks and a constructability gate. On VB-Bench, 3,577 physics-adjudicated building instances across six code families, PE-Loop achieves the highest verified success rate under three of four backbone LLMs, the highest held-out seismic pass rate under all four, and the highest held-out wind pass rate under three. Replacing the physics verdict with a language-model judge, all else fixed, leaves 58.43% of accepted designs noncompliant. These results suggest that reliable structural design rests less on a stronger LLM proposer than on an adjudicator the proposer cannot influence, a division of labor for agents whose outputs must hold up in the physical world.

OpenAI Releases 722 Math Manuscripts in One Night, Spanning Riemann, Hodge and BSD

Wed, 07 Oct 2026 01:05:13 +0000

OpenAI has released 722 mathematics manuscripts produced by a single unreleased internal model, grouped into 372 results across 17 areas. According to the company, the model tried roughly 4,000 research problems, with each result consuming compute equal to about three hours of ChatGPT Pro thinking. Papers, source code and some Lean proofs were posted to GitHub, along with 10 model reasoning summaries. Headline claims include: no zeros for the zeta function and all Dirichlet L-functions where the real part exceeds 7/8, with a separate independent proof covering real parts above 11/12 that also rules out Siegel zeros; no general algorithm for the rational version of Hilbert's tenth problem; a complete formula for BSD-type elliptic curves under conditions such as Selmer rank 0 or 1; Hodge conjecture manuscripts restricted to complex-multiplication abelian varieties and products of K3 surfaces; an irrationality measure of exactly 2 for pi; irrationality of Catalan's constant; and a claimed proof of the Unique Games Conjecture. The repository states that verification status varies, some results lack Lean formalization, and two items did not follow the standard pipeline. An independent advisory group of nine scholars, including three Fields medalists, said its earlier advice does not constitute endorsement of the results or the process.

UniSkill: Learning Actor-Aligned Skill Proposals for an Evolving Policy

Wed, 07 Oct 2026 00:00:00 +0000

Large language model agents can improve across tasks by retaining reusable skills distilled from prior interactions. Recent work jointly optimizes task execution and skill extraction, enabling the policy and skillbank to co-evolve. However, as the actor continues learning, rewarding skill proposals through their reuse in subsequent training steps may conflate skill benefits with actor improvement, while directly testing each proposed skill requires costly additional actor rollouts. In this paper, we introduce UniSkill, which uses a shared policy to interact with the environment and propose skillbank edits (Add, Update, or No Edit) from the resulting trajectories. Specifically, the actor learns from environment rewards, while contrastive action feedback guides skill proposal learning. This feedback provides an actor-alignment signal by measuring how replacing the retrieved skill with a proposed skill changes the current actor's action log-likelihood gap between previously collected successful and failed trajectories from the same task, thereby avoiding new rollouts for each proposal. Since proposal-level feedback may suppress an otherwise appropriate edit operation when the proposed skill content scores poorly, we further apply skill-edit support regularization to preserve exploration. Empirically, UniSkill achieves strong performance, reaching 98.4% success on ALFWorld and 84.7% on WebShop while maintaining stable joint training. Further ALFWorld experiments show that UniSkill remains effective when the shared policy uses a smaller backbone. Our implementation is available at https://github.com/LimOkii/UniSKill.

Evaluating the Transfer of Co-Evolved Communication from 2D to 3D Simulation

Wed, 07 Oct 2026 00:00:00 +0000

This work examines the transfer of a co-evolved communication mechanism between two robotic agents from a discrete two-dimensional (2D) simulator to a three-dimensional simulator with real physics (3D). The study focuses on whether a communication mechanism co-evolved in a 2D environment retains its functional role after transfer to a 3D physics-based simulator. To support this analysis, the effects of the episode time budget, the social cue, and the asymmetry between the two co-evolved roles were examined. The results indicate that the success rate increased approximately linearly with the evaluated time budgets, with no evidence of a plateau between 2,000 and 6,000 physics steps, suggesting that evaluations based on shorter episodes may underestimate the performance of the trained controllers. In both simulators, the social cue functioned primarily as a jam- assistance mechanism rather than as a navigation guide, although with a more pronounced effect in 2D. Analysis of eight independent evolutionary runs revealed a consistent direction of asymmetry, although its magnitude varied across runs. Controlling the processing order between agents allowed us to rule out an artifact of the physics engine. Finally, the results are discussed in terms of the factors that may contribute to the remaining performance gap observed after transfer.

System Switch: When Should a Fast Decision Model Stop and Think?

Wed, 07 Oct 2026 00:00:00 +0000

Dual-process agents pair a fast policy with a slow deliberative model. In real-time settings the slow model usually runs continuously; in turn-based agents and robot planners it is invoked on events such as uncertainty or a detected failure. We study a fast learned actor that takes every decision and hands control to a reasoning vision-language model only when a gate opens, while the game keeps running. We use closed-loop Doom and the new open "System One" typed-decision models, served through a common llama.cpp interface. On 900 held-out questions, (i) zero-shot decision models from 0.15B to 9B parameters choose to collect items 1.6-1.8 times more often than chance among their errors, in any option order, although the order changes some models' accuracy; (ii) accuracy, calibration and sensitivity (how well confidence separates right from wrong answers) are distinct: models of similar accuracy differ widely in AUROC, and the confidence of the most sensitive one tracks which kinds of situation it fails, not which answers are wrong; (iii) offline, deferring the least confident 30% of decisions to a reasoning model gains over random deferral in proportion to the actor's AUROC (rank correlation 0.87); with actor and rate chosen on held-out games the gain is +0.13 [0.08, 0.18] with doomLaya's option order and +0.08 [0.02, 0.14] with shuffled options, and reasoning carries about half of it; (iv) in closed loop (33 games, three seeds) no variant reaches the exit. Committing to plans, the reasoner's or a fixed explore rule's, opens more doors and makes an actor that stands still play; with the rule the agent dies more often. Told that some doors need keys, the reasoner takes ordinary doors for locked ones, which the state cannot tell apart; without that knowledge it goes back to collecting. We release code, prompts, data and logs.

Composing What Each Teacher Learned: Multi-Teacher On-Policy Distillation through Teacher-Relative Shifts

Wed, 07 Oct 2026 00:00:00 +0000

Multi-teacher on-policy distillation (MOPD) is used in two settings. In common-domain composition, several teachers score each student rollout from one prompt domain and their signals form a single target; in routed-domain distillation, prompts from different domains are assigned to the corresponding specialist. Both settings usually transfer each teacher's endpoint policy, which mixes what post-training changed with preferences inherited from the teacher's base. We introduce Δ-MOPD, which transfers each teacher's teacher-minus-base logit shift re-anchored at the student's frozen initialization, and compare it with endpoint supervision in both settings while holding teacher selection fixed. We first expose the mechanism that impedes endpoint transfer: inherited base pull can exceed the post-training shift. Removing it reduces the teacher-term norm ratio and target--student KL. Across our experiments, the results suggest that shift targets are particularly useful when teacher signals are combined at a state. With three composed teachers, Δ-MOPD exceeds endpoint composition by 4.11 Math and 1.95 five-benchmark points; with two, it matches endpoint accuracy. Under phased routing, it achieves higher mean performance in both phase orders and reduces the observed order gap from 10.50 to 6.42 points. Under interleaved routing, where each update involves one teacher, the two targets perform comparably. The phased results provide supporting evidence that the benefit may extend to signals accumulated across training phases. Target construction is thus an independent design axis in MOPD, complementary to teacher selection.

RoboJEPA: Scaling Robotic Latent World Models

Wed, 07 Oct 2026 00:00:00 +0000

Latent world models have shown a remarkable ability to predict future states and to plan in the real world. In practice, however, we lack a principled way to estimate how their capabilities scale with model size, data, and compute, an open problem that slows progress in the field. In this work we present RoboJEPA, a world model based on the Joint Embedding Predictive Architecture (JEPA) and trained on a large-scale dataset spanning 12 robotic embodiments. We show that RoboJEPA's imagination error, the error of its latent rollouts, follows a second-order power law in compute, allowing us to predict model quality well beyond the scale at which the law is fit. We further show that downstream robotic planning performance improves predictably with compute, and that imagination error is strongly correlated with it, making it a reliable proxy for real-robot evaluation. Finally, we demonstrate that latent world models can be deployed zero-shot as robotic agents, planning toward a single goal image to solve tasks requiring long-horizon planning on real hardware. We release all model checkpoints together with our training and robot deployment code. To our knowledge, this is the first work to establish scaling laws for multi-embodiment robotic world models trained on real robot data, and RoboJEPA, at 8B parameters, is the largest JEPA predictor model trained to date.

Q-Learning with Scalar Adjoint Matching

Wed, 07 Oct 2026 00:00:00 +0000

Flow policies capture rich and diverse action distributions, and fine-tuning them with off-policy RL to improve beyond the demonstrations has drawn growing interest. However, fine-tuning a flow policy against a learned value function is not trivial, because the policy generates its action over many flow steps. Adjoint matching offers a principled way to update the flow model itself by propagating value information from the final action back to each flow step, but it requires a vector--Jacobian product through the policy at every step, a cost that grows with the number of flow steps and the policy size. We observe that the batch-averaged velocity Jacobian of pretrained flow policies concentrates on its diagonal. Motivated by this finding, we derive a closed-form scalar adjoint that scales the value gradient at the final action by the flow time, eliminating the per-step vector--Jacobian products. We further find that controlling the critic's value at policy-generated actions is particularly important under the scalar adjoint. Based on these findings, we propose Q-learning with Scalar Adjoint Matching (SQAM), which combines the scalar adjoint with a value penalty at those actions. SQAM's gains concentrate on the four hardest OGBench domains, where its success rate exceeds that of the strongest baseline in each domain by 18 to 35 percentage points. To test whether SQAM extends to large pretrained policies, we also fine-tune a vision-language-action policy on a real bimanual robot. SQAM improves over supervised fine-tuning on all three tasks.

OpenAI Releases 722 Math Manuscripts From an Unreleased AI Model

Tue, 06 Oct 2026 22:39:01 +0000

AI Models & Platforms OpenAI Releases 722 Math Manuscripts From an Unreleased AI Model Add Unite.AI to your preferred sources on Google In a research post published October 6, 2026, OpenAI said it is releasing a broad range of new mathematical results produced by an unreleased internal frontier model, publishing 722 manuscripts organized into 372 families in a public GitHub repository alongside Lean proof formalizations and abridged summaries of the model’s reasoning. Inside the GitHub Repository The results are published in the openai/math repository, released under an Apache-2.0 license. Its README describes the contents as mathematical manuscripts and supporting proof artifacts produced by an internal OpenAI model, assembled as part of model-development evaluations on open research problems. The current catalogue contains 722 manuscripts organized into 372 families. A family groups related papers, which may include a principal result, companion arguments, consequences, or alternative proofs, and each family is classified by mathematical discipline. A preprints directory holds PDFs, source files, and manuscript-specific citation and build instructions, while a Lean library and formalization catalogue describe the available formal proofs, their associated papers, and verification configurations. The collection spans different stages of verification. Many manuscripts have accompanying formalizations in Lean, a programming language that allows mathematical proofs to be checked by a computer, but not all do. The README cautions that some of the unformalized results could have issues, and OpenAI says it will endeavor to fix any such issues quickly while updating the repository with more formalizations as they are obtained. OpenAI also says it will preserve the collection’s public release history, recording corrections and revisions as new versions while keeping previously released versions accessible, and each manuscript directory carries a BibTeX block for citations. How the Results Were Produced According to the README, the vast majority of results were obtained with the same procedure using an unreleased internal OpenAI model. Over the course of the evaluation, the model was posed approximately 4,000 problems, and OpenAI reports that the average result used the equivalent compute of roughly three hours of ChatGPT Pro thinking. The README says OpenAI expande…

OpenAI Releases 372 More Math Results as Skeptical Mathematicians Ask for Proof

Tue, 06 Oct 2026 22:19:00 +0000

The floodgates are open. Right on the heels of OpenAI’s new large language model producing the biggest math breakthrough in two decades, the company just released no less than 372 results from the same model. Each resolves or makes substantial progress on a major open question in mathematics or theoretical computer science, the company says. OpenAI revealed the results in a GitHub repository at 6 P.M. EDT. The deluge will take mathematicians months to parse through and understand—including to determine whether the proofs contain novel and important ideas or are mostly mash-ups of existing techniques. But many of the results have already been verified in Lean—a programming language that validates a proof’s logic—and are therefore all but certain to be correct. Among the results claimed are a solution to the four-dimensional Kakeya conjecture, improvements on some of the world’s most important computer algorithms, and actual progress toward math’s scariest problem, the Riemann hypothesis. On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. A spokesperson for OpenAI told Scientific American that the new model—which the company has not released to the public—produced almost every one of the results in response to a single prompt handed to a single AI agent. This would be a striking difference from OpenAI’s earlier blockbuster solution to the Navier-Stokes problem, which was produced through the collective efforts of a 10,000-strong agentic swarm that cost millions of dollars in computing power. If true, it would mean that unprecedented mathematical power could soon be accessible to anyone. But OpenAI’s reputation, among mathematicians, for bold claims and little transparency is causing considerable skepticism. “Until and unless they release the model and people can replicate their results, I think you should treat any claims about one-shotting problems with a single agent as unverified,” says Andrew Sutherland, a mathematician at the Massachusetts Institute of Technology. “We should ask for receipts.” (The OpenAI spokesperson also told Scientific American that some results might have taken multiple attempts.) OpenAI’s handling of the Navier-S…

Hexagon

Tue, 06 Oct 2026 21:50:11 +0000

[This is a guest post by Ben Antieau , cross-posted from his blog . — T.] There was no personal problem, no world problem, whose eloquent solution did not exist— somewhere in some hexagon. —Jorge Luis Borges, “The Library of Babel” [1] New repository I am writing to announce Hexagon , a new repository for research works in the mathematical sciences and theoretical computer science. This has been a joint effort of many people over the last couple of months, starting in early August 2026. We felt that the proliferation of results proved with LLM assistance required a new model for collecting the associated papers. Besides wanting these works to be citable and reliably stored for long periods of time, we wanted to allow lower barriers for authorship and for community interest than the arXiv. We also wanted for these works to have a home besides random GitHub repositories or X/Bluesky/Mathstodon/LinkedIn posts. In order to support the development of this project, we founded the Hexagon Mathematics Foundation in the state of Delaware and we intend to apply for recognition of tax-exempt status under section 501(c)(3). The Board of Directors consists of Mohammed Abouzaid, François Charles, Bryna Kra, David Savitt, and Lauren Williams. I serve as the first Executive Director. And, we have a wonderful Advisory Board, where Kevin Buzzard, Akhil Mathew, Johannes Schmitt, Steinn Sigurðsson, Nikhil Srivastava, Ravi Vakil, and Rachel Ward serve. For more details, see our who we are page. My personal interest When working with a group of people brought together by an FRG grant, we clicked a button and discovered a new result, namely derived invariance of Hodge numbers of fourfolds in characteristic zero. In the past, this would be a result I would have submitted to an excellent, but not top, journal. In this case, there is no way I would put my name to the result. Instead, I blogged about it here . This is a brittle way to aggregate mathematics. Now, it is on Hexagon as hexagon:2609.00104 . The creation of new results happens naturally in working with LLMs. They bubble up, even as side results. My personal hope is that it becomes the community standard to identify these and submit them to Hexagon. The Lifecycle of Hexagon Objects (The title alludes to Ted Chiang’s “The Lifecycle of Software Objects” [2].) My ambition is that Hexagon is where mathematical ideas emerge from…

Microsoft Research Podcast: What AI gets wrong and what failure teaches us

Tue, 06 Oct 2026 16:19:06 +0000

Jennifer Neville is a partner research manager at Microsoft who’s built a career around understanding and advancing AI for real-world use, and much like the human-AI interactions she’s been studying, her early-career path was multiturn: math, then physics; cognitive science, then work; and finally computer science—despite her best efforts to avoid the field. In this conversation with Principal Applied Scientist Chad Atalla , she explores the role evaluation plays in pushing the performance boundaries of today’s AI systems to meet user needs and the “surprising failures” that emerge when models are tested beyond traditional benchmarks. Neville also shares practical guidance for working with current AI systems and discusses why looking closely at data matters when results defy expectations, and what decades of AI progress have taught her about predicting what comes next. From an unexpected career trajectory to the frontier of AI interaction and learning, this episode asks a larger question: what can we learn when the path—whether human or artificial—doesn’t unfold the way we expect? Subscribe to the Microsoft Research Podcast : Apple Podcasts Email Android Spotify RSS Feed Transcript [MUSIC] JENNIFER NEVILLE: There were times that I felt like I was spinning my wheels. Things weren’t working. I would get, kind of, dejected. And then we’d get to a point where we did learn something, and it was just the, the emotional thrill of that was, that was really what hooked me. The being able to, kind of, understand something that no one else understood yet … CHAD ATALLA: Sure … NEVILLE: … because it’s just at the frontier of what we know about things. STANDARD INTRODUCTION: This is the Microsoft Research Podcast , where Microsoft researchers—driving advancement through fundamental science and technology research—explore the who, how, and what’s next in computing and AI. [MUSIC ENDS] CHAD ATALLA: Hello, and welcome. I’m Chad Atalla, an applied scientist here at Microsoft Research. Today, I’m joined by Jennifer Neville, a partner research manager at Microsoft Research and the Samuel D. Conte chair professor of computer science and statistics at Purdue. Her research examines machine learning and AI for interactive domains and structured data, looking at how the data points that AI systems are trained on affect their behaviors and how that aligns with what users actually wa…

Erdős problems site founder Thomas Bloom on AI's gains and concerns

Tue, 06 Oct 2026 14:37:30 +0000

[This is a guest post by Thomas Bloom , crossposted from the Erdős problems forum . This blog post was initially written in a different file format and converted using AI. — T.] AI is changing everything, for better and/or worse, and the rate of change is dizzying; in recent months this has been particularly evident in mathematics, where AI has gone from being essentially useless to helping solve some of the hardest problems in mathematics in less than a year. The website http://www.erdosproblems.com has often been on the front line of these changes, and a barometer by which one could measure AI capabilities. This was not at all my intention when I created the site — I wanted to promote these problems to a human audience, and make a useful reference on what work has been done. But the easy availability of a large pool of questions that are simple to state, ranging from easy and obscure to deep and impenetrable, provided the ideal showcase for AI. In some ways this has led to a huge amount of progress — we now know the answer to many questions we did not before. (Although a lot of this recent progress has come from increased human efforts and renewed interest in some problems, rather than just AI solutions.) There have also been negative effects, however: some mathematicians have dismissed Erdős-style mathematics as ‘easy/recreational’; many have stopped thinking about Erdős problems believing that they cannot compete with AI; and, most significantly, there has been a wave of AI-produced solutions provided with no explanation. While of course these are useful and tell us new things, they are also displacing and discouraging those who are actually interested in the mathematics. (These issues have stopped being Erdős-specific as AI continues to make breakthroughs in an ever wider range of fields.) A couple of weeks ago I asked for feedback on how people used the site, and what they thought should be changed. I received, both publicly and privately, a wide variety of opinions, and I’m grateful to everyone for their feedback. What was particularly striking was the number of people I heard from who have benefited a lot from the site, learning new mathematics and finding new problems to think about, but have never commented on the site. I have been particularly conscious of this large silent, audience, and have tried to make sure their experiences were not drowned…

insitro-Designed Machine Learning Models for Small Molecule in vivo Pharmacokinetic Behavior Prediction Now Available in Lilly TuneLab

Tue, 06 Oct 2026 11:00:00 +0000

SOUTH SAN FRANCISCO, Calif., October 06, 2026--insitro, the AI company unlocking causal human biology to systematically generate high-impact therapeutics, today announced that the advanced machine learning models that insitro built with Eli Lilly and Company (Lilly) to predict in vitro and, critically, in vivo properties of small molecules can now be used by certain biotech companies in Lilly TuneLab™. Trained on multi-species preclinical data that Lilly generated over decades of research from h

insitro's Machine Learning Models for Small Molecule in vivo Pharmacokinetic Prediction Now Available in Lilly TuneLab

Tue, 06 Oct 2026 11:00:00 +0000

Share SOUTH SAN FRANCISCO, Calif. – October 6, 2026 – insitro, the AI company unlocking causal human biology to systematically generate high-impact therapeutics, today announced that the advanced machine learning models that insitro built with Eli Lilly and Company (Lilly) to predict in vitro and, critically, in vivo properties of small molecules can now be used by certain biotech companies in Lilly TuneLab™. Trained on multi-species preclinical data that Lilly generated over decades of research from hundreds of thousands of unique molecules, the models offer a powerful alternative to today’s industry standard method of extrapolation from in vitro assays, providing an enterprise-grade capability traditionally available only to organizations with the scale to generate such data volumes themselves. The models will be updated as the dataset continues to expand. Announced in September 2025, the collaboration paired insitro’s computational and AI/ML expertise with Lilly’s proprietary preclinical dataset of in vitro and in vivo measurements from a wide array of compounds with established ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, derived from decades of Lilly drug discovery, and representing a world-class dataset in quality, consistency and scale. This collaboration demonstrates the power of insitro’s platform by combining insitro’s AI and ML expertise with Lilly’s data to unlock and advance small molecule development. “Nine in 10 programs that enter the clinic fail, most often when we first test for efficacy. That happens when the mechanism did not drive the disease, but also when the molecule never reaches the tissue at the right concentration for the right duration,” said Daphne Koller, Ph.D., founder and CEO of insitro. “The question of a molecule’s in vivo behavior has been an empirical question, answered experimentally, slowly, one compound at a time. These models make it a computational question first.” The downstream implications extend to patients. Most small molecule candidates fail, often for pharmacokinetic or safety reasons that surface late, after significant time and capital have been committed. Better prediction at the design stage may mean that fewer candidates advance on flawed structures, promising molecules reach the clinic on shorter timelines, and program development becomes more tractable. Improved in…

insitro AI meets Lilly data: a new ML model for in vivo PK prediction

Tue, 06 Oct 2026 10:59:44 +0000

Share Last year, insitro and Eli Lilly and Company (Lilly) teamed up to build AI models that predict in vivo properties of small molecules, powered by the corpus of proprietary data that Lilly has accumulated over decades of R&D. Together, we built the in vivo PK model available in Lilly TuneLab as of October 6, to predict in vivo pharmacokinetics (PK) across four preclinical species: mouse, rat, dog, and monkey. Designing a clinic-ready small molecule drug requires optimizing the pharmacokinetic profile of that molecule—ensuring the drug reaches the right tissues at appropriate concentrations for the required length of time to have a therapeutic effect. Existing ML models that predict the PK profile of small molecules have focused on predicting in vitro ADME values due to limited in vivo PK data availability [1]. Lilly’s corpus of historical in vitro ADME and in vivo PK data lets us go one step further: training models to directly predict the in vivo PK behavior of a molecule. Our models are designed to outperform today’s industry standard method based on extrapolation from in vitro assays, potentially enabling prioritization of compounds entering expensive and lengthy animal studies. The gap between a dish and an animal Medicinal chemists designing a safe and effective small molecule have to cross a critical translation gap between what happens in a dish (in vitro) and what happens in the body (in vivo). The first critical checkpoint on that path is a preclinical PK study. Animal models have translation challenges, but they are where a candidate molecule has to survive the complexity of an intact physiological system capable of performing all of the aspects of a drug’s fate in the body. A single IV dose study in animals produces three PK data points that are important signals about whether the candidate molecule has the potential to be a drug: - Plasma clearance (CL): How efficiently the body breaks the molecule down and/or excretes it and its metabolites. - Volume of distribution at steady state (Vdss): How widely the molecule spreads into tissues from the bloodstream. - Mean residence time (MRT): The average time a molecule remains intact in the body before it’s eliminated. Getting this data is slow, expensive, and requires animal testing. Anything that predicts these outcomes reliably ahead of time means running fewer studies on molecules that fail, re…

Terence Tao Calls for Slowing AI in Mathematics, Warning of 'Proof Indigestion'

Tue, 06 Oct 2026 07:59:17 +0000

Terence Tao used a SAIR talk to criticize AI model companies for accelerating mathematics without knowing what comes next, saying, "We must slow down." He described a normal proof lifecycle—proposing a solution, mechanical verification, exposition, peer review, and textbook incorporation—and said AI is racing through generation and Lean formalization while the later stages stall, creating "proof indigestion": proofs no human understands or publishes. He warned of long-term risks including loss of mathematical creativity and data contamination, comparing repeated AI-generated proofs to the cumulative defects of cloned mice. Previously, he called AI in math and theoretical physics "ready for primetime" and said mathematics was undergoing a Copernican revolution. The source also reports that Cédric Villani was stunned by OpenAI having solved a Millennium Prize problem and that 25 Fields medalists signed a joint open letter criticizing OpenAI and other model companies.

Accurate and well-powered case–control analysis of spatial molecular data

Tue, 06 Oct 2026 00:00:00 +0000

As spatial molecular data grow in scope, there is a pressing need to identify disease-associated spatial structures. Current approaches typically make restrictive assumptions such as representing tissue regions by abundances of discrete cell types and samples by abundances of discrete niches; this risks overlooking important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method combining deep learning with principled statistics to discover disease-associated spatial features with greater flexibility and precision. VIMA trains an ensemble of variational autoencoders to summarize the contents of every small tissue patch in a dataset via numeric ‘fingerprints’. It uses these to define many data-dependent, overlapping ‘microniches’ and meta-analyzes them to identify microniches whose abundance correlates significantly with case−control status. We confirm VIMA’s calibration, power and spatial accuracy in simulations. We then apply VIMA to spatial datasets spanning three diseases and spatial modalities, recapitulating known biology and identifying new spatial features of disease. VIMA uses deep-learning architecture to identify differentially enriched features within spatial datasets.

Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI

Tue, 06 Oct 2026 00:00:00 +0000

Large language models (LLMs) and vision-language models (VLMs) are usually frozen after deployment, so they do not learn from the cases they solve. This is especially concerning in medicine, where new clinical evidence, updated guidelines, and new therapies can change established practice. Fine-tuning can update the model, but it requires access to model weights and additional training. Parameter-free methods avoid training, but they may overfit a fixed validation set, lack reliable domain knowledge, or lose visual details by saving experience only as text. To address these limitations, we present a model-agnostic framework that allows frozen LLMs and VLMs to learn from deployment experience through three forms of external expertise: a Skill that guides reasoning and tool use, a Knowledge Memory that stores reliable facts supported by earlier cases or trusted external evidence, and a Multimodal Knowledge Base that keeps visual examples and guides the model to relate each retrieved case to the current image. Instead of relying on a fixed validation set, a validation strategy keeps an update only if it helps on new cases without degrading performance on earlier ones. Across six benchmarks covering clinical diagnosis, clinical workflows, medical reasoning, and medical and non-medical visual reasoning, and with four open-weight and closed-source base models, our framework improves performance during online deployment by up to 34.2% over the base model on medical tasks, generalizes to unseen cases, transfers to other models without further optimization, and works in non-medical domains.

Incidental information contaminates patient notes and disrupts clinical reasoning in large language models

Tue, 06 Oct 2026 00:00:00 +0000

Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failure mode shared between these two applications by assessing their sensitivity to information incidental to the patient encounter. In 576 patient-clinician dialogues, we found that frontier models inserted small-talk exchanges into 35% of notes, while mean quality scores changed by at most 0.20 points on five-point scales. In 3.7% of frontier notes, models misattributed the asides or used them clinically. In 57 mock recorded consultations, background speech from a separate patient encounter at -10 dB leaked into 48.2% of transcripts, with contamination detected in 5.3% of downstream notes generated by four open-weight models. We propose a dual encoding hypothesis of clinical reasoning and distraction in LLMs, with preliminary evidence that LLM components associated with disruption by incidental information also support clinical reasoning. These findings support evaluating resistance to incidental information before clinical use, with safeguards that prevent contamination while preserving clinical reasoning.

VIMA discovers disease-associated tissue structures without cell-type annotation

Tue, 06 Oct 2026 00:00:00 +0000

Nature Methods, Published online: 06 October 2026; doi:10.1038/s41592-026-03242-3 Identification of disease-associated patterns in spatial molecular data is challenging. We introduce variational inference-based microniche analysis (VIMA), a deep learning-based statistical method that can identify such patterns without requiring annotation of the data into cell types or niches. VIMA has high power and fidelity across a range of spatial molecular technologies and diseases.

Google Earth AI’s Population Dynamics Foundation Model applied to global public health

Tue, 06 Oct 2026 00:00:00 +0000

October 6, 2026 Arbaaz Muslim, Software Engineer, Google Research, and Gautam Prasad, Software Engineer, Google Research With Google Earth AI’s Population Dynamics Foundation Model (PDFM), we can address the data gaps and temporal reporting lags of existing epidemiological workflows. In our latest work, we present five partner-driven case studies demonstrating how this model exemplifies the planetary geospatial foundation model paradigm for global public health. Public health decisions rely heavily on timely, granular data. For health conditions, such as cardiovascular disease or postpartum depression, that data shows us where to focus resources and support. For acute disease outbreaks, such as dengue or cholera, it can help inform urgent operational protocols and resource allocation. However, conventional epidemiological surveillance is often hindered by limitations in data: multi-year reporting lags, data siloed by rigid geopolitical boundaries, and data sparsity. Even without these limitations, traditional modeling approaches require extensive task-specific data collection and custom data engineering pipelines that are difficult to deploy during rapid outbreaks or in resource-constrained settings. To address these systemic bottlenecks, we introduce a new paradigm in public health leveraging planetary geospatial foundation models. Using Google Earth AI’s Population Dynamics Foundation Model (PDFM) as a proof-of-concept, we demonstrate how self-supervised, pre-trained representations of "place" can be integrated directly into existing health sciences and epidemiological workflows as plug-and-play inputs — enhancing the statistical and machine learning (ML) models epidemiologists already use, rather than building new pipelines from scratch. PDFM compresses privacy-preserving search trends, human mobility, built-environment density, and environmental determinants into location embeddings. Without requiring task-specific fine-tuning, these off-the-shelf location embeddings matched or improved on conventional inputs across a wide variety of disease domains, geographic settings, and epidemiological tasks. Part of Google Earth AI — our suite of geospatial models connecting satellite imagery, weather, anonymous search trends, human mobility, and other population dynamics — PDFM uses self-supervised learning to synthesize the following diverse, privacy-preserving…

Sherpa: Teaching LLMs to Teach Adaptively

Tue, 06 Oct 2026 00:00:00 +0000

Large language models (LLMs) have become increasingly capable problem solvers, but being able to solve a problem is not the same as being able to teach it. Existing approaches to training LLMs as teachers rely on demonstrations, preference data, or predefined pedagogical criteria that specify what good teaching looks like. However, these signals are often not grounded in individual student learning outcomes, where effective teaching strategies can vary substantially across learners. To address this, we introduce Sherpa, a multi-turn reinforcement learning framework that instantiates multiple student archetypes with LLMs conditioned on distinct learning preferences and trains a teacher model to adapt its instruction by directly maximizing their learning outcomes. Teacher LLMs trained with Sherpa improve instructed students' performance across all archetypes by an average of 20.5 percentage points. Under MathTutorBench's evaluation, Sherpa raises the overall pedagogy score from 52.5% to 79.2%, indicating better teaching responses. Our human studies show that the trained teacher is preferred over the base model in 79.6% of pairwise comparisons. Together, Sherpa trains LLM teachers to adapt to diverse simulated students and become better aligned with human teachers, paving the road towards AI tutors teaching real students.

OpenAI's Navier–Stokes Claim Puts Mathematicians in a Reckoning at Heidelberg Forum

Mon, 05 Oct 2026 22:30:01 +0000

On 13 September, leading luminaries and up-and-coming talents in mathematics and computer science congregated at the annual Heidelberg Laureate Forum in Germany for a week of discussion, networking, and—naturally—wurst. Having attended several of these events in the past, the mathematicians normally focus their chatter on which famed researchers are present, or what interesting problems they have been working on. But instead, every snippet of conversation caught in passing or any debate accidentally overheard was about how AI companies such as OpenAI, Anthropic, and Google are steamrollering their way through mathematics. And there is good reason for these fervent discussions. Mathematics is the perfect testing ground for AI, involving step-by-step logical reasoning and answers that are automatically and objectively verifiable. This has led tech giants to develop their AI mathematical capabilities at a terrifying rate this year, leading to both new solutions to previously unsolved problems and a reckoning within the community as to what it means to do math in the age of AI. Tech Giants Target the Millennium Problems In short order, AI has gone from struggling with everyday research-level problems to solving a whole raft of teasers posed by prolific Hungarian mathematician Paul Erdős, then verifying the proof of Fermat’s Last Theorem, and—most recently and famously—OpenAI announcing it had solved the Navier-Stokes existence and smoothness problem. As attendee and young researcher Ailsa Robertson, of the University of Amsterdam, put it: “AI and LLMs set the math community on fire over summer.” Of the seven extremely difficult Millennium Prize Problems posed in the year 2000 by the Clay Mathematics Institute, only one, the Poincaré conjecture, has been solved by humans so far. OpenAI’s claim to have solved a second, the Navier-Stokes existence and smoothness problem, if verified, represents a watershed moment for automated reasoning. Fields Medalist Jacob Tsimerman, of the University of Toronto, was keen to recognize this achievement during a press conference at the forum: “We’ve seen rapid increases in capabilities, even faster than many people, including myself, expected,” he said. “Is AI doing something new? I don’t know the details, but…solving Navier-Stokes feels pretty definitive.” Since this announcement, rumors have swirled about which of the remaining…

Technical Report on the Turba Fertilizer Machine Learning Stack in Morocco

Mon, 05 Oct 2026 08:04:09 +0000

Site-specific fertilizer recommendation systems adapt nutrient advice to location, soil properties, crop type, and production targets, but scientific reuse is constrained when recommendation functions remain accessible mainly through interactive interfaces, outputs are not versioned, and trained approximations cannot be independently loaded or benchmarked. This technical report presents the Turba fertilizer machine learning stack, a three-layer open-source implementation for reproducible site-specific fertilizer recommendation in Morocco. turba-client provides programmatic access to publicly accessible site profiles, crop-specific target-yield spaces, and N, P_2O_5, and K_2O recommendation workflows; turba-data distributes analysis-ready snapshots; and turba-models packages crop-specific machine learning surrogates of recommendation outputs. The architecture links upstream retrieval, versioned analytical snapshots, reproducible cross-model benchmarking, and loadable offline surrogates while preserving the distinction between recommendation-system outputs, observed agricultural data, and model-generated predictions. The first dataset was constructed from 44,096 unique ESA WorldCereal locations. Scenario expansion across supported cereal workflows generated 132,017 crop-location recommendation requests under a medium target-yield setting. The resulting 22-variable dataset spans 10 regions, 66 provinces, and 1,149 communes. Nine regression families were evaluated under a fixed deterministic 80/20 protocol, and the current release packages five best-performing crop-specific models. The machine learning task is recommendation-function emulation rather than prediction of observed crop response. The stack provides a reproducible basis for spatial and temporal validation, uncertainty estimation, field-trial comparison, and future integration with additional data.

Interview with William Yijiang Li: vision language models and the physical world

Mon, 05 Oct 2026 08:00:47 +0000

At the International Conference on Machine Learning (ICML 2026) , Dezhi Luo, Yijiang Li, Maijunxian Wang, Tianwei Zhao, Bingyang Wang, Siheng Wang, Pinyuan Feng, Pooyan Rahmanzadehgervi, Ziqiao Ma , and Hokin Deng presented their work Vision Language Models Cannot Reason About Physical Transformation . In this interview, William Yijiang Li tells us more about the research, the method the team used, and the controlled experiments they carried out. What is the topic of the research in your paper and why is it an interesting area for study? Our research asks a fairly fundamental question: do vision-language models actually understand how the physical world changes over time? Modern vision-language models are increasingly being used in areas such as robotics, embodied agents, and video understanding. They can recognize objects, describe scenes, and answer surprisingly sophisticated questions. However, being able to describe a scene is different from maintaining an understanding of an object or physical quantity as the scene changes. We study this through the concept of conservation . For example, if water is poured from a short, wide glass into a tall, narrow one, its appearance changes dramatically, but the amount of water remains the same. Similarly, spreading a row of coins farther apart does not change the number of coins. These tasks are simple for humans, but they require an important form of reasoning: you need to track what happens during a transformation and distinguish changes in appearance from changes in the underlying physical state. This is particularly interesting for embodied AI. If an AI system is expected to interact with the real world, it cannot rely only on static visual recognition. It needs to maintain and update representations of objects and their properties as events unfold. What are the main contributions of your research? I would summarize the contributions in three parts. First, and most importantly, our study shows that current vision-language models still struggle to maintain and update a stable representation of the physical world as it changes over time. Although these models can often answer basic questions about physical concepts such as conservation correctly, our controlled experiments show that this does not necessarily mean they understand the underlying transformation. Models that perform well when a quantity is conserved…

FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models

Mon, 05 Oct 2026 00:00:00 +0000

Remote sensing foundation models (RSFMs) are commonly evaluated using aggregate metrics, which can hide systematic performance disparities across ecological regions. We introduce FairRSFM, a biome-aware benchmark for evaluating ecological group robustness in RSFMs. FairRSFM maps georeferenced samples from 14 terrestrial biome classes into six ecologically meaningful macro-groups and evaluates models under a unified frozen-backbone evaluation protocol. The benchmark covers four downstream datasets: m-EuroSAT, m-BigEarthNet, m-SA-Crop-Type, and MMEarth20K with Dynamic World label maps. Using Prithvi-EO-2.0, SatMAE, and DOFA across three random seeds, we show that aggregate performance consistently masks biome-dependent disparities across architectures and tasks. For example, Prithvi-EO-2.0 reaches 90.98% overall macro-F1 on m-EuroSAT but a mean worst-group score of only 83.72%, while m-SA-Crop-Type drops from 27.30% overall mIoU to 18.47% in the Xeric and Mineralogical group. We further evaluate Biome-Orthogonal Linear Probing (BOLP), Dynamic Biome Reweighting (DBR), and GroupDRO as complementary mitigation baselines. Their effectiveness is model- and task-dependent; for example, BOLP improves Prithvi-EO-2.0 worst-group F1@opt on m-BigEarthNet from 46.12% to 50.27% without updating the RSFM backbone. FairRSFM provides a reusable protocol for diagnosing and mitigating ecological robustness gaps in remote sensing foundation models. Code and datasets are available at: https://github.com/aminurhossain/FairRSFM.

Collaborative Personalized Preference Alignment for LLMs under Data Deficiency

Mon, 05 Oct 2026 00:00:00 +0000

Real-world users often exhibit highly heterogeneous preferences over multiple objectives for LLM responses. A lightweight aligner can tailor these responses to individual preferences, but scarce user-specific feedback makes personalized training difficult. Learning shared initializations across users can support few-shot adaptation. However, heterogeneous preferences and competing objectives cause gradient conflicts across users and within each user, hindering effective initialization learning. This raises a central question: how can we collaboratively learn aligner initializations that support few-shot adaptation to diverse user preferences? To answer this question, we propose Approximate Pareto Optimality (APO). We first group users whose updates are compatible, so that their information can be combined with less interference. Within each group, we combine gradient descent with controlled ascent to coordinate competing objectives and move towards preference-specific points on the Pareto front. This produces an initialization that is close to the optima of the users in the group. We then iteratively refine it using updates from few-shot local adaptation, making it more effective for personalization. Furthermore, we establish conditional suboptimality bounds for a one-local-step collaborative update and characterize how initialization error affects subsequent stochastic adaptation. Experiments on Fed-ChatbotPA and UltraFeedback show consistent improvements over existing methods using only 20 local examples.

Photonic neuromorphic learning via generalized in situ physical gradient descent

Mon, 05 Oct 2026 00:00:00 +0000

Physics-based neuromorphic computing promises major gains in performance and energy efficiency, but training remains largely in silico, requiring costly physical modeling and suffering from fabrication-induced errors. Here we introduce on-chip, in situ physical gradient descent (INSPIRE), a general training method for photonic integrated circuits. INSPIRE uses on-chip synthetic time-reversal holography to measure the full complex fields of bidirectional photonic modes, enabling gradient computation and parameter updates directly in the physical system. The framework is topology-agnostic and compatible with diverse optical circuits. Experiments yield trained matrices with a relative error of 0.26%, and we demonstrate in situ training through scattering media for matrices larger than the number of native tunable elements. INSPIRE also enables meta-photonic circuits to perform in situ meta-learning, realizing single-shot photonic learning with 251-fold model compression and 136-fold task-specific training acceleration. This work offers a practical route toward adaptive and efficient intelligent photonic systems. This study reports a physics-based in situ learning method that trains artificial neural networks directly within the photonic integrated circuits, turning diverse optical circuits into efficient AI learning hardware.

Execution-Aligned Progressive Noise for Consistent Asynchronous Replanning in Generative Robot Policies

Mon, 05 Oct 2026 00:00:00 +0000

Continuous asynchronous replanning is essential for real-time generative robot policies, but independent stochastic initialization can cause mode switching and inconsistent continuation across action chunks. We propose Execution-Aligned Progressive Noise (EAPN), which introduces structured stochasticity at both inter-chunk and intra-chunk levels. Across replanning steps, EAPN propagates a shared noise trajectory and aligns it with the actual execution displacement, establishing execution-aligned inter-chunk correlation. Within each action chunk, it models temporal correlation along action time. The aligned stochastic history is further combined with committed action context to condition subsequent generation, allowing new chunks to continue from execution-consistent generative states rather than restart from independent noise. We evaluate EAPN on D3IL, Kinetix, LIBERO, and real-world manipulation tasks. EAPN improves multimodal behavior consistency on D3IL and achieves an average success rate of 88.59% on Kinetix. On LIBERO, it remains robust and maintains strong task performance even under long inference delays. Real-robot experiments further achieve 90.0% success on Object Storage and 96.7% on bimanual Cloth Folding, demonstrating reliable continuous execution under asynchronous replanning.

Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents

Mon, 05 Oct 2026 00:00:00 +0000

Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester could not derive, and departments can silently compute a same-named key performance indicator (KPI) through conflicting logic. Existing agent-memory systems (e.g., MemGPT, Zep, A-MEM) gate retrieval by content, ownership, and role, not derivation, missing a cached insight that embeds a forbidden column. We introduce the Analytical Memory Unit (AMU), a memory schema that attaches a full derivation (lineage) graph to every cached result, gated by a retrieval policy that serves a hit only when the requester is authorised for every column touched. Provided lineage recording is complete, we prove by construction that the policy blocks retrieval of results derived from a sensitive column outside the requester's permissions, at O(n) worst case -- a conditional design guarantee, not an empirical claim, that excludes derived features encoding sensitive information without naming their source. Eliminating measured leakage required 75-90% recorded lineage completeness, so we treat 90% as a conservative deployment target. Across six experiments, lineage-gated retrieval removes the 18.8-25.5% cross-department leakage naive content-gated memory suffers, keeping 81.5-82.6% of memory reuse at 13.8 microsecond worst-case overhead. A real-agent proof-of-concept with LLM-generated SQL is consistent with the guarantee: zero leaks over 9 round-trips, two conflicts caught automatically -- though a feasibility demonstration, not evidence of production viability. This offers a practical governance layer for shared agent memory, complementing source-layer access control and supporting EU AI Act compliance.

Detectrons convert transient RNA sequences into stable DNA barcodes for high-throughput analysis of RNA-dependent processes

Mon, 05 Oct 2026 00:00:00 +0000

Programmable RNA sensors such as toehold switches are used to detect specific RNA sequences. However, their reliance on protein-based or RNA-based outputs limits their use in multiplexed and sequencing-based applications. Here we introduce Detectrons, modular biosensors that couple programmable toehold switches with retron-mediated reverse transcription to transduce RNA inputs into unique DNA barcodes, converting dynamic RNA signals into durable DNA records within living cells. The framework enables alternative modes of transcript-based sensing with applications including viral infection detection. Through the construction of a synthetic toehold retron library and application of machine learning, we uncover key design principles that improve signal strength and specificity. We apply Detectrons to the multiplexed live-cell detection of specific phage infections, enabling transcript-triggered barcode synthesis and quantitative host susceptibility profiling in pooled bacterial populations. Detectrons provide a scalable and generalizable strategy for phage screening and for recording transcriptional events in complex bacterial communities. RNA sequences are detected and converted directly into DNA barcodes.

Beyond Semantic Similarity: Performance and Costs of Agentic Retrieval for Complex Tasks

Mon, 05 Oct 2026 00:00:00 +0000

Modern information systems, including many agentic workflows, use dense retrieval to explore large amounts of unstructured data. However, dense retrieval relies on surface-level semantic similarity, which is insufficient for increasingly complex search applications. Here, we investigate agentic retrieval that combines the reasoning capabilities of Large Language Models (LLMs) with the efficient corpus exploration of retrievers in a ReAct agentic loop to solve complex retrieval tasks. In our experiments, we show that agentic retrieval is more effective than standard retrieval, improving nDCG@10 by 8.7 points using the same embedding model. Moreover, while specialized retrieval methods struggle on out-of-domain tasks, agentic retrieval is highly generalizable: the same pipeline achieves competitive results on both the ViDoRe v3 and BRIGHT leaderboards. However, this improvement comes at a cost. On average, agentic retrieval takes 107.4 seconds, compared to 0.67 seconds for standard retrieval, and consumes 764.1K input and 5.8K output tokens per query. In short, our study demonstrates the effectiveness of agentic retrieval in modern data systems and motivates future work on more cost-efficient retrieval agents for large-scale deployment.

Sharpen Without Search: On-Policy Distillation of Sequence-Level Power Distribution

Mon, 05 Oct 2026 00:00:00 +0000

A language model can give a correct answer more probability than any single incorrect answer and still usually sample an incorrect one, because the incorrect answers together hold more probability. The power distribution raises each complete answer's probability to a power above one and renormalizes, shifting probability toward answers the model finds most likely (sharpening). Sampling from it improves reasoning without changing parameters, but needs many scored candidates per query. We show that a model can instead be trained to produce such answers in one generation. On-policy power distillation (OPPD) runs a sequential Monte Carlo sampler in which the model being trained generates candidates and a frozen teacher's power distribution weights them; the same probabilities weight each answer in a maximum-likelihood update. Training raises single-generation accuracy by up to 23.0 points on MATH500 and 27.3 on GSM8K over the untrained model at the same temperature, and one generation scores 2.4 and 3.5 points above published power sampling with 64 candidates, recovering 94 percent of the gain that 16 candidates give the untrained model. For context, against GRPO trained with verified rewards from the same checkpoint and budget, OPPD scores 3.8, 4.0 and 5.4 points higher on MATH500, GSM8K and AIME using no reference answers; the two are complementary, and OPPD applied after GRPO adds up to 9.3 points. Trained only on mathematics, OPPD raises HumanEval accuracy by up to 5.3 points. One loss coefficient moves the sharpening exponent the model absorbs between 1.19 and 2.02, against 1.14 for ordinary on-policy distillation, and it rises mostly on the model's own answers. Gains hold across model families and sizes, including a model already trained with verified rewards, where lowering the temperature gives nothing and OPPD adds 4.4 points on MATH500. Code: https://github.com/ArminAzizi98/OPPD.

Rationale-Guided Policy Optimization: Learning to Reason with Adaptive Rationale Scaffolding

Mon, 05 Oct 2026 00:00:00 +0000

On-policy reinforcement learning has become a central paradigm for improving the reasoning abilities of large language models. However, its effectiveness is often limited by reward sparsity: when a model fails to discover correct trajectories for difficult problems, the optimization process receives little useful signal and may stagnate. Existing approaches mitigate this issue by incorporating off-policy demonstrations, expert traces, or model-generated solutions, but they typically require the auxiliary data to match the format of the reinforcement-learning task, often relying on rejection sampling from stronger models to obtain suitable training trajectories. We introduce Rationale-Guided Policy Optimization (RGPO), a framework that adaptively leverages ground-truth rationale information according to the model's current capability while preserving its freedom to explore. Rather than treating reference solutions as fixed imitation targets, RGPO uses them as temporary scaffolds: rationales help the model generate improved responses, after which only higher-reward, model-generated solutions are transferred back to the original unguided setting. This design allows training to exploit available ground-truth information without requiring off-policy data to follow the same format as the RL task. Across both language-only and vision-language reasoning settings, RGPO consistently improves performance over RLVR baselines, and ablation studies show that adaptive rationale guidance is a key contributor to these gains. These results suggest that RGPO offers a practical and general approach for reducing reward sparsity, stabilizing reinforcement learning, and improving reasoning performance in both text-only and multimodal models.

Google DeepMind creates watermarked AI-designed proteins that still work in lab tests

Sun, 04 Oct 2026 23:45:08 +0000

Google DeepMind creates watermarked AI-designed proteins that still work in lab tests SynthID Bio embeds a detectable signature into AI-generated protein sequences and structures, with Nature research showing the watermark can survive without significantly disrupting biological function Google DeepMind researchers have demonstrated a way to watermark AI-designed proteins while preserving their biological function, an attempt to bring the provenance systems already used for AI-generated media into synthetic biology. The new method, SynthID Bio, puts a detectable signature directly into either a protein’s amino acid sequence or its predicted three-dimensional structure. The central challenge is very different from watermarking an AI-generated image. Changing a few pixels may leave a picture effectively unchanged. Altering a protein can change how it folds, binds to other molecules or functions altogether. DeepMind’s researchers had to show not simply that their watermark could be detected, but that the resulting biological designs still worked. In laboratory testing reported in Nature, watermarked protein binders remained functional across three targets: VEGF-A, PD-L1 and the receptor-binding domain of the SARS-CoV-2 spike protein. Pushmeet Kohli, Chief Scientist at Google Cloud and VP Science at Google DeepMind, described the result on LinkedIn as the “successful synthesis of AI-designed proteins that are both functional and watermarked.” How do you watermark a protein? SynthID Bio actually covers two different approaches. For protein sequences, the system subtly changes how amino acids are selected while an AI model generates the sequence. The resulting signature can then be detected using a secret watermarking key. For three-dimensional protein structures, the researchers took a different route. They fine-tuned part of AlphaFold 3 so the watermark is built into the predicted atomic coordinates produced by the model. The recommended version of that structural method achieved a detection rate above 99.8% at a 0.1% false-positive rate in the researchers’ tests, without reducing key structural accuracy measures compared with the AlphaFold 3 baseline. The more important test for the sequence watermark happened outside the computer. Researchers synthesized watermarked and non-watermarked protein binders and tested their ability to attach to the three target prot…

Classical Solutions and Singularity Formation in Incompressible Fluids

Sun, 04 Oct 2026 15:25:28 +0000

[This is a guest post by Diego Córdoba and Luis Martínez-Zoroa . This blog post was initially written in a different file format and converted using AI. — T.] Euler formulated the equations for an inviscid incompressible fluid more than 250 years ago. Published in his 1757 memoir , they belong to the earliest systems of partial differential equations in mathematical physics, following d’Alembert’s work on the wave equation. Understanding the evolution they describe has required the work of many generations of mathematicians. One of the central questions is whether an initially regular fluid flow can develop a singularity in finite time. The recent work on Euler and Navier–Stokes obtained with the aid of large language models has brought renewed attention to this question. These exciting developments build on decades of mathematical research, including works from recent years, when the field has continued to be specially active. In this post we describe some contributions to that body of knowledge, including our own. We will go over some of that recent mathematical literature, and discuss the ideas of constructing blow-ups through a cascade of vortex layers. Partial differential equations provide a common language for problems ranging from wave propagation and fluid motion to geometry and general relativity. For an evolution equation, the Cauchy problem asks us to determine the future from prescribed initial data. A local existence theorem gives a solution for a short time; the next question is whether it continues for all time or develops a singularity at a finite time. This distinction between global existence and finite-time blow-up is already visible in the elementary Riccati equation The solution starts from a finite value and remains smooth until , when it becomes unbounded. The quadratic nonlinearity in three-dimensional Euler suggests a similar possibility, but the fluid problem also involves transport, geometry and a pressure determined nonlocally by the entire flow. The challenge is to understand whether the full equation can sustain the amplification suggested by this simple ODE. The central aim of our research has been to construct classical solutions of incompressible fluid equations that develop singularities in finite time, starting within a regime of local existence and uniqueness. We want the initial data to determine a unique regular evolut…

LLM-as-Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them

Sun, 04 Oct 2026 00:00:00 +0000

Jev-style decision models return categorical probability distributions over predefined options without generating free-form text, enabling software systems to act on their outputs directly. In this work, we investigate the extent to which general-purpose LLMs already possess this capability out of the box, and when fine-tuning is actually necessary. We present LLM-as-Jev, an architecture-preserving framework that extracts calibrated decisions directly from next-token probabilities over bracketed numeric identifiers. LLM-as-Jev provides both a training-free inference recipe and a fine-tuning objective that optimizes candidate selection via a tree-factorized listwise loss while anchoring auxiliary predictions to the base model using KL divergence penalties. Evaluating on Qwen3.5-4B and Qwen3-0.6B, we find that modern LLMs are inherently effective decision models: without training, the 4B model matches community Jev-style models built on the same backbone, outperforms letter-logit readouts, supports arbitrary option counts, and natively handles multimodal decisions over images. Fine-tuning provides targeted rather than universal benefits -- substantially improving weaker models and specific tasks (such as many-option intent routing), but offering diminishing returns for strong backbones. Crucially, our KL anchors prevent behavioral degradation in conversational text generation, with LoRA delivering the strongest performance on capable models.

ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience

Sun, 04 Oct 2026 00:00:00 +0000

A large language model (LLM) agent solves long-horizon tasks through many reasoning-action turns, with one verification signal at termination. Deployed agents face streams of related tasks, making their trajectories a natural resource for improvement. In-context adaptation agents store reflections, memories, or skills as text, so reuse depends on retrieving the right experience and on a frozen policy executing it. We study Online Agentic Test-Time Training (OaTTT), which trains the LLM's weights on its own execution trajectories during deployment. The agent executes each task once, in one pass over the stream, and the executed trajectory with its verification result is the only learning signal for weight updates that persist across tasks. Directly imitating or reinforcing the generated tokens of this single attempt destabilizes the policy. We introduce ASCENT (Agentic Self-distillation for Cross-task EvolutioN at Test-time), which instead self-distills verified experience. A stable version of the LLM, its frozen initial copy, receives the verified trajectory as privileged information and predicts next-token distributions along it with this hindsight. Distilling them into persistent LoRA fast weights updates the agent for later tasks, without an external reference solution or stronger teacher. By further removing invalid-action turns, ASCENT distills enhanced privileged experience for more efficient execution. We characterize its population target and the limits of sparse outcome selection. Across ALFWorld, WebShop, and AppWorld at varied model scales, ASCENT improves task success and interaction efficiency as experience accumulates, outperforms online adaptation methods, and transfers to held-out scenes, showing that an agent can consolidate verified experience into its weights without a separate training phase or memory retrieval. Project page: https://artificer-ai-lab.github.io/ASCENT

CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video

Sun, 04 Oct 2026 00:00:00 +0000

Partnered human-humanoid interaction couples locomotion with continuous physical contact. A humanoid needs to coordinate with a person's motion while responding to interaction forces and maintaining stable and natural movement. We present CoDance, a framework for learning reactive and compliant human-humanoid interaction from video. We study partnered dancing as a challenging instantiation, where a humanoid coordinates its footsteps with a moving partner and maintains continuous two-hand contact. Given a single video of two human dancers, CoDance retargets their motions into a robot reference and a moving partner. We introduce a multi-link compliance augmentation that transforms the kinematic demonstration into force-aware training data by adapting the robot reference under structured forces at both hands. Policies trained on this data follow the observed partner while preserving the demonstrated locomotion style and responding compliantly to physical interaction. In simulation, the policies adapt their footsteps to changes in the partner and reproduce approximately 80% of the wrist displacement encoded by the augmented demonstrations. On a physical humanoid, CoDance enables sustained two-hand dancing with a human partner including repeated transitions between forward and backward motions.

LMBuild: Evaluating LLM Agents for Generating Buildable and Functional Structures

Sat, 03 Oct 2026 00:00:00 +0000

LLM-based agents are increasingly capable of generating complex 3D structures, with the potential to reshape how objects are designed and realized in the physical world. Yet, producing elegant geometry is fundamentally different from producing objects that can be built and perform their intended functions. Existing evaluations largely focus on geometric quality while overlooking physical realizability. We introduce LMBuild, a benchmark for evaluating LLM agents on generating buildable and functional structures. LMBuild represents generated objects as assembled structures comprising part decompositions, joints, materials, and sequences. To support reproducible evaluation, we provide a unified framework consisting of: (1) an interactive environment in which agents can use tools to retrieve, create, and place components to construct objects; (2) a curated benchmark that repurposes established CAD datasets and augments them with knowledge from Wikipedia; and (3) a evaluation framework covering structural soundness, functional affordance, design quality, and physical realization. Evaluations across 30 systems reveal several intriguing findings: (a) Soundness and alignment are no longer the primary bottlenecks for frontier closed-source models, while functional affordance and physical operability remain substantially more challenging; (b) stronger models more effectively create new components, whereas weaker models tend to rely on retrieval; and (c) providing functional specifications substantially improves part completeness, kinematics, and physical operability. These results show that generating real-world structures requires deeper reasoning about functional affordances, mechanics, and designing and creating novel components. We expect LMBuild to provide a foundation for measuring progress and incentivizing research toward agents that generate buildable and functional structures.

LoHi: A Joint Allocation Perspective on Frames, Pixels, and Front-End Latency in Long-Video Understanding

Sat, 03 Oct 2026 00:00:00 +0000

Efficient long-video understanding with vision-language models (VLMs) is often framed as selecting informative frames or visual tokens at a fixed native resolution. We show that per-frame resolution can instead be traded for denser temporal coverage, while front-end decoding latency depends on the size of the candidate pool rather than the final token budget. An empirical study across multiple VLMs and long-video benchmarks yields three findings: dense low-resolution sampling outperforms sparse native-resolution sampling at matched token budgets; resolution-sensitive tasks benefit from selected high-resolution frames; and front-end decoding dominates wall time for hour-long videos. Motivated by these findings, we introduce LoHi, a training-free, single-pass framework that combines a dense low-resolution video stream with sparse high-resolution image frames through the VLM's native video and image pathways. LoHi-Anchor selects high-resolution frames using codec I-frame metadata, while LoHi-SemDiv uses query relevance and visual diversity over CLIP features. Across three long-video benchmarks, LoHi improves average accuracy by 10.6 percentage points over the native-resolution baseline at a matched token budget and by 5.2 percentage points over the strongest prior efficiency method. It also reduces front-end decoding latency by up to 7x on hour-long videos. Project page: https://sixundong.com/projects/lohi

DiffGate: Difficulty-Gated Teacher Guidance for On-Policy Distillation

Sat, 03 Oct 2026 00:00:00 +0000

On-policy distillation (OPD) has emerged as a widely used paradigm for post-training large language models, reducing the train--test mismatch of conventional distillation by supervising the student on its own generated trajectories. However, existing OPD objectives remain largely token-local and outcome-agnostic, optimizing teacher--student agreement at each prefix despite reasoning quality being determined at the trajectory level. Reinforcement learning with verifiable rewards (RLVR), particularly Group Relative Policy Optimization (GRPO), provides complementary outcome-level supervision but suffers from sparse rewards and coarse credit assignment. We show that OPD and RLVR exhibit complementary blind spots: teacher signals provide dense local guidance but are weakly aligned with rollout correctness, whereas group-relative rewards capture task success but provide coarse token-level credit and vanish on all-failure groups. We introduce DiffGate, an outcome-gated objective that combines GRPO with selective, bounded teacher guidance. Teacher supervision is applied only to failed trajectories, scaled by group difficulty, and smoothly bounded to prevent extreme teacher--student discrepancies from dominating optimization. The verifier therefore determines which trajectories receive teacher guidance, while the teacher provides dense token-level update directions within those trajectories. Across Qwen3-0.6B and Qwen3-1.7B students, DiffGate improves code avg@8 over matched GRPO by +1.7 and +1.8 points and pass@8 by +1.6 and +5.7 points, respectively. On mathematics, avg@8 remains within 0.5 points of GRPO while pass@8 improves by +1.1 and +3.9 points. Overall, DiffGate improves pass@8 across all four model--domain settings, demonstrating improved solution coverage under our evaluation protocol.

The Numerical Linear Algebra of Large Language Models

Sat, 03 Oct 2026 00:00:00 +0000

Numerical Linear Algebra (NLA) has consistently played a vital role in advancing science by providing tools to solve fundamental problems encountered in scientific and engineering applications. Over the decades, it has continually evolved to meet the demands driven by successive waves of scientific discovery. For instance, during the 1950s and 1960s, substantial efforts were devoted to developing methods for solving eigenvalue problems that emerged from the rapidly growing field of aerodynamics. This led to the discovery of the LR and QR algorithms. Later the attention turned to the solution of sparse linear systems that were common in applications like computational aerodynamics. Today we are experiencing yet another wave of major scientific advancement and NLA is once more at the heart of its development. This Machine Learning (ML) wave is proving to be utterly disruptive in science and engineering. Many tools in ML particularly Large Language Models (LLMs) are grounded in matrix and tensor methods. As we are approaching Artificial General Intelligence (AGI), it is clear that matrix methods will be called to play an even more significant role. For the numerical linear practitioner the speed of the current change makes it particularly challenging to adapt. This is a survey article that centers on machine learning techniques, with a particular focus on large language models. It has two main objectives. The first is to clarify the core concepts behind Large Language Models in a manner accessible to specialists in numerical methods. The second is to examine the key Numerical Linear Algebra concepts employed by LLM techniques, while also highlighting several significant recent contributions of NLA to the field.

Google DeepMind watermarks AI-designed proteins, and the first binders still bind

Fri, 02 Oct 2026 12:49:52 +0000

Google DeepMind watermarks AI-designed proteins, and the first binders still bind Google DeepMind has taken its SynthID watermark into biology, and says the first watermarked protein binders still bind. In wet-lab testing against three target proteins, VEGF-A, the SARS-CoV-2 spike protein RBD and PD-L1, the watermarked designs “matched the hit rate, binding affinity, and natural sequence diversity of unwatermarked versions”, the company says. It calls those “the first-ever watermarked and biologically functional protein binders”. The method is published as a peer-reviewed paper in Nature, “Function-preserving watermarking of AI-generated proteins”, and Google describes the work as a proof of concept. The mark goes into the sequence, not into a file SynthID Bio “embeds an imperceptible signature directly into the biological code”, Google says, so the watermark is “verifiable not just on a digital model but on the synthesized, physical protein itself”. A design that leaves the model and gets made in a laboratory carries the mark with it. For binders, molecules built to latch onto other proteins, the team used AlphaProteo, DeepMind’s own binder design method, alongside a SynthID Bio-enabled version of ProteinMPNN, a widely used protein sequence generator. Adaptyv Bio helped with the in vitro validation. AlphaFold 3 carries the watermark in its weights The second target is 3D structures. Google says SynthID Bio fine-tunes “a small part of AlphaFold 3’s diffusion network” so that predicted coordinates “inherently carry a detectable signature regardless of who runs the model”, and says prediction accuracy is preserved alongside near-perfect detectability. Google gives two reasons for wanting any of this. Novel AI designs “can bypass traditional DNA synthesis screening”, and mislabelled synthetic 3D structures “risk polluting public databases and misleading downstream research”. Tampering is the part that does not work yet “Moving forward, key challenges include making the watermark more robust against deliberate tampering,” Google says. It suggests pairing the watermark with provenance metadata, along the lines of C2PA for digital media, or with central repositories of AI-generated biological data. The work on viruses is further back. With the Hie lab at Stanford University and the Arc Institute, DeepMind put SynthID Bio into the genomic model Evo 2 and watermark…

Open-sourcing AstaBrief 8B, a small open model for fast cited report generation

Fri, 02 Oct 2026 08:00:00 +0000

Language models can already help researchers search the literature, synthesize evidence, and work through complex questions. But scientific work places particular demands on these models—answers need to stay grounded in evidence, the models need to preserve what the evidence actually supports rather than quietly broadening a study’s conclusions, and researchers need to be able to verify the final outputs. We see that in how scientists use Asta, our agentic platform for scientific work. Instead of simple keyword searches, users often bring substantial context and many constraints—for example, asking Asta to compare approaches across a body of literature while accounting for a particular method, population, or setting. Many also return to generated reports later, treating them as working research artifacts rather than one-off answers. We wanted to help scientists generate cited reports faster, with a model they could download and run themselves. To do that, we tested whether a small, open model trained specifically for scientific report generation could match the report quality of the proprietary models we were using, while reducing generation time and serving costs. We built AstaBrief 8B, a model that turns a research question and retrieved literature excerpts into a cited report. AstaBrief is available in Asta’s Generate a report feature today as Fast mode alongside Claude-powered Thinking mode, and we’re also open-sourcing it and the training data so others can study, reproduce, and build on our approach. Developing AstaBrief required tens of thousands of real research queries, citation-focused filtering, preference data, and a redesigned report-generation pipeline that writes the full report in one pass rather than section by section. The result is nearly an order-of-magnitude reduction in report generation time compared to the proprietary models we tracked—across the full Asta pipeline, Fast mode averages 51.1 seconds per report compared with 178.5 seconds for Thinking mode, about 3.5× faster. Together, those efficiency gains made AstaBrief a useful test case for a broader goal: building open language models that can be adapted to the specific demands of scientific work. Open weights will also let institutions run AstaBrief on their own infrastructure, which is necessary when research questions reveal sensitive or unpublished work. Alongside the model we…

Solving Open Research Problems Together: Six Papers from Meta AI and Mathematicians

Fri, 02 Oct 2026 07:00:00 +0000

Solving Open Research Problems Together Mathematicians and Muse Spark collaborate on six research papers Earlier this year, our models achieved gold-medal-level performance across five high-school Olympiad competitions in mathematics, physics, and chemistry. Those results inspired us to see whether AI could also help scientists solve open research problems. Competition problems can be incredibly difficult, but those problems already have a solution. Open research is different. There is no answer key, no guarantee that an approach will work. Making progress means trying and retrying ideas, making new mistakes and resolving them, and sometimes going back to the beginning. Over the past several months, we've partnered with mathematicians to explore problems across several areas of mathematics. They used both Muse Spark 1.1 and 1.2 in Thinking Mode through the regular meta.ai chat interface, with no custom research scaffold. Open research takes time. Our goal here wasn't to mass-produce papers, but to empower researchers and help them develop mathematical insights that others can understand and build on. We’re proud to partner with the scientific community to help them advance research. These collaborations were done under the following principles: - A team of mathematicians guided the research and worked with Muse Spark to explore ideas and develop the arguments. - A second group of mathematicians then reviewed their work. - Each paper clearly marks which passages were primarily drafted by researchers and which were drafted by AI. - Each paper gives credit to the earlier research and mathematical ideas it builds on. Today, we're sharing six such papers from that collaboration. Five present answers to previously open research questions. After completing our work, we learned that other teams outside Meta had independently announced solutions to some of the same problems using different approaches. We recognize and appreciate their contributions and clearly acknowledge their work and how it relates to ours in the papers. Probability: The Strict Threshold for Gaussian Ellipsoid Fitting Aykut Arslan with Muse Spark via meta.ai. Review by Babak Modami, Alexander Roitershtein, Mark Sepanski, and Grigory Sokolov Paper: The Strict Threshold for Gaussian Ellipsoid Fitting. The paper answers a long-standing question about fitting random Gaussian points in high dimensions…

Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability

Fri, 02 Oct 2026 00:00:00 +0000

Chain-of-thought (CoT) reasoning allows humans to inspect how large language models reach their answers, and oversee model behaviour. This reasoning comes at an increased inference cost, motivating efficient methods that train models to solve tasks using fewer tokens. However, a common concern is that such training may cause models to skip important reasoning steps, so the CoT no longer faithfully reflects the model's decision. It is unclear whether or when this occurs in practice, since different efficiency methods apply length pressure to models' CoT in distinct ways, and faithfully explaining a model's decision takes more tokens on some tasks than others. To understand these dynamics, we fine-tune a variety of models with three methods that apply length pressure differently, namely a fixed generation budget, a per-example length target, and a group-relative length reward. We evaluate how efficient reasoning affects CoT faithfulness (i.e., how well the CoT reflects model decisions on related inputs) and monitorability (i.e., whether the CoT reveals when input interventions alter the output). We find that it affects faithfulness and monitorability differently. Faithfulness falls in most settings, primarily because the trained models are less consistent. Monitorability is more robust, as models keep acknowledging the influence on their answer even when the CoT is much shorter.

Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models

Fri, 02 Oct 2026 00:00:00 +0000

Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment challenge, since a few commitments during denoising sharply reduce the uncertainty over the remaining masked positions and shape much of the response. Most post-training recipes for dLMs do not use this signal to decide which tokens to train on: they typically train on the final text or assign rewards to whole denoising steps, rather than selecting the individual commitments that shape the response. We introduce Pivot-SD, an efficient offline self-distillation framework that supervises only these high-impact commitments (pivots). Pivot-SD selects pivots using an information-gain metric measuring uncertainty reduction over the remaining masked positions. Pivots from successful trajectories are trained with cross-entropy, and pivots from failed trajectories with targeted unlikelihood, leaving the rest of the failed trajectory untouched. Using only 200 questions and four rollouts each, Pivot-SD improves LLaDA-8B-Instruct over full-sequence SFT and budget-matched diffusion RL baselines across math and code benchmarks.

FOVEATED: Focused Views for Improving Atomic-Fact Recall in Unstructured Knowledge Editing

Fri, 02 Oct 2026 00:00:00 +0000

Large language models (LLMs) increasingly serve as general-purpose interfaces to factual knowledge, but their parameters do not automatically reflect information that changes after pretraining. Knowledge editing (KE) provides a targeted alternative to costly retraining by modifying selected knowledge and preserving unrelated knowledge and general capabilities. Conventional KE uses structured factual triples, whereas unstructured KE (UKE) uses free-form passages containing multiple facts. Nonetheless, existing UKE editors exhibit a failure mode known as context reliance: edited LLMs can often reproduce the editing passage but fail to reliably recall its individual facts without the original passage context. We identify context-induced difficulty underestimation under the standard passage-level editing objective: later facts receive increasingly rich ground-truth context and consequently incur lower initial losses, making them appear easier to learn. In response, we propose FOVEATED, a plug-and-play framework that constructs focused views of each sentence by randomly shifting the Rotary Position Embedding (RoPE) positions assigned to the keys of its preceding context. The perturbation is applied during editing and removed afterward, leaving the model's native positional encoding unchanged at inference time. We instantiate FOVEATED for both direct-optimization and locate-then-edit editors. We theoretically analyze how FOVEATED counteracts context-induced difficulty underestimation and empirically demonstrate consistent improvements across five KE editors, two LLM backbones, and three benchmarks.

SEA-Driven OdorNet: A Large-Scale Standardized Molecular Olfactory Dataset Based on a Hybrid Semantic Taxonomy

Fri, 02 Oct 2026 00:00:00 +0000

The development of machine olfaction (digitization of odor) is currently hindered by data scarcity and the lack of standardization in descriptive labels. Datasets integration faces the challenges of descriptor-level conflicts, including cross-source labeling inconsistencies, synonym redundancy, skewed distributions, etc. To address the semantic confusion inherent in multi-source data, we developed a SEA (Statistical co-occurrence, Expert correction, and AI alignment) taxonomy framework that integrates statistical co-occurrence analysis, expert knowledge (perfumery taxonomy), and AI-assisted semantic alignment based on the definitions from both encyclopedia and odor-related scientific literatures. A unified-labeled olfactory dataset, namely OdorNet, is further presented, which is by far the largest molecule dataset with unified olfactory labels comprising over 20,000 entries and 9,000 unique molecules originated from academic literatures (1960–2021) and open databases. This process consolidates cluttered labels into a hierarchical system with the most extensively effective vocabulary coverage in the field to date. Technical validation demonstrates that a baseline model on OdorNet achieves a Macro F1 score of 0.42, substantially higher than the scores obtained on commonly used odor datasets and on OdorNet itself using the same model but without the SEA-based label taxonomy. This indicates that OdorNet allows to better capture learnable structure-odor relationships, laying a solid fundamental for digital odor editing and human-computer interaction.

Self-Supervised Scaling of Terminal Environments for Scientific Domains

Fri, 02 Oct 2026 00:00:00 +0000

Terminal agents are increasingly deployed beyond software engineering in science and other specialized domains. Constructing training environments requires executable reference behavior and a domain-specific verifier that distinguishes semantic correctness from superficially plausible artifacts. Authoring these components for each task requires repeated engineering and limits reuse. We introduce software-in-the-loop reconstruction, a self-supervised framework that obtains reference outputs and verification targets from existing software workflows, executable programs mapping structured inputs to outputs. For each workflow, we execute multiple input configurations and partition cases into public observations and hidden evaluations. Given the instruction, input schema, and public input--output observations, an agent constructs an editable program without access to the source workflow. The candidate is evaluated on hidden configurations against workflow outputs. A hierarchical verifier combines domain-specific semantic comparison, structural validity, and anti-shortcut checks, while public feedback supports iterative revision. The construction admits additional workflows and configurations without authoring a reference solution for each task. We instantiate SWR with 500 workflows and 46 software families across six domains. Across three attempts per task, Qwen3.8-Max solves 838 tasks and produces 1,422 verified trajectories, which we oversample to 3,000 reconstruction-only training examples. Supervised fine-tuning of Qwen3.8-27B improves mean Terminal-Bench 2 performance from 47.94% to 53.56% across three seeds and achieves the highest mean among four matched-token corpus controls on all four reported evaluations. These results indicate that existing scientific software can provide scalable, behaviorally verified supervision for terminal agents.

Investigating the Role of Reasoning-Language Alignment in Monolingual Retrieval-Augmented Generation

Fri, 02 Oct 2026 00:00:00 +0000

Reasoning traces improve large language models (LLMs), but current models are trained to reason mostly in English. It has been shown that forcing a model to reason in another language degrades accuracy, even when the reasoning language matches the language of the prompt -- but only for a setting where the model reasons over a short prompt. Here, we ask whether the same holds for retrieval-augmented generation (RAG), where the model must read and integrate a large amount of retrieved evidence in the target language. To study this, we build a fully monolingual German RAG question-answering testbed over the fictional world of the tabletop role-playing game The Dark Eye, a domain that is richly documented in German but too niche for the model to answer from memory, so that it has to rely on retrieval. Varying the forced reasoning language of an agentic RAG system on this testbed, we find that aligning the reasoning language with the language of the query and the retrieved documents helps. Forced German reasoning outperforms forced French, although the model benchmarks higher in French, so the benefit comes from alignment and not from language proficiency. The advantage grows when the retrieved context is richer and structure-aware. However, forced German only reaches the level of the model's native, unconstrained English reasoning without surpassing it, showing that native multilingual reasoning is needed. We publicly release the testbed and QA benchmark.

OPD Before RL: Warm-Starting Rubric-Based RL with On-Policy Distillation

Fri, 02 Oct 2026 00:00:00 +0000

Many useful language-model tasks cannot be evaluated by exact outcome verification. Rubric-based reinforcement learning (RL) addresses this issue by scoring open-ended responses against explicit criteria. However, because the reward is assigned after the complete response, the training signal does not directly identify which individual decisions contributed to the final score. We propose a two-stage training framework that uses rubrics first as privileged teacher context for dense token-level supervision, then as rewards for further RL. In the first stage, rubric-privileged on-policy distillation (RP-OPD), a student without access to the rubric matches a rubric-aware teacher's next-token distributions at student-generated prefixes. In the second stage, RL directly optimizes the rubric reward and improves beyond the observed distillation plateau. We evaluate the framework on health and science tasks using open-weight models. Across HealthBench, ResearchQA, and RubricHub Science, we compare post-training methods and vary the amount of SFT or RP-OPD training before RL, finding that our two-stage framework achieves the highest scores among the methods evaluated. RP-OPD + RL shows limited signs of reward hacking on RubricHub Science, whereas the SFT + RL baseline increasingly receives high rewards for claims of rubric compliance without providing the required content. These findings support using rubrics to guide on-policy distillation before applying rubric-based RL.

Foresight: planning future perception in streaming VLMs without retraining

Fri, 02 Oct 2026 00:00:00 +0000

Existing streaming vision-language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed throughout inference. Consequently, they cannot adapt computation to evolving scene dynamics, where different future events demand different levels and forms of perception. We show that streaming VLMs inherently possess the ability to anticipate the immediate future, and leverage this capability to dynamically configure future computation in a training-free manner. Realizing such anticipatory computation, however, is very challenging: future anticipation must be sufficiently reliable to guide computation, planning must run concurrently with streaming inference, and online reconfiguration must incur negligible overhead. To address these challenges, we introduce FORESIGHT, a dual-stream architecture comprising two Siamese LLMs with shared weights, input encoders, and KV cache. The first LLM continuously processes incoming tokens, while the second runs ahead of the stream to anticipate future context, plan future computation, and generate task responses without interrupting streaming inference. Each plan decides when to reason next, what to check then, and how densely to sample, keeping transient evidence separate from persistent control. The resulting computation plan is executed online through an efficient reconfiguration protocol with schemaguided decoding and lightweight diff-based updates, enabling dynamic adaptation with low overhead. With a frozen Qwen3-VL-8B backbone, FORESIGHT achieves 23.0 mean joint F1 on OmniPro Online evaluation beating strongest trained baseline by 9.5%, while improving the backbone by 6.7 on StreamingBench and 15.4 on OVO-Bench, with the largest gain of 18.7 when evidence arrives later in the video stream. Our source code will be made publicly available.

FastOPD: On-Policy Distillation for Lightweight VLA Deployment

Fri, 02 Oct 2026 00:00:00 +0000

Vision-Language-Action (VLA) foundation models have scaled rapidly to enhance manipulation performance and generalizability, but this scaling incurs high computational costs that render real-world deployment increasingly challenging. Existing approaches typically mitigate this issue by designing smaller architectures or reducing the iterative denoising steps in flow-based policies. In this work, we propose FastOPD, a foundation-to-lightweight VLA framework that enables the practical deployment of large-scale VLAs through efficient on-policy distillation. Specifically, FastOPD adapts a flow map for single-state teacher supervision and combines it with a self-consistency objective to construct a compact student that learns the teacher dynamics. Furthermore, we theoretically demonstrate that minimizing this objective allows the distilled student to recover a distribution on par with that induced by an ideal few-step teacher model. We evaluate FastOPD across diverse foundation policies in simulation and real-world experiments. On LIBERO, FastOPD retains 84% of the performance of π_{0.5} with only two inference steps, reducing inference latency by 78.1% while outperforming existing few-step distillation baselines in average success rate. With LingBot-VLA as the teacher, FastOPD improves the single-step success rate over the base student by 15.9 percentage points on RoboTwin 2.0. We further demonstrate its applicability to a World Action Model (WAM) and deploy a compact student distilled from MolmoAct2 on a real robot.

Native Action-Prior Learning from Videos for World Action Models

Fri, 02 Oct 2026 00:00:00 +0000

World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotated robot trajectories. Observation-only videos contain rich evidence about interaction dynamics, but existing approaches typically use them either to pretrain visual representations that must later be adapted for control, or to infer latent actions that are subsequently grounded to robot commands. We present NAVA-WAM, which introduces native action-prior learning by directly pretraining the action policy from observation-only videos, avoiding indirect representation-to-control transfer or a separate latent-action model. Our training consists of two stages. First, we pretrain on observation-only videos, where future-video flow-matching supervision over visual transitions is propagated through transition-structured joint attention to optimize the Action-DiT and learn action-relevant priors. Second, we use action-labeled demonstrations to post-train the Action-DiT for robot control through joint video--action flow matching, while asymmetric attention decouples the visual branch from iterative action denoising and enables efficient action-only inference. Extensive experiments show that NAVA-WAM consistently outperforms prior approaches under both in-distribution and out-of-distribution settings, while demonstrating strong action-label efficiency and effective real-robot generalization. These results establish native action-prior learning as an effective approach to directly pretrain action policies from observation-only videos, providing a scalable path beyond action-labeled robot data.

COSMI: COmpositional Synthesis of Multi-object Interactions

Fri, 02 Oct 2026 00:00:00 +0000

Generative models of human-object interaction are bounded by the data that exists: everyday activities involve several objects, but most captured datasets record one at a time, as multi-object capture is combinatorially expensive. Our observation is that interactions are local, so single-object captures already contain the parts of multi-object activities. We compose them: contact-consistent clips of single interactions, mirrored to balance the hands, transfer between bodies, and a language model and geometric checks admit only the pairings that are plausible, semantically and physically. Therefore, the dataset grows combinatorially with the clips rather than recording time. The COSMI dataset holds 222k sequences and 275 hours with up to five objects, nearly thirty times the largest multi-object capture, and can be extended by adding datasets or even hand-object recordings. On this data we train the COSMI method, a text-to-interaction diffusion transformer that follows how the data is built: weight-shared object slots generate a variable number of objects, predicted relative to the body parts that move them. On a benchmark with an unseen object and unseen interaction combinations, models trained on the dataset generalize to the unseen combinations. COSMI outperforms baselines in text alignment and contact accuracy, where its margin is largest on the unseen object. Code, models, and the dataset pipeline will be released on the project page: https://ptrvilya.github.io/cosmi.

Machine Learning Reveals That Elephant Choruses Encode Behavior

Thu, 01 Oct 2026 15:31:19 +0000

Image 1. A matriarch leads her family across the dry lake bed in Amboseli, Kenya. Such a movement is typically preceded by single and chorused Let’s go and Cadenced rumbles as individuals initiate and negotiate a plan of action. Image Credit: ElephantVoices . Key Takeaways Around 60% of elephant rumbles occur in overlapping choruses , yet these multi-caller exchanges have been far less studied than isolated calls. In a new study published in Scientific Reports and authored in collaboration with ElephantVoices , we analyzed elephant choruses as they naturally occur, without needing to separate each call . We could predict broad behavioral context from the choruses much better than chance. We also identified vocal convergence during some coordinated interactions, with some rumbles becoming progressively more acoustically similar as an exchange unfolded. Elephants face habitat loss and human-wildlife conflict across their range. Understanding how elephants communicate, make decisions, and coordinate their movements could help protect key habitats and corridors , ease human-elephant conflict , and support people and elephants living alongside one another. The Challenge of Studying Elephant Choruses In many social contexts, including when elephants greet each other after time apart, several animals may rumble at once. Some of those low-frequency, overlapping sounds even extend into the infrasonic range below human hearing. Roughly 60% of elephant rumbles occur inside choruses of two or more voices. Yet these exchanges have received far less scientific attention than isolated calls because they are difficult to analyze. That means a large part of elephants’ natural communication has not been studied. Instead of trying to pull each individual voice apart, we wanted to know whether we could predict behavior from choruses as a whole. In a new study published in Scientific Reports , working with ElephantVoices and Amboseli Trust for Elephants , we used machine learning to extract behavioral information directly from complex, overlapping elephant choruses. Listening to the Whole Chorus We analyzed 903 rumbles from adult females recorded between 1986 and 2022. The recordings come from one of the most closely studied elephant populations in the world. Since the 1970s, researchers from the Amboseli Elephant Research Project have followed these families across generations…

Matthew Schwartz lets Claude find "Claude-shaped" problems, yielding the BootLoops toolkit

Thu, 01 Oct 2026 14:02:00 +0000

Science Summary: In this guest post, Prof. Matthew Schwartz returns to describe a new approach to AI-accelerated science. In Vibe Physics, Schwartz discussed similarities in capability between Claude and a physics graduate student. Here, he describes what happened when he stopped fighting Claude and allowed Claude to find “Claude-shaped” problems: ones best suited to the capabilities of the current generation of LLM tools. This led him to build BootLoops, a toolkit for exact calculations in quantitative science. Because similar calculations often turn up across very disparate areas of science, Claude found connections to ecology, population genetics, and a dozen other fields. These connections were often technically correct but scientifically unremarkable at first, so Schwartz worked with domain experts to steer BootLoops toward questions those fields care about. Below, we share more about these projects and how BootLoops came about. Agentic AI is improving rapidly. Everyone notices the models seem smarter: they know more, make fewer mistakes, and have better ideas. If you follow the trend lines, it is easy to speak confidently about the potential of AI to revolutionize science. However, academic scientists trying to use the models today in their own work often feel a disconnect. The models may be solving challenging and longstanding problems, but so far these have mostly been well-scoped applications of existing techniques. Many of the headlines seem to be in mathematics, the one part of science where a problem can be stated completely and an answer checked absolutely. But most of science is not like that. And for many of us researchers and students, the distance between those headlines and what happens when we use these models can leave us frustrated and anxious. The core conflict, as I see it, is that although these models are brilliant, working like a human scientist is not what current LLMs do best. Claude and GPT are good at science, but they are not scientists: yes, they are smart, but it can take a lot of hand-holding to get them to produce anything of scientific value. Physicists would call this an “impedance mismatch”: two systems that each work fine but are poorly matched, so most of what one puts in never gets through to…

Google DeepMind publishes SynthID Bio, a watermarking method for AI-designed proteins

Thu, 01 Oct 2026 12:20:00 +0000

In what can be called a major breakthrough for synthetic biology and AI-powered biosecurity, Google DeepMind, Google’s AI lab, has developed a new method to watermark AI-designed proteins, establishing a permanent proof of origin directly inside biological code. Published in the journal Nature, the proof-of-concept technology, which is named SynthID Bio, solves a long-standing technical challenge: embedding an non-noticeable, verifiable digital identifier into biological sequences and predicted molecular structures without degrading their physical real-world utility in laboratory tests. The rollout comes amid rapid advancements in biological machine learning, where systems like AlphaFold 3, AlphaProteo and ProteinMPNN allow scientists to predict complex folding patterns and engineer functional binders. Yet these generative capabilities have heightened global biosecurity risks. Unfamiliar, machine-generated sequences can slip past conventional gene-synthesis filters, while unverified synthetic structural data risks contaminating open-access scientific repositories. ‘An urgent challenge for the AI era’ Google DeepMind Chief AI Scientist Demis Hassabis emphasised that guarding against biological misuse must be treated as a frontline safety priority for frontier AI: “Biosecurity is one of the most urgent challenges for the AI era. Bringing SynthID to biology so AI-generated proteins can be watermarked is a critical step - and we’re open sourcing SynthID Bio tools so the research community can build on this work. Published in @Nature today, congrats to the team!” Pushmeet Kohli, Vice President of Science at Google DeepMind and Chief Scientist at Google Cloud, celebrated the practical synthesis of these molecules: “Very happy to announce that our team at Google DeepMind has pushed the boundaries of generative biology, achieving the successful synthesis of AI-designed proteins that are both functional and watermarked. This proof-of-concept watermarking of the building blocks of life is enabled by SynthID Bio, our new protein watermarking method. It is designed to safeguard the new era of AI-powered generative biology and strengthen global biosecurity.” How SynthID Bio operates SynthID Bio functions as a modular toolkit tailored across two primary dimensions of structural biology: SynthIDBio-sequence that subtly steers the selection of amino acid building blocks du…

Introducing SynthID Bio: Google DeepMind Puts Watermarks Inside AI-Designed Proteins

Thu, 01 Oct 2026 11:14:58 +0000

Introducing SynthID Bio: Google DeepMind Puts Watermarks Inside AI-Designed Proteins Google DeepMind is introducing SynthID Bio after laboratory tests produced the first watermarked protein binders that retained their intended biological function. The September 30 announcement moves AI watermarking beyond images, text, audio, and video. Its signature can remain detectable in a synthesized physical protein, not merely in a digital design file. That makes the work more consequential than another content-labeling experiment. The central conflict is provenance versus control. SynthID Bio can identify outputs from participating models, but it cannot establish that every unmarked protein is natural or safe. Researchers also found that determined users can remove its sequence watermark by redesigning the protein. The result is a credible proof of concept with a demanding path to deployment. It pressures model developers, biological databases, and DNA synthesis providers to decide whether shared provenance standards belong in the infrastructure of AI-assisted biology. Introducing SynthID Bio as a Physical Provenance Test Google DeepMind has shown that an AI-generated protein can carry a detectable signature without losing the function researchers designed it to perform. SynthID Bio is a family of methods for watermarking protein sequences and predicted biomolecular structures. A watermark is a hidden statistical signal that authorized software can detect later. It indicates an output came from a compatible AI system. The project has two distinct components. SynthIDBio-sequence influences which amino acids a sequence-generation model selects. SynthIDBio-structure introduces small, detectable patterns into the atomic coordinates predicted by a folding model. That distinction matters because a protein sequence and a predicted structure represent different things. The sequence specifies an ordered chain of amino acids. The structure describes how atoms in that chain are arranged in three dimensions. For the sequence test, researchers combined AlphaProteo-designed backbones with a modified version of ProteinMPNN. ProteinMPNN generates amino acid sequences expected to fold into a supplied protein structure. The team evaluated binders against three targets: VEGF-A, PD-L1, and the receptor-binding domain of the SARS-CoV-2 spike protein. Protein binders are molecules design…

Google DeepMind develops invisible watermarks for AI-designed proteins

Thu, 01 Oct 2026 07:00:00 +0000

October 1, 2026 report Google DeepMind develops invisible watermarks for AI-designed proteins Paul Arnold Author Gaby Clark Scientific Editor Robert Egan Senior Editor Watermarks have long protected everything from bank notes and fine art to digital photographs and software, helping prove authenticity and trace an object's origin. Their main function is to protect copyright and verify authenticity. That same traceability is needed in synthetic biology, where AI is now a useful research tool. To address this, Google DeepMind has introduced SynthIDBio, a method for embedding invisible signatures directly into biological sequences and 3D structures without interfering with their function. AI is actively designing new functional proteins and predicting their three-dimensional shapes. But along with the benefits come a host of potential problems. These include biosecurity risks, such as the potential misuse of AI-designed biological molecules, and the spread of fake or misleading scientific data. The team released details of its protein watermarking in a paper published in the journal Nature. How to watermark a protein There are two kinds of watermarks, digital patterns built directly into the protein design data. Which one is used depends on what the AI is generating. If the model is writing a protein sequence, the watermark is a secret code hidden in the order of the amino acids. If the model is predicting a 3D biomolecular structure, the watermark is an invisible pattern hidden in the 3D coordinates of the atoms. Then came the jump from computer models to the real world. Researchers made watermarked protein binders in the lab to target three specific molecules. These included part of the coronavirus spike protein, a human protein involved in blood vessel growth and a human protein involved in regulating immune responses. They then needed to find out two things. The first was whether the watermarks changed how the proteins work, and the second was whether a detector could spot them. The watermarked proteins attached to all three targets with comparable strength to nonwatermarked versions. Meanwhile, the detection tool correctly identified all of the watermarked protein sequences and more than 99.8% of the watermarked 3D structures. Adding the watermarks did not significantly affect how well the proteins worked or the accuracy of the 3D structures. There are st…

Keyword Harnesses Fail Open: A Cheap Diagnostic Ladder for Tool-Use Claims in Small Language Models

Thu, 01 Oct 2026 00:00:00 +0000

Keyword-matching benchmarks can credit small models for tool use they never perform. We document such a false positive in a matched-architecture pair of Spanish security language models and propose a ladder of strict, cheap diagnostics. A 661.6M parameter model (approx. 65% code/technical text; no dedicated SFT) and a 1,109M model (web-heavy multi-phase curriculum; 6B-token tool-SFT) share decoder, tokenizer, and special tokens, scoring almost identically on lenient tool-use metrics (B4: 0.660 vs. 0.650). Verbatim-reproduction checks on training examples separate them completely: the 600M emits valid tool calls with generalized arguments on 6/6 examples; the 1B does so on 0/6 across checkpoints. A first-token probe localizes the 1B's failure to a missing prior (prob. 10^{-4}--10^{-5} on < |tool_call|>), which was erased by its web-heavy training phase. A targeted SFT recipe (diverse corpus, 5x higher learning rate, 2,202 steps, ~3.3 GPU-hours) repairs the 1B using three orders of magnitude fewer tokens than the failed phase. On all 269 corpus rows, valid emission rises from 0.100 to 0.959 (600M: 0.926). On 238 unseen prompts, the repaired 1B passes 0.536 vs. the 600M's 0.428 (p = 0.004). Embedding-drift checks show the repair did not move the trigger token's tied embedding (97.7% of the bf16 table remains bit-identical), meaning changes live in the surrounding network. Both models over-trigger, rarely answering negative prompts without a call (0.09 for 600M, 0.17 for repaired 1B). Factorial analyses confirm all repair configurations install the format, though suppression benefits from a diverse corpus remain a hypothesis due to seed sensitivity. This cheap diagnostic ladder costs minutes of CPU time and should gate tool-use claims on small models.

World Action Modeling with Progressive Visual Planning

Thu, 01 Oct 2026 00:00:00 +0000

World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an initial observation and instruction. However, existing WAMs struggle with long-horizon prediction, as generating dense video rollouts is highly inefficient. Some recent WAMs address this by predicting a single future frame without generating the full video, but this approach neglects how to progress toward the goal. We present ProWAM, a progressive world action model that jointly predicts actions and an ordered sequence of sparse visual sub-goals, providing explicit visual guidance to anchor action generation throughout task execution. This design scales naturally, as sub-goal prediction can be learned from large-scale action-free videos, allowing the video backbone to offload complex visual planning from the action policy. For efficient action generation, ProWAM executes a single video-backbone forward pass to cache sparse sub-goal features, eliminating iterative full-video generation and requiring only lightweight action denoising during replanning. Across extensive evaluations, ProWAM achieves superior out-of-distribution robustness. On simulation benchmarks, it sets new state-of-the-art results on LIBERO-Plus (85.8%) and randomized RoboTwin (75.7%), outperforming the strongest baseline with relative gains of up to +35.9%. On RoboCasa365, ProWAM achieves a 48.1% success rate and 18.2% on the challenging Composite-Unseen split, ranking 4th overall. Crucially, in zero-shot real-world experiments, ProWAM achieves 70.0% success, outperforming the strongest baseline by +15.0 (from 55.0% to 70.0%, a +27.3% relative gain) in novel scenes. These results demonstrate the value of progress-indexed visual foresight for closed-loop control. Our program is in https://sii-ferenas.github.io/ProWAM-page.

From Retrieval to Typed Decisions: Calibrated System One Models from Biomedical Sentence Encoders

Thu, 01 Oct 2026 00:00:00 +0000

Typed decision models answer schema-constrained questions about a text in one forward pass and return probabilities meant to be thresholded. We ask whether biomedical sentence encoders trained for retrieval are good starting points for such models. We present SBERT2S1, which converts Sentence-Transformers encoders into bi-encoder, cross-head (C) and prior-fused residual (PFR) decision models, together with BIODECIDE, a biomedical typed-decision suite, and MEDLINE-S1, 243k training decisions derived from NLM indexing. Across six parent-retriever pairs, retrieval training improves zero-shot matching of content-bearing options. After fine-tuning, its effect depends on the head: across five pairs and three training-set sizes, retrieval training significantly helps PFR, which keeps the retrieval prior, in 10 of 15 comparisons, but helps C in one and hurts it in five. A matched grid of two heads and five training objectives shows that C outperforms PFR under every objective, and that the released RLCD recipe of open System One models trails cross-entropy by 2.5-3.0 points. The deficit stems mainly from its reward normalisation, which inflates the noisy score-function term 3.6-15-fold; an unbiased leave-one-out estimator recovers most of the gap. After temperature scaling, no objective is clearly better calibrated than cross-entropy. We release the code, the MEDLINE-S1 labels and a model.

Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Models

Thu, 01 Oct 2026 00:00:00 +0000

In this paper, we study how to achieve one-step action generation in Robotic Foundation Models (RFMs), aiming to overcome the high inference latency of multi-step flow matching. MeanFlow provides a promising framework for this goal, yet its direct application leads to performance collapse. We discover that this stems from two distinctive dynamics exhibited in the RFM velocity field: (1) the ``local acceleration" exhibits stability early on, but surges sharply towards the end of the denoising process, and (2) the spread of its magnitudes across samples widens as denoising progresses. To address these issues, we introduce Kinematic MeanFlow (K-MF), a novel one-step action policy tailored for RFMs. Specifically, grounded in a kinematic identity, K-MF decouples the time derivative term in the MeanFlow formulation into two sub-interval terms separated by an intermediate point. This decoupled formulation enables the two terms to capture early-stage and late-stage denoising dynamics, respectively, while mitigating the error amplification across the process. As a result, our K-MF empowers RFMs to achieve one-step action generation in both training from scratch and fine-tuning paradigms across diverse tasks, while outperforming multi-step flow matching in most settings. In terms of inference efficiency, K-MF reduces action-head latency of GR00T-N1.6 by 67.5%~74.4% across L40 and Jetson Orin in eager and compiled modes, yielding end-to-end latency reductions of 30.3%~54.9%. Code will be available at https://github.com/IntelChina-AI/K-MF.

iADD: Improving Alignment and Diversity in Diffusion Policy Optimization

Thu, 01 Oct 2026 00:00:00 +0000

Reinforcement learning based post training of diffusion models, such as Denoising Diffusion Policy Optimization (DDPO), optimizes a reverse diffusion process under a reward function. However, current approaches to reward optimizations do so at the cost of diversity and quality. In this paper, we provide better tradeoffs through careful theoretical considerations and method design. We analyze the theoretical framework and mathematically demonstrate that only-latter timestep updates of diffusion model may be harmful for diversity contrary to the conclusions presented in a previous work. Additionally, we propose an incremental Feynman-Kac training based on strong theoretical foundations in order to achieve the best-yet alignment-diversity tradeoffs. We perform extensive experiments and compare our method against related diffusion policy optimization approaches in three different tasks and also provide strong ablations for each component, thus validating strong performance gains in both alignment and diversity.

Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems

Thu, 01 Oct 2026 00:00:00 +0000

Scientific progress emerges from a longitudinal ecosystem in which researchers, institutions, funding agencies, collaboration networks, and the scientific literature co-evolve. As AI becomes increasingly involved throughout the scientific research cycle, understanding these interconnected and evolving processes becomes increasingly important. We introduce SciUtopia, a persistent, closed-loop LLM-agent simulation framework for studying academic research ecosystems. SciUtopia models interconnected scientific processes such as research-direction choice, collaboration, submission, peer review, resubmission, citation, funding, and researcher attrition, while maintaining evolving states across simulated years. Its configurable institutional mechanisms and information channels provide a controlled testbed for matched counterfactual experiments and targeted interventions. Across 61 simulation worlds, SciUtopia simulates over 40,000 researchers from 8,000 institutions, producing around 400,000 publication decisions and 1.2 million LLM-generated peer reviews. Using these longitudinal simulations, we find that rejection-driven resubmission substantially amplifies reviewer burden beyond population growth alone, cautious exploration balances citation impact with career success and long-term topic diversity, and resource inequality can emerge even without detectable cumulative advantage from narrowly winning early funding. Code is available at https://github.com/Ahren09/ScienceUtopia.

DeskForge: Dense Supervision from Desktop Environments for Computer-Use Agents

Thu, 01 Oct 2026 00:00:00 +0000

Computer-use agents need to reliably ground action targets in complex desktop scenes, where multiple applications, overlapping windows, and visually similar controls compete for attention. Existing training data rarely pair such scenes with dense annotations or vary them in a controlled way. We introduce DeskForge, a controllable desktop environment that composes and explores real applications to generate large-scale supervision for computer-use agents. It varies application states, content, window layout, appearance, and resolution, and fuses screenshots, accessibility trees, and window geometry into dense element annotations while recording the outcome of each executed action. Using this environment, we construct DeskForge-1M, a corpus of 1.2M annotated desktop observations containing 159.7M element instances. We fine-tune four vision-language models on 200K grounding examples drawn from DeskForge-1M. All four improve across held-out desktop conditions and on all five external GUI grounding benchmarks; for Qwen3.5-4B, accuracy increases by 11.51 percentage points on ScreenSpot-Pro and 10.11 points on OSWorld-G. The gains also translate to long-horizon task completion: under a fixed planner, the fine-tuned action models solve more WebArena-Infinity and OpenApps tasks, with Qwen3.5-4B increasing from 31 to 50 of 119 tasks and from 3 to 15 of 100 tasks, respectively. These results show that controllable composition of real desktop environments provides a scalable source of supervision for improving both GUI grounding and long-horizon computer use. The framework code, the dataset, and the fine-tuned model are available from the project page: https://saidgurbuz.github.io/deskforge/

ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

Thu, 01 Oct 2026 00:00:00 +0000

What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-growing archive of research, a new problem needs. To study this skill, we draw on researchers who know firsthand which earlier work advanced their completed projects, with papers serving as pointers to the ideas within. Using our automated pipeline that makes author annotation scalable, we build ScholarCatalyst by having 184 lead authors of 207 recent computer science papers label which candidates did or could have advanced their project, each with a detailed rationale. We introduce a retrieval task with author-provided judgments: given an initial research question, retrieve these papers from only the literature available when the project began. Agentic search does no better than embedding retrieval (0.42 vs. 0.48 Recall@20) despite calling that same retriever as a tool. Even an agent built on Claude Fable 5.1, which may have seen the completed papers during training, reaches only 0.51 R@20. These results highlight the need for new training recipes that equip models with expert intuition for searching broad corpora. We envision ScholarCatalyst as a step toward scientific agents that can take a half-formed idea and point to the prior research it needs.

VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks

Thu, 01 Oct 2026 00:00:00 +0000

As LLM agents undertake increasingly complex, long-horizon tasks, verifying their outputs becomes increasingly challenging. We study how verification capability can be strengthened with a fixed base model, without access to reference answers or grading rubrics at test time. Repeated sampling yields multiple rollouts that can contain complementary correct claims, but we need a reliable verification mechanism to determine which claims to trust. We first find that disagreement often exposes correct alternatives, while consensus can conceal errors. These observations motivate VeriHarness, which turns the underlying LLM a generator uses into an agentic verifier by giving it a workspace, evidence tools, and reusable verification skills. A disagreement resolver checks competing claims against environmental evidence, while a consensus challenger tests shared claims and searches for omitted requirements. Their findings guide the selection and revision of the final artifact. Across five long-horizon workspace benchmarks and two frontier models, VeriHarness achieves the highest selection scores among the evaluated baselines. Evidence-backed revision further improves average performance, bringing gains over a single rollout to 6.2 points with Gemini 3.5 Flash and 6.4 points with Claude Opus 4.8. We further show that verification skills can self-improve from failure feedback, demonstrating VeriHarness as a novel and critical approach for scaling long-horizon agentic verification. We release the full pool of approximately 26,000 rollouts across all five benchmarks and both models, produced at a cost of over $100,000, to support future research on agentic verification.

Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes

Thu, 01 Oct 2026 00:00:00 +0000

On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a frozen or EMA version of themselves that receives privileged information. Its application to multimodal large language models (MLLMs), however, remains largely unexplored. Recent approaches use privileged visual information, such as image crops corresponding to a question, to improve fine-grained perception, but their gains are confined to tasks that benefit from such visual zooming and require either human-annotated grounding data or external teacher models. We introduce a different form of on-policy self-distillation for MLLMs that provides the teacher with textual, spatially grounded guidance identifying the visual elements relevant to a query. We use procedurally generated scenes with automatically available object identities and spatial coordinates, enabling scalable and annotation-free post-training. The teacher uses this spatial guidance to locate and integrate evidence from multiple relevant image regions, while the student learns to reproduce the resulting behavior from the image and question alone. Our approach consistently improves performance on counting, document and chart understanding benchmarks across multiple models. Importantly, although post-training uses only synthetic scenes, the resulting improvements transfer to real-world perception benchmarks, yielding a 3.23-point gain in average performance across CVBench, V*, ZoomBench, BLINK, HR-Bench, and MME-RealWorld. These results show that spatially grounded privileged information can induce broader perceptual capabilities through on-policy self-distillation, enabling substantial synthetic-to-real transfer beyond the task and data distribution used for post-training. Project page: https://github.com/sirkosophia/Where-OPD

Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation

Thu, 01 Oct 2026 00:00:00 +0000

Video generation models are increasingly being explored as world simulators for embodied planning and learning. To do so effectively, these models must not only generate visually appealing frames, but also predict how environments dynamically evolve when executing goal-directed actions. While evaluating these capabilities is crucial, existing benchmarks focus mainly on single short actions or step-by-step instructions. This leaves multi-step physical reasoning underexplored, especially in egocentric video generation that requires planning to simulate proper execution to accomplish high-level goals by carrying out multiple real-world manipulations. We introduce Ego2Act, a goal-directed benchmark featuring 2,640 videos from 110 real-world tasks across day-to-day settings, varying object clutter and multi-step complexity. Given an initial scene image and a high-level goal, Ego2Act evaluates whether video generation models can produce realistic egocentric videos of a hand manipulating objects to carry out the task. To support scalable evaluation, we also introduce Ego2ActJudge, a reference-free evaluation pipeline that achieves better task completion and physics plausibility evaluation alignment with human consensus compared to relevant baselines. Our findings reveal that models' generated simulations often skip or partially execute steps, leaving later steps missing dependent states, which leads to unfulfilled goal. Furthermore, models consistently fail at fine-grained physical dynamics, particularly during complex object manipulation and persistent world modeling. We hope Ego2Act provides a rigorous testbed for advancing video models toward physically plausible, goal-directed simulation.

A multimodal synthetic 3D point cloud dataset of shoe uppers for robotic processing boundary segmentation

Thu, 01 Oct 2026 00:00:00 +0000

Abstract Robotic processing of shoe uppers requires accurate segmentation of thin processing boundaries in 3D point clouds. However, acquiring and annotating large collections of real-world scans is costly. We present a multimodal synthetic 3D point cloud dataset of shoe uppers for robotic processing boundary segmentation. The dataset contains 1,067 samples, including 1,000 synthetic point clouds and 67 real-world test scans for independent evaluation. The synthetic data generation pipeline includes texture and appearance augmentation, region-aware geometric deformation, and sensor degradation simulation. Both branches provide three-dimensional coordinates, RGB attributes, and pointwise binary processing boundary labels. The synthetic branch additionally includes panel identifiers, while the real-world labels were manually refined. Technical validation with standard point cloud segmentation backbones assesses dataset usability under a controlled synthetic-to-real evaluation protocol. The released point clouds form a complete, directly usable dataset. The accompanying code and assets support regeneration and adaptation of synthetic subsets.

Forecasting space weather risks on power grids

Wed, 30 Sep 2026 16:00:00 +0000

At a glance End-to-end forecasting: A machine learning pipeline uses forecast-time solar-wind information to generate location-specific risk estimates for 66,935 substations in the continental United States. Physics and place: The system combines Auroral Electrojet (AE) and Disturbance Storm Time (Dst) forecasts with local latitude, geology, and ground conductivity. Advance warning: The pipeline detected nearly 80% of major space-weather events during the evaluation period and can warn grid operators 30 to 60 minutes before a specific risk appears. Forecasting a threat to critical infrastructure During the May 2024 geomagnetic storm, utilities across North America prepared for possible impacts as auroras extended far beyond their usual range. The storm would degrade GPS accuracy and satellite operations, impacting farm operations in North America and Europe. Modern society depends on reliable electric power, yet extreme space-weather events can induce currents in transmission networks that damage equipment and increase operational risk. The challenge is not only knowing that a storm is approaching, but estimating when and where its effects could be most severe with enough warning for grid operators to respond. During my summer internship at Microsoft Research, I developed a machine learning system that forecasts space-weather risk across 66,935 substations in the continental United States. The system measures how the sun’s activity can affect the earth’s magnetic field and, ultimately, the power grid. It combines solar-wind observations, forecasts of the Auroral Electrojet (AE) and Disturbance Storm Time (Dst) indices, physics-informed constraints, local geological conductivity, and grid-infrastructure data to produce location-specific risk estimates 30-60 minutes ahead of potential impact. Building an end-to-end prediction pipeline Space-weather prediction is difficult because it spans several coupled systems. The solar wind changes rapidly, its interaction with Earth’s magnetosphere is irregular, and the resulting ground effects depend on local conditions. Regions with resistive bedrock can experience stronger geomagnetically induced currents (GICs) than regions with more conductive geology. Transmission-line orientation, latitude, and other power-system characteristics further influence the exposure of individual assets. The pipeline addresses this compl…

Google DeepMind Introduces SynthID Bio for Watermarking AI-Designed Proteins

Wed, 30 Sep 2026 15:03:07 +0000

Introducing SynthID Bio Proof of concept for watermarking AI-generated proteins while preserving biological function. Today, we’re introducing SynthID Bio to bring watermarking technology to synthetic biology. SynthID Bio embeds an imperceptible signature directly into the biological code, ensuring the watermark is verifiable not just on a digital model but on the synthesized, physical protein itself – all while preserving its biological function in laboratory testing. Generative AI is helping scientists address critical biological challenges, from predicting the structure of proteins (AlphaFold) to designing entirely new proteins (AlphaProteo, and ProteinMPNN), and more recently, developing new bacteriophages, viruses that infect bacteria. Yet these tools also present new challenges: novel AI designs can bypass traditional DNA synthesis screening, while mislabeled synthetic 3D structures risk polluting public databases and misleading downstream research. How SynthID Bio works SynthID Bio is a family of watermarking methods developed specifically for synthetic biology to strengthen biosecurity and scientific integrity. It adapts its approach depending on the type of data, subtly guiding the choice of amino acids for sequences and adjusting atomic coordinates for predicted 3D structures, creating a reliable signal for detection. In experiments, these adjustments did not compromise the protein’s biological function, which is essential to effectively treat disease and advance scientific research. We verified our approach for watermarking protein binders, i.e. molecules built to selectively latch onto other proteins, by using our binder design method AlphaProteo alongside a SynthID Bio-enabled version of ProteinMPNN, the commonly used protein sequence generation method. In wet-lab testing across three target proteins (VEGF-A, the SARS-CoV-2 spike protein RBD, and PD-L1), our watermarked designs matched the hit rate, binding affinity, and natural sequence diversity of unwatermarked versions, successfully creating the first-ever watermarked and biologically functional protein binders. For protein folding, SynthID Bio fine-tunes a small part of AlphaFold 3’s diffusion network, building the ability to watermark directly into the model’s weights. This ensures that the predicted 3D coordinates inherently carry a detectable signature regardless of who runs the model.…

OpenAI's Noam Brown on multi-agent systems: 10,000 agents deserve at most 10% of the credit for the Millennium Prize breakthrough

Wed, 30 Sep 2026 14:03:43 +0000

Noam Brown, an OpenAI researcher and core author of o1, discussed his current focus, multi-agent systems, in a recent podcast. He said OpenAI previously used 10,000 agents over 88 hours and 130 billion tokens to solve a Millennium Prize mathematics problem. Brown was explicit that the result came from having a general, powerful model, and that he would not even attribute 10% of the credit to multi-agent methods, which he feels have received disproportionate praise because they are eye-catching. Multi-agent systems simply scale test-time compute in parallel rather than serially, at some efficiency loss since agents do not share full context. His testing reached only 16 agents: four agents solve a problem about twice as fast for roughly double the cost, and 16 still improve performance with slightly lower efficiency. Speedups are somewhat sublinear and domain-dependent, and ablation experiments at 1,000-agent scale would be unaffordable.

An ancient evolutionary origin for IL-1 cytokines

Wed, 30 Sep 2026 00:00:00 +0000

Inflammation is widely considered a hallmark of animal immune responses. In mammals, this process is mediated in part by pro-inflammatory cytokines of the interleukin 1 (IL-1) superfamily, which establish and support microenvironments that regulate immune cell activity. However, the evolutionary origins of IL-1 cytokines remain elusive, as homologous proteins have been difficult to confidently identify outside vertebrates using sequence-based approaches. Although IL-1 family members have been described in all vertebrate lineages, including cartilaginous and jawless fishes, their distribution and evolutionary relationships beyond these groups remain unclear. Here we applied protein structure prediction algorithms to identify genes encoding a previously unrecognized family of IL-1 proteins (termed 'IL-1anc' to reflect their ancestral evolutionary origin) with homologues throughout Eumetazoa. Here, using Petromyzon marinus (sea lamprey) and Strongylocentrotus purpuratus (purple sea urchin) as model systems, we demonstrate that IL-1anc proteins share key features with mammalian IL-1α/IL-1β, including expression patterns, subcellular localization and post-translational processing. Together with previous evidence identifying other IL-1 axis components in invertebrates, our data support a model in which the IL-1 superfamily and associated circuitry represent a foundational module of animal immunity that far predates the jawed vertebrates.

Video Generation Models: A Survey of Post-Training and Alignment

Wed, 30 Sep 2026 00:00:00 +0000

Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamics. Despite strong generative priors learned through large-scale pretraining, pretrained video models often fail to reliably follow human intent, maintain temporal coherence, or satisfy physical and safety constraints. Compared with image and text generation, alignment in video generation presents unique challenges, including error accumulation over time, motion-appearance coupling, multi-objective trade-offs, and limited supervision for temporal properties. These challenges motivate systematic post-training strategies that adapt pretrained models without retraining them from scratch. In this survey, we present the first comprehensive review of post-training and alignment in video generation models. We frame post-training as a unifying framework and distinguish between implicit alignment and explicit alignment based on how alignment signals are enforced. From this perspective, we organize existing approaches into four broad categories: supervised fine-tuning methods, self-training and distillation methods, preference- and reward-based methods, and inference-time methods. This taxonomy provides a coherent view of how alignment signals shape model behavior across both training and deployment. Beyond methodological advances, we review commonly used datasets, benchmarks, and evaluation practices, and discuss open challenges such as scalable reward design, long-horizon temporal consistency, stability-expressiveness trade-offs, and safety-aware generation. This survey aims to provide a structured conceptual foundation and practical guidance for advancing controllable and reliable video generation models.

CytoVI: deep generative modeling of antibody-based single cell data

Wed, 30 Sep 2026 00:00:00 +0000

Antibody-based single-cell technologies, such as flow cytometry, mass cytometry and CITE-seq, have become widely used in clinical diagnostics and basic research; however, their analysis is complicated by technical noise, batch effects, platform differences and restricted antibody panels. Here we present CytoVI, a probabilistic generative model for statistically rigorous unified analysis of antibody-based single-cell data. CytoVI generates informative cell embeddings, imputes missing measurements, performs differential protein expression testing and automates annotation of cells in a single probabilistic model. We applied CytoVI to build an integrated B cell maturation atlas spanning 350 proteins and identified proteins associated with immunoglobulin class-switching. In a cohort of patients with B cell non-Hodgkin lymphoma profiled by flow cytometry and CITE-seq, CytoVI uncovered disease-associated T cell states. CytoVI is available as open-source software at scvi-tools.org .

LexReward: A Taxonomy-Driven Reward Framework for Legal Language Models

Wed, 30 Sep 2026 00:00:00 +0000

Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing reward methods, however, often rely on coarse-grained holistic judgments, providing limited domain specificity and interpretability. We introduce LexReward, a taxonomy-driven framework for legal reward modeling. LexReward characterizes legal response quality along three complementary dimensions: Style, covering lexical and syntactic quality; Element, assessing legal subjects, facts, statutes, and decisions; and Chain, evaluating the order, completeness, correctness, and non-redundancy of legal reasoning. For each dimension, we develop rubrics that specify evaluation criteria and quality levels. The resulting rewards are used to construct pairwise preference data for Direct Preference Optimization (DPO) and reward-model training. Experiments show that the rubric-based rewards reliably distinguish legal responses of different quality and that DPO training on the preference data improves performance across all three dimensions. The learned reward models, LexRM, also support effective downstream optimization: each dimension-specific reward model improves policy performance in its corresponding dimension through reinforcement learning, without requiring reference answers at reward time. Dimension-wise analyses further support the effectiveness of the proposed taxonomy and reward construction.

Does Learning Protein Folding Generalize to Broader Reasoning?

Wed, 30 Sep 2026 00:00:00 +0000

Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natural testbed, because one solved structure yields thousands of exactly checkable spatial and topological statements. We ask: can learning to fold proteins teach general models reusable reasoning capabilities? To answer this, we build FoldingCorpus, a protein-derived question-answer dataset, and Fold2Reason, a recipe that post-trains on it through two complementary signals: discrete structural answers predicted via the model's native language head, and continuous 3D geometry decoded from the same shared representations. On FoldBench, Fold2Reason achieves structure prediction scores 2.7 to 3.5 times those of Qwen3.5-9B. Beyond protein structure prediction, it improves performance on all 10 benchmarks spanning spatial, graph, scientific, and general reasoning, raising macro-average accuracy from 45.09% to 48.33% (+3.23 pp), with positive gains on all 10 benchmarks, while matched controls built from random, synthetic, and shuffled structure yield substantially smaller or negative gains. Our work shows that non-linguistic, structure-dense scientific data can systematically improve broad reasoning in language models, making a solved scientific problem a practical source of post-training supervision.

Galahad's Byte-Exact Memory Turns LLM Reading into a One-Time Cost

Wed, 30 Sep 2026 00:00:00 +0000

A transformer language model performs a bounded amount of computation per token, and recent work by Vishal Sikka, former CEO of Infosys, argues that this bound limits which tasks a model can carry out or verify (arXiv:2507.07505). We ask how much of the budget beneath that ceiling is spent on work the model has already done. Serving is stateless across requests: a model that answers a second question about a document recomputes the document's attention state from the first token. On seven real-world datasets, 98.7% of prompt tokens were text the model had already read. We present Galahad, a memory layer for vLLM, SGLang and llama.cpp that makes this reading a one-time cost. Taliesin saves the model's key-value (KV) state for a block of text and loads it on the next request that contains the same bytes, instead of recomputing it. Blaise keeps the documents themselves and passes the model only the section a question needs. On a recall test with 100 facts hidden in a 97,000-token corpus (Gemma 4 31B), Taliesin alone let the model attend to the whole corpus and answered 98 of 100 on llama.cpp at 3.0 s and 572 J per question, against 10 of 100, 9.3 s and 2,754 J for the same model without Galahad, which could hold only the last 12,000 tokens. With Blaise added, the model read about 668 tokens per question and answered 100 of 100 on all three runtimes at 0.59-0.64 s and 200-213 J; a tuned RAGFlow pipeline answered 77. Storing the corpus is a one-time cost of about 100 s and 28 kJ, whose energy is recovered after 13 questions. Restored state is bit-identical: all 262,144 output logits matched after restart, rehydration and hot-load. Galahad worked with all 30 models we tested under vLLM, and it fails closed: any load that does not pass its checks is recomputed. Together these results move LLM serving from stateless to stateful inference.

OSWorld-Science: A Benchmark of Computer Use Agents for Learning and Using Scientific Software

Wed, 30 Sep 2026 00:00:00 +0000

Scientific software presents a demanding test for computer-using agents based on visual language models (VLMs): completing a research workflow requires interpreting specialized interfaces, manipulating scientific objects, and producing verifiable results. We thus introduce OSWorld-Science, a benchmark and evaluation environment that combines scientifically meaningful tasks, artifact-based evaluation, and an efficient agent harness for studying computer use in the scientific domain. The benchmark contains 12 VLMs and 146 high-quality tasks across several scientific domains and software configurations, covering workflows such as molecular drawing and retrosynthesis, pathology image analysis, statistical computing, and physical simulation. Tasks are developed through expert proposals and iterative human--AI co-design, with selection guided by scientific value and difficulty. Task-specific execution-based evaluators inspect application states and generated artifacts, including molecular structures, segmentation masks, plots, and numerical results, and award partial credit for incomplete outcomes. Our special harness integrates model adapters, interaction-loop control, and trajectory logging to support comparisons of models and interaction strategies. Our results show that current state-of-the-art VLMs with a strong harness still face challenges in addressing key questions in the scientific domains. We also analyze the benchmarking results across multi-linguistics, reasoning efforts, context length and other factors and derive several important conclusions and directions to assist future development. Overall, we provide an integrated framework connecting expert-defined scientific goals to verifiable software outcomes, enabling systematic evaluation of both agent capabilities and harness design in scientific workflows.

Learning Functional Subspaces for Neural Network Compression

Wed, 30 Sep 2026 00:00:00 +0000

Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keeping the matrices dense, and thus efficient on standard hardware. Existing methods, however, choose the subspace to remove from each weight matrix with local closed-form criteria: activation energy, layer-wise reconstruction error, or a quadratic approximation of the loss. These criteria ignore how errors propagate through the network, so at high compression the errors compound with depth and performance collapses. We introduce Learnable Subspace Projections (LSP), which instead learns the subspaces to discard end-to-end. Each linear layer, or tied group of layers that read the same activations, is assigned an orthogonal projector. All projectors are optimized jointly against a global objective--the KL divergence to the dense model's output distribution or the model's original training loss--while the pretrained weights remain frozen. Projectors are initialized from a whitened SVD truncation, and ranks are allocated by the output KL each projector induces per parameter saved. After training, the projectors merge into standard low-rank factors, with each tied group sharing one factor. In attention, this also lets the model cache one narrow latent in place of full keys and values. Across LLMs (OPT-125M/1.3B, Qwen3-4B, Llama-2-7B) and ViT-B/16, LSP outperforms baselines, and its advantage widens as compression increases. At -70% compression, LSP brings Llama-2-7B to 10.9 WikiText-2 perplexity and 42.2% mean zero-shot accuracy, versus 13.3 and 36.0% for the strongest baseline. The factorized model decodes up to 1.6x faster than the dense model at small batch sizes, and aching the shared latent shrinks the combined memory of weights and KV cache by 13.5x at a 128k-token context, versus at most 6.5x for untied baseline factorizations.

UniEvo-VL: An On-policy Self-Distillation Recipe for Multimodal Model Self-improvement

Wed, 30 Sep 2026 00:00:00 +0000

Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified capacity, we introduce UniEvo-VL, a self-evolving framework for multimodal models to learn from this constructive self-correction feedback during test-time compute. Instead of relying on a separate, often larger, teacher, we leverage their self-critiques as privileged information and ask a single multimodal model to act as both teacher and student with different contexts. The student only sees the vanilla question, while the teacher conditions on the privileged critique. Then training minimizes the per-state divergence between their denoising diffusion distributions over the student's own sampling trajectories. Experiments demonstrate that UniEvo-VL improves the image generation capabilities of multimodal models, while maintaining their sensitivity to additional reflection information. Specifically, we build on top of the open-source Qwen-image-2512 and observe a significant performance gain from 0.747 to 0.808 on GenEval and from 32.97 to 35.53 on GenEval2 Soft-TIFA. Moreover, attempts with more powerful external critics (e.g., GPT5.6-Luna) show that multimodal models with strong judge capabilities can anticipate a higher self-evolving ceiling. Last but not least, mixed text-rendering outcomes show that our self-improvements may not be uniform across different tasks. Our study aims to shed light on the current hot recursive self-improvement research line to enhance the user experience when using multimodal models without external supervision or guidance.

The Evolution of Attention in Large Language Models: Mechanisms, Trade-offs, and Emerging Trends

Wed, 30 Sep 2026 00:00:00 +0000

Self-attention gives LLMs fine-grained, query-dependent access to context, but dense token interactions incur quadratic prefill cost and a key--value cache growing with context length. Research thus spans explicit-memory compression, sparse access, recurrent state construction, structured state dynamics, and heterogeneous mechanism composition. This survey analyzes these developments as model-internal contextual memory. We introduce a five-dimensional lens---Memory Representation, Memory Update, Access, Readout, and Integration---describing what is represented, how it changes, what is query-eligible, how it is read, and how readouts form outputs. This lens compares overlapping research lines without imposing one computational model. We reconstruct mechanism-level developments and architectural adoption using 59 release-level records from 14 major model lineages and 11 high-performing open-weight endpoints. First, explicit-memory and recurrent-state methods retain distinct interfaces but increasingly control overlapping memory functions. Second, heterogeneous architectures increasingly coordinate across network depth: layer-wise composition distributes complementary memory processing across representational stages, while cross-layer reuse carries selected memory and routing artifacts forward. Depth thus becomes a dimension along which contextual memory is constructed and managed. Third, these developments motivate a stateful multidimensional memory-routing hypothesis: persistent memory is organized across temporal scope, network depth, substrate type, and representation granularity, while coordinated Sparse Write and Sparse Read determine what is maintained and what contributes to each query. Overall, efficient sequence architecture design increasingly concerns the organization, lifecycle, and selective use of contextual memory rather than an isolated Attention operator.

JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces

Wed, 30 Sep 2026 00:00:00 +0000

LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models offer fast probabilistic predictions over finite fields, but require those fields to be specified in advance. This requirement limits autonomous task solving, where the available actions must be derived from natural language instructions and adapted through interaction. We introduce JevSpawn, a compositional policy that connects natural language task specifications to finite probabilistic exploration. Parallel action spawning is coupled with feedback driven branch selection, representation revision, and recovery from retained alternatives. Shared action structure and model prefixes reduce repeated generation and context computation without additional training. Evaluations on eight benchmark tasks against seven agent baselines and a TypeSafe Jev variant establish JevSpawn as a promising approach to structured agentic inference, with improved task performance and faster navigation.

Introducing Quine: An AI research system designed for the complexity of biology

Tue, 29 Sep 2026 14:00:02 +0000

Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback, helping researchers sharpen future research directions.

AIhub monthly digest: September 2026 – tracking animal populations, recommender systems, and an interview with Ken Goldberg

Tue, 29 Sep 2026 10:36:34 +0000

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet AI pioneer Ken Goldberg, use AI techniques to track animal populations, investigate how to improve recommender systems, and find out how external knowledge is used in AI systems. Combining cultures, from code to canvas: an interview with Ken Goldberg In the latest instalment in our AI pioneers series , we spoke to award-winning roboticist, filmmaker, and artist, Ken Goldberg . We discussed the culture clash within robotics, what art and science have to learn from each other, and how AI will shape the future of art. Understanding animal behaviour and tracking populations Deep learning is a powerful tool for understanding animal behaviour and tracking populations. We caught up with Isla Duporge to find out about her work developing remote-sensing and AI methods to investigate animal movement in the wild. Optimising sensor placement for estimating wildlife populations Hannah Murray is also researching wildlife populations. In this interview , she told us more about work developing optimization methods that help ecologists determine where to place sensors, such as camera traps, to get precise estimates of species population counts from the data they collect. Improving the process for large-scale recommender systems In large-scale recommender systems, platforms often need to handle massive volumes of items at web latency. To meet this requirement, systems often employ a two-stage decision process. We heard from Haruka Kiyohara about her research trying to improve the first part of this decision process – an early-stage ranker, which filters up to billions of items cheaply into a smaller candidate set. How is external knowledge used in AI systems? We kicked off our series meeting the IJCAI-ECAI doctoral consortium participants meeting Yash Saxena . Many AI systems now look through external documents before answering a question. In his research, Yash follows what happens to that evidence as it moves through the system. How much can fair budget-division rules resist manipulation? Haris Aziz, Patrick Lederer and Jeremy Vollen won a distinguished paper award at IJCAI-ECAI for their work “Approximate Strategyproofness in Approval-based Budget Division”. In this blog post , they summarise their pape…

Microsoft Research Debuts Quine, a Multimodal World Model of Biology

Tue, 29 Sep 2026 07:00:00 +0000

AI Models & Platforms Microsoft Research Debuts Quine, a Multimodal World Model of Biology Add Unite.AI to your preferred sources on Google Microsoft Research on September 29, 2026, introduced Quine, a research effort built around a multimodal world model of biology and an interactive harness that links the model to orchestration and reasoning models, scientific tools, the research literature, the wet lab, and the teams of scientists using them. With the announcement, Microsoft opened applications for the first Quine Fellows cohort and reported that, working with researchers at the Broad Institute of MIT and Harvard, it has used the system to rank compounds predicted to produce therapeutic shifts in tumor cell state, with several top-ranked candidates validated across multiple wet-lab assays. The announcement is authored by Nicolo Fusi, VP and distinguished scientist, and Jonathan M. Carlson, vice president. A World Model Built Across Biological Scales Microsoft defines the world model it is pursuing as one that represents the state of a biological system, forecasts how that state would change under an intervention, and reasons through the downstream consequences several steps ahead. The company says such a system would not replace experimentation; instead, it would let researchers weigh and rank candidate directions computationally before committing limited laboratory resources. Quine’s core model is trained to learn representations shared across biological scales and data types, among them sequence, structure, function, cellular state, and imaging. Its training modalities span genomics, proteins, chemistry, RNA and cell state, and bioimaging. Microsoft reports that joint training across these modalities strengthens rather than dilutes performance: evidence from one modality can inform predictions in another, capturing relationships the company says could stay siloed or be lost if separate single-domain specialist models were orchestrated instead. In Microsoft’s description, the system runs as a cycle that starts and ends with the scientist: a question yields proposals, proposals turn into experiments worth running, and the resulting measurements refine both the next question and the model itself. The model need not be perfect, the company says, adding that it will never perfectly capture biology; it needs only to guide experimental design usefully. The Qu…

Building a new path to make medicines with AI - Isomorphic Labs

Tue, 29 Sep 2026 07:00:00 +0000

Listen: Solving all disease is one of the most monumentally complex challenges in science. Today, for the first time, AI provides the foundation to make it achievable. Tackling the frontier of human health is our mission, and it means identifying exactly where modern AI can make the greatest difference. We see drug discovery as the critical wedge in solving all disease, and therefore our initial focal point where AI can have a transformative impact on human health. Developing a drug that successfully and effectively treats disease fundamentally involves understanding biological mechanisms and designing exquisitely engineered molecules that can modulate them safely. So we are taking a straight shot at making that a reality. We are generating the data, training the AI models, developing the software, and laying down the physical infrastructure to make new medicines; taking these potential therapeutics all the way to patients in the clinic. We are building this new architecture in a way that will scale across multiple disease areas, and different types of medicines, from small molecules to antibodies, peptides, and beyond. In 2021, when we started Isomorphic Labs, AI drug discovery was just a hypothesis. Conventional approaches were bottlenecked by slow, resource-intensive iterative design-make-test cycles, viewing AI as an experimental tool for narrow tasks. At the same time the AI revolution was well underway across multiple industries. A seismic shift was happening with the emergence of foundation models, moving away from single-task algorithms toward highly versatile systems capable of world-understanding, zero-shot reasoning and generating complex text and images. Up to this point, I had spent my education and career in core AI, building intelligent systems by scaling up neural networks and deep reinforcement learning to solve the hardest problems at a superhuman level. But having grown up in a medical family, I recognised the potential to apply this superhuman new technology to humanity’s most meaningful challenge: human health. Working with Sir Demis Hassabis, we created the founding team at Isomorphic Labs to pivot breakthroughs in AI toward solving the drug discovery problem. We set out on a path to approach drug discovery as an engineering process, stripping back to first principles, to rebuild the process to design medicines. Almost every field of e…

Hidden Dates in System Prompts Shift LLM Evaluation Results

Tue, 29 Sep 2026 00:00:00 +0000

Reproducibility is essential for scientific research, yet prior work shows that LLM outputs vary with hardware and batching. We identify an overlooked factor: the hidden injection of the current date into system prompts, which users cannot control and which changes every day. Across 9 recent LLMs and 6 datasets spanning multiple-choice QA (MCQA), math reasoning, code generation, and machine translation, performance varies solely with the current date, with deltas of up to 6% on MCQA, 14% on math reasoning, 7% on code generation, and 2.84 BLEU on machine translation. Model rankings also shift, affecting leaderboards. This date effect exceeds other sources of non-determinism, such as batch size and numerical precision. Standard prompting techniques -- chain-of-thought and few-shot prompting -- do not reduce the sensitivity; chain-of-thought even amplifies it. Our findings underscore the need for careful evaluation protocols to ensure reproducibility and fair comparisons in LLM research.

Behavioral Persistence and Incomplete Functional Transfer of Co-evolved Communication in Evolutionary Robotics

Tue, 29 Sep 2026 00:00:00 +0000

This work evaluates the direct transfer of a co-evolved communication protocol from a 2D simulation to a 3D physical environment, without retraining the network weights. Two e-puck-type robots, controlled by a GRU network with residual connection, were evaluated in a food-seeking task with social signaling. The sensory and motor translation layer required three corrections for stable physical operation, including the calibration of a hunger term based on a measurable asymmetry in the trained residual weights. Even with these corrections, the transfer was partial and asymmetric: one agent reached the food source in one of thirty tested seeds, while the other did not reach it in any. Task success was measured by both agents reaching the food area. An additional experiment incorporating explicit directional information in the social channel produced observable changes in the trajectory of the receiving agent and improvements in several specific cases. However, these improvements were not enough to allow the second agent to reach the food source, suggesting that the limitation may not be explained solely by signal translation, but also by the ability to navigate under the new physical constraints. The results suggest that successful transfer of emergent communication may depend not only on preserving the signaling process itself, but also on preserving the ecological and navigational conditions under which the protocol evolved.

OTRetarget: Joint Robot and Object Motion Retargeting via Optimal Transport

Tue, 29 Sep 2026 00:00:00 +0000

Transferring human motion to humanoid robots requires adapting the demonstrated motion to the robot morphology while preserving interactions with the environment. This is particularly challenging for loco-manipulation tasks, where contacts with the ground and manipulated objects must remain consistent despite differences in body proportions. Yet, skeletal motion alone does not fully describe these interactions, and fixing object trajectories limits the adaptation to a new embodiment. In this paper, we introduce OTR ETARGET, a unified approach to jointly retarget robot and multi-object motion from human demonstrations. Our approach represents surface interactions through signed distances, closest surface points, and relative directions, and uses entropic optimal transport to transfer these quantities across human, robot, and object geometries. We incorporate the resulting interaction targets into a constrained inverse kinematics formulation that balances contact preservation with motion style and jointly optimizes robot and object poses at each frame. This formulation accommodates robot-object and object-object interactions without rescaling the scene or the demonstration. We validate the proposed approach on OMOMO, where it achieves a robot- object interaction Jaccard score of 87% and a depth error of 8.7 mm, compared with 28% and 29.3 mm for OmniRetarget. Finally, we demonstrate transfer to a physical G1 humanoid using whole-body policies trained with reinforcement learning on the retargeted references, across motions including two-handed box pick-and-place onto a table.

Video2Skill: Benchmarking the Organizing of Streaming Video Experience into Reusable Embodied Skills

Tue, 29 Sep 2026 00:00:00 +0000

Manipulation behaviors vary widely across objects and scenes, but they share a small set of reusable skills, and planning with these skills helps embodied agents generalize to new tasks. Yet an agent can only plan with skills it knows. Recovering skills from observed experience, the inverse of planning, builds this knowledge over time and yields skill data for training future agents. Vision-Language Models (VLMs) describe individual manipulation events well, but can they organize a stream of events into reusable skills? We formulate this problem as Streaming Embodied Skill Discovery (SESD): a model watches videos in sequence and maintains a persistent skill library that shapes its later decisions. To systematically measure this ability, we introduce Video2Skill, a benchmark that covers robot tabletop manipulation and human kitchen activity and tests three core capabilities: (i) locating manipulation events in time, (ii) grouping events of the same transformation, and (iii) deciding when to reuse an existing skill or create a new one. Across 19 open-source VLMs, many models group events at near-chance level, and scale does not consistently help. Their errors depend on how perception and library updates are coupled: joint models merge distinct transformations into one skill, while models that update the library from text descriptions duplicate recurring ones. Supervised fine-tuning, including our counterfactual library-state rebalancing (CLaRe), improves grouping but exposes a deeper bottleneck: trained models consolidate familiar skills yet rarely expand the library. Their libraries stall below half the reference size, and transformations unseen in training are located in time but almost never given a new skill. Recognizing when existing skills are insufficient thus emerges as the central challenge.

EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making

Tue, 29 Sep 2026 00:00:00 +0000

Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by training agents to predict future observations, at the cost of additional training and errors that compound when predictions are used for planning. However, for LLM agents operating in digital environments, much of this world knowledge is already internalized during pretraining, which shifts the problem from acquiring it to eliciting it. We argue that typical post-training provides little pressure for such elicitation, since supervision under a single goal at each visited state inadvertently drives policies to rely on superficial contextual habits. We introduce EVOKE, a post-training method that supplies this pressure through goal diversity at fixed states. Motivated by theory showing that an agent competent across diverse goals must encode a world model recoverable from its action preferences, EVOKE holds the environment state and interaction history fixed and ranks the same candidate actions under alternative goals, forcing action preferences to change, so that a policy relying on contextual habits or single-goal correlations cannot order them correctly. This implicitly elicits the policy's pretrained world knowledge to inform decisions. We evaluate EVOKE across diverse tasks in three backbones, demonstrating improved task performance, unseen environment generalization, and data efficiency. We further conduct controlled analyses to better understand what drives these gains. These findings offer a new perspective on eliciting internalized world knowledge for transferable action through direct decision supervision.

RLE-Bench: A Qualifying Exam for Coding Agents as Robot Learning Engineers

Tue, 29 Sep 2026 00:00:00 +0000

Coding agents are beginning to move beyond purely digital tasks to tackle physical-world challenges, particularly in robotics. Existing robotics benchmarks, however, primarily focus on the performance of individual artifacts, such as policies or controllers, offering limited coverage of coding agents' broader engineering capabilities. Real-world robotics extends beyond control: agents must build, integrate, diagnose, and improve heterogeneous artifacts under resource constraints and reason from multimodal feedback. To evaluate these broader capabilities, we introduce RLE-Bench, a benchmark of robot-learning tasks spanning four representative robotics development workflows: interactive control, policy learning, perception and estimation, and mechanical design. We use diverse task-specific metrics to evaluate the artifacts submitted by the coding agents, from the success rate the agents achieved to the policy agents trained, the harness agent built, and the mechanical structures the agent designed. We aggregate these metrics into an overall RLE Index and report workflow-specific capability profiles, enabling systematic comparison of coding agents' capabilities across multiple capability dimensions. Beyond performance ranks, we also conduct in-depth case studies examining agent behavior on representative tasks, highlighting both current capabilities and limitations, and pointing to the opportunities robotics tasks have to offer for future agent training.

AdviSD: Learning to Advise Frontier LLMs via Targeted Multi-Turn Self-Distillation

Tue, 29 Sep 2026 00:00:00 +0000

A small trainable advisor can steer a frozen language-model executor using natural-language advice. In addition to learning from task rewards, the advisor can use feedback from completed interactions to improve its advice. However, a plausible correction need not change execution, yet learning from such corrections can still affect the advisor's future decisions in other contexts. In a shared-parameter model, we prove that such corrections can limit learning if their targets favor useful advice less strongly than those of other corrections. Keeping them less often than the rest improves the model's eventual performance compared to learning from every correction. Motivated by this, our method, Advisor Self-Distillation (AdviSD), pairs outcome-based reinforcement learning with self-distillation from a feedback-conditioned copy of the advisor selectively. Reflection proposes corrections, and the advisor scores the same recorded executor response with and without its issued advice, using the magnitude of the difference to select decisions for supervision. This approach does not require executor likelihoods or additional executor rollouts. Experiments with Qwen3-8B advisors for Gemini and Claude show that AdviSD outperforms advisor-GRPO by 4.2-6.4 percentage points on BFCL-v3 and by 3.9-5.1 score points on EnvScaler. The trained advisors generalize to out-of-domain tasks and transfer across different executor versions and model families. AdviSD also beats matched-count random selection, supporting the value of its selection rule.

Joint Statement about Mathathon

Mon, 28 Sep 2026 18:33:48 +0000

[This is a guest post by a coalition of Caltech mathematicians: the original organizers of Mathathon (Alvan Arulandu, Andrea Li, Avni Garg, Brian Zhao, Caiman Moreno-Earle, Sathvik Redrouthu), two coauthors of the Open Letter about the Mathathon (Dylan King, Tasmin Chu), and other members of the Caltech community (Mayla Ward, Merrick Hua, Robert Joseph George, Sergei Gukov, Shaowu Zhang, Tony Yue Yu, Vihaan Dheer). This blog post was initially written in a different file format and converted using AI. It is also crossposted at Proofs and Prompts — T.] Mathathon is centered around one question: how can we use AI to augment human understanding of mathematics? This past week, the original organizers of Mathathon, two coauthors of an open letter about the mathathon , and other members of the Caltech community engaged in a conversation to redesign this event. We quickly reached a consensus: problem solving is but one component of mathematics; mathematical understanding and exposition are similarly meaningful. But these components have often been overlooked by grantmakers and hiring committees. We want to celebrate human mathematicians who undertake this work. Thus the new theme of Mathathon is Old Problems, New Proofs . Consider the four color theorem, the ABC conjecture, or the Navier-Stokes problem. Many mathematicians find their proofs—or claimed proofs—difficult to understand or unsatisfying. We invite our participants to pick a problem with an unintuitive solution, learn as much as possible in 40 hours, and present their findings to their peers. Then, they will take two months to develop an alternative proof or exposition. Afterward, they’ll submit an explainer and a GitHub repository. The explainer can take any form: a paper, a blog post, a video, an interactive game, et cetera. The repository will store whatever was used to produce the explainer—LLM chat histories, code used to produce visualizations, and more—so the mathematics community can examine the ideas behind the finished product. By shifting the focus from open problems, we want to encourage Mathathon participants to explore everything else we do to make sense of mathematics: generalizations, new notations and techniques, connections to other problems. These innovations can be more exciting than solving individual problems. Mathathon treats AI as one of many tools available to mathematicians. We…

AI-powered platforms predict proteins that undergo phase separation

Mon, 28 Sep 2026 11:25:45 +0000

From left: Dr Liang Qiyu, research fellow at the Institute for Digital Molecular Analytics and Science at NTU, and Professor Miao Yansong. Credit: NTU Singapore. Scientists at Nanyang Technological University, Singapore (NTU Singapore) have developed two artificial intelligence (AI)-powered platforms that could enable researchers to more accurately predict proteins that undergo phase separation – a process through which proteins in cells separate like oil droplets in water. The tools are the result of a systematic analysis of predicted phase-separating proteins from various living things, including animals, plants, fungi and single-celled organisms such as bacteria. From their analyses, the scientists also uncovered fundamental insights about the phase separation process. Led by Professor Miao Yansong from the School of Biological Sciences and the Institute for Digital Molecular Analytics and Science (IDMxS) at NTU, in collaboration with Professor Weibo Gao from NTU’s School of Electrical and Electronic Engineering, the research could be applied to advance the understanding of diseases and ageing, as well as to improve crops. Both AI platforms and their research findings have been reported in the peer-reviewed journal Molecular Plant . Predicting which proteins separate Phase-separating proteins separate from their surroundings and concentrate into droplet-like compartments, enabling cells to bring molecules together, accelerate or suppress reactions to respond rapidly to stress. When phase separation is altered, these proteins can form abnormal condensates or aggregates, which are associated with diseases, including cancer and neurodegenerative disorders such as Alzheimer’s disease. In plants, changes in phase separation are linked to impaired stress responses and lowered immunity against microbial pathogens. Identifying proteins that undergo phase separation and determining how this behaviour is regulated are important for applications ranging from biomedical research on diseases and ageing to devising approaches for sustainable agriculture. However, accurate prediction remains challenging because phase-separating proteins assemble dynamically into complex structures and interact with numerous cellular components. Addressing these challenges requires comprehensive experimental datasets and systems-level information across diverse organisms. In addition, m…

CheatBench: A Benchmark for Measuring Reward Gaming in AI Agents

Mon, 28 Sep 2026 00:00:00 +0000

Reinforcement learning has helped AI agents solve increasingly difficult tasks, but high rewards do not always reflect the work users intended. In recent incidents and controlled evaluations across the AI industry, agents trained to maximize reward have accessed unauthorized information, attempted to evade monitoring systems, and even breached sandbox protections to attack external systems. As agents become more capable, this behavior could pose increasingly serious risks. To measure this problem, we introduce CheatBench, a benchmark of cheating in AI agents across mathematical research, knowledge work, coding, visual tasks, and other domains. Its environments combine challenging assignments with opportunities to cheat, allowing researchers to study how agents pursue a goal when honest work is difficult. CheatBench supports comparisons across models and task categories, providing a testbed for measuring and reducing cheating as agents take on more consequential responsibilities. We publicly release CheatBench at https://cheatbench.ai

Microrobot navigation policies trained in under 10 minutes

Mon, 28 Sep 2026 00:00:00 +0000

Microrobots hold considerable potential for various applications in which targeted navigation is a basic requirement. Deep reinforcement learning has recently emerged as a powerful paradigm for fully autonomous microrobot navigation. Yet, current deep reinforcement learning-based approaches pay limited attention to learning efficiency and effectiveness, requiring hours to days for model training. Consequently, this impedes both rapid practical deployment and parameter optimization. To address these challenges, we present a learning framework that enables effective microrobot navigation policies to be trained within minutes. Here in the proposed framework, we develop a fully vectorized simulator with more than 10,000 artificial vascular environments, parallelizing dynamics, ray-casting-based visual feature extraction and feasibility checks across thousands of environments to achieve roughly 190,000 transitions per second. To achieve effectiveness in the fast training, we propose a task-shaping-regularization reward framework. The task-shaping-regularization framework accelerates convergence, improves final performance, reduces action variation by at least 33.7% and increases obstacle clearance by at least 2.1% across all evaluated scenarios. Results show that the proposed learning framework reduces training time to under 10 minutes, while supporting zero-shot deployment across distinct microrobot types and navigation scenarios. Collectively, this framework can substantially shorten the design loop and accelerate the deployment of autonomous microrobots. A vectorized simulator and structured reward framework enable microrobot navigation policies to be trained within minutes and transferred without retraining across robots and environments.

On-Policy or Off-Policy Learning? A Systematic Study of Distillation Dynamics

Mon, 28 Sep 2026 00:00:00 +0000

On-policy learning has been argued to reduce catastrophic forgetting, produce sparser parameter updates, and improve generalisation. However, existing comparisons between supervised fine-tuning and reinforcement learning vary many factors simultaneously, making the contribution of rollout policy difficult to isolate. We study the effect of rollout policy in a controlled strong-to-weak distillation setting, by independently varying rollout policy, token-level KL direction, and learning rate across the Llama3 and Qwen2.5 model families and reasoning tasks spanning scientific, medical, and arithmetic domains. Our analysis reveals a nuanced picture of distillation dynamics in which rollout policy does not necessarily play a central role. Instead, token-level KL direction more clearly shapes task performance and output coverage, while learning rate governs forgetting and update sparsity. Analysis of KL gradients and experiments along a continuous student-teacher rollout-policy spectrum explain this pattern: forward KL is remarkably robust to rollout policy, with its performance stable and strong despite changes to the rollout policy, whereas reverse KL is substantially more sensitive and favours student-generated rollouts. On-policy data nevertheless improves generalisation to harder variants of the Countdown arithmetic task under both KL directions, although this advantage does not reliably persist after subsequent RLVR. Our broader conclusions remain robust to removing gradient clipping, using sampled KL estimators, and training on tasks requiring longer reasoning chains. Overall, our results challenge the view that on-policy rollouts are inherently preferable and show that their value depends critically on the objective, evaluation setting, and optimisation hyperparameters.

When Does Correction Become Repair? Mechanistic Auditing of Internal Interventions in Tool-Using LLMs

Mon, 28 Sep 2026 00:00:00 +0000

Before invoking external tools, an agentic LLM must select among a K-way action space: executing a call, seeking clarification, answering directly, or declining. While internal activation steering can alter these pre-execution decisions, conventional aggregate metrics obscure where altered states land and what collateral damage they inflict. We present SAKIKO, an auditing framework that formalizes representation repair via directional error discovery, router-conditioned intervention, destination-resolved verification, and prospectively frozen statistical licensing. Across seven LLMs on When2Call and MetaTool, channel-keyed interventions induce direction-specific net gains in five models; across three sealed evaluations, none of 59 budget-matched random directions matches calibrated target gain. Crucially, destination auditing shows that behavioral movement does not equal repair: an intervention achieving +55 net gain corrupts over half of the baseline-correct decisions it touches, and promising point estimates on Qwen3-4B and Gemma-2-9B are formally declined due to finite-sample uncertainty. SAKIKO establishes the necessity of outcome-resolved adjudication before claiming internal repair. Code: https://github.com/ruizheliUOA/mechanistic-tool-use-llm.

MM-ABC: Towards Generalist Mobile Manipulation via Seeing, Coordinating and Imagining

Mon, 28 Sep 2026 00:00:00 +0000

Mobile manipulation extends robot interaction beyond a fixed kinematic workspace by making the reachable region itself controllable. This flexibility introduces two central challenges: spatially grounded perception under continuous ego-motion and coordinated control of heterogeneous arm and base actions. Existing approaches strengthen geometry through explicit 3D representations or predictive world models, and often decouple mobility and manipulation into separate action streams. We argue that effective mobile manipulation requires not only decoupling, but also representations that support efficient cross-stream collaboration. We present MM-ABC, a foundation model built around Seeing, Coordinating, and Imagining Arm-Base Collaboration. MM-ABC combines sparse multi-level VLM features for spatial perception; a training-only future branch that uses world imagination and geometric intent as extra supervision, strengthening perception and manipulation-intent prediction and improving the overall learning signal; and MM-APT, which coordinates separate manipulation and mobility streams through masked joint attention and clean-action x-prediction. In controlled ablations, replacing clean-action prediction with velocity prediction lowers success on RoboCasa365 composite-seen tasks from 32.8% to 29.2%, and removing future supervision or multilevel conditioning causes larger drops. We pretrain MM-ABC on 5,000+ hours of heterogeneous robot data spanning 400K+ episodes, 12 datasets, and 17 embodiments. Experiments cover EBench, RoboCasa365, ManiSkill-HAB, LIBERO, LIBERO-Plus, and real-world mobile manipulation. MM-ABC achieves 44.71% success on EBench, 61.2% on RoboCasa365, 99.1% on LIBERO, 82.8% on LIBERO-Plus without perturbation training, and 83% mean success on five real-world tasks.

Multilinguality in Hybrid Attention LLMs

Mon, 28 Sep 2026 00:00:00 +0000

In response to the growing demand for long sequences in agentic and reasoning use cases, many state-of-the-art LLMs combine multiple variants of attention to mitigate the quadratic complexity of traditional softmax attention. These hybrid attention LLMs aim to balance the strengths and limitations of full attention and alternatives based on recurrence. This work presents a first study of how hybrid attention impacts the multilinguality of LLMs. Beyond the impact on long sequences in poorly tokenized languages, our study is motivated by the possibility that the inductive biases of the recurrent state alter linguistic processing. Our interpretability analysis confirms this, showing that cross-lingual representations in hybrid models develop in patterns tied to the ordering of recurrent and full-attention layers. Across diverse models, we notably observe a pronounced spike in cross-lingual alignment around the first full-attention layer. These findings lead us to question the conventional ordering of attention layers. In distillation experiments on multilingual data, all alternative layer orderings outperform the standard throughout training, learning up to 2.5X faster. These stark, replicable results prompt our theory that multilingual models would benefit from starting with a full-attention layer rather than recurrent layers.

Unifying AI-assisted scientific discovery around exploration, hypothesis generation, and testing

Mon, 28 Sep 2026 00:00:00 +0000

In this review, Hasib et al. introduce EXHYTE a process centric survey to unify research on AI-assisted scientific discovery. Mapping recent studies reveals rapid advances in evidence retrieval, knowledge assembly, and idea generation, but it also reveals a persistent bottleneck in translating hypotheses into executable tests and using results to refine subsequent steps. The survey underscores the need for human oversight, reproducibility, validation, and auditable end-to-end discovery workflows.

WM-VLM: Probing Internal World Models for Interleaved Visual-Textual Reasoning

Mon, 28 Sep 2026 00:00:00 +0000

Humans often solve spatial problems by mentally simulating visual transformations. In contrast, conventional vision-language models (VLMs) reason primarily through language. We investigate whether VLMs can solve spatial problems by reasoning with both text and generated visual states. To this end, we introduce WM-VLM, which equips a pretrained VLM with a lightweight world model branch for generating intermediate visual states. Our two-stage training first teaches the model to generate the next visual state and then to use that state for reasoning. We programmatically construct spatial reasoning tasks with verifiable intermediate visual states. These tasks allow us to evaluate how well the model generates visual states and how much it relies on them to answer the question. On 2D and 3D mental rotation tasks, WM-VLM consistently outperforms the supervised fine-tuned backbone, with gains of up to 39.25 percentage points. Ablations suggest that these gains depend on the generated visual states, as removing or corrupting them sharply reduces performance. Together, these results suggest that internal world models offer a promising path toward VLMs that reason in both language and visual space.

Unmask the State: When Does State Adaptation Matter for Masked Diffusion Language Models

Sun, 27 Sep 2026 00:00:00 +0000

Masked diffusion language models (MDMs) admit flexible generation orders, making the unmasking strategy an inference decision. Existing methods vary in how they prioritize positions, control parallelism, restrict selection regions, revise predictions, or plan future denoising, yet it remains unclear when these choices should change during generation. We study this question through strategy reversals, where an alternative action becomes preferable to a fixed choice. We organize MDM inference into five axes--score, cardinality, region, commitment, and planning--and define adaptation opportunity as the one-step utility advantage of the best candidate action over a validation-selected fixed action. This view shows that adaptation value depends on both the frequency and magnitude of such reversals. Across three MDMs and ten tasks, adaptation opportunities are highly heterogeneous, with some regimes exhibiting concentrated and predictable one-step gains. This motivates selective adaptation: lightweight detectors calibrated on validation prompts identify high-opportunity states, capturing, for example, 56.9 percent of the candidate-set oracle opportunity by adapting only the top 10 percent of states on LLaDA-8B constrained JSON filling. Our transition-level results suggest that state adaptation is most useful when applied selectively rather than uniformly.

OpenTumorBoard: A Real-World Benchmark of Multidisciplinary Tumor Board Discussion Trajectories

Sat, 26 Sep 2026 00:00:00 +0000

Multidisciplinary tumor boards integrate multimodal clinical observations and longitudinal patient histories through specialist discussions, yet benchmarks rarely capture these real-world trajectories. We introduce OpenTumorBoard, a benchmark with 611 patient cases and 19,157 discussion turns across ten specialist roles, transcribed from 12,534 minutes of publicly available tumor board recordings on YouTube. The benchmark evaluates two settings: SPECIALIST TURN, in which an LLM responds to a clinically significant question posed during a real discussion, and BOARD SIMULATION, in which it generates an entire back-and-forth discussion and reaches a consensus on therapy recommendations, surgical plans, next actions and clinical trial matching. Evaluation of 14 general-purpose frontier and medical LLMs reveals substantial limitations: the best models score 3.43 out of 5 in clinical equivalence to specialist answers and 2.78 out of 5 in alignment with recorded board conclusions. Supervised finetuning and reinforcement learning improve performance on a held-out test set, suggesting that real-world discussion trajectories can support model adaptation. Three M.D. experts review a subset of the benchmark, finding high information coverage and factuality of patient cases and strong fidelity of extracted consensus conclusions. We will release OpenTumorBoard and its automated curation pipeline to support the development and evaluation of LLMs for multidisciplinary, personalized cancer decision-making.

LANTERN: Illuminating Hidden Mathematical Knowledge in Language Models

Sat, 26 Sep 2026 00:00:00 +0000

Language models can now prove theorems, but people still decide which problems to pursue. We ask whether a model's internal representations can help identify promising mathematical connections. We develop LANTERN, a fast, cost-efficient pipeline that uses a classifier over pretrained-model activations to rank candidate relations, followed by staged filtering, hypothesis generation, executable verification, and analytical checking. Applied to the On-Line Encyclopedia of Integer Sequences (OEIS), LANTERN ranked 50 million pairs among 10,000 frequently referenced sequences and produced 62 verified relations between pairs without an existing OEIS cross-reference. A content screen retained 13 relations worth presenting; nine of these are informative or insightful, including four which are entirely novel to the best of our knowledge: none appears in the OEIS or in our targeted literature search. The entire end-to-end process including classifier training, candidate ranking, filtering and verification took under 8 hours.

X-Tree: Tokenizing Reusable Experience for Efficient Agent Generalization

Sat, 26 Sep 2026 00:00:00 +0000

Multi-step agents are trained on flat action streams: SFT and RLVR weight every token uniformly and ignore the sub-procedures that recur across tasks, the hierarchy that lets humans plan top-down from reusable routines. This structure sits unused, and flat training uses each scarce trajectory less fully than its content allows. Recent agents do use that structure, but only as LLM-written skills in context, never in the weights, so their gains do not generalize beyond retrieval. We instead recover this hierarchy from the data itself and train on it, with no LLM calls. Following text tokenizers, which build a vocabulary by counting alone, we score action spans by reusability and merge canonicalized actions into a reusable eXperience tree (X-Tree). Each X-Tree node captures how a frequent and success-bearing skill is composed from sub-skills, guiding efficient generalization. We integrate X-Tree into three training settings: offline RL, with each node as a training instance; online RLVR, with an adaptive skill bonus; and on-policy self-distillation, with X-Tree as the self-teacher's privileged context. Across WebArena, ScienceWorld, and WebShop at three model scales, X-Tree improves over standard recipes at matched data and budget by up to 4.5% SR on WebArena, 5.8% SR on ScienceWorld and 4.1% success on WebShop. Matched analyses attribute the gains to the X-Tree structure and the three integrations.

Anthropic Says Claude Computed a Nine-Loop Particle Physics Amplitude

Fri, 25 Sep 2026 18:02:01 +0000

AI Models & Platforms Anthropic Says Claude Computed a Nine-Loop Particle Physics Amplitude Add Unite.AI to your preferred sources on Google Anthropic on September 25, 2026 published a guest post on its research site reporting that two of its physicists used the company’s Claude model to compute the six-particle scattering amplitude in planar N=4 super Yang-Mills at nine loops, answering a public challenge issued to AI companies on August 7, 2026. The Challenge Matt von Hippel, a former theoretical physicist who now writes about physics at 4gravitons.com, set the terms in an August 7, 2026 blog post. He asked AI companies to show that an AI, using the computing resources an academic has access to, could solve one of the scattering amplitudes field’s big outstanding problems: determining whether N=8 supergravity diverges at seven loops, or finding the six-particle amplitude in N=4 super Yang-Mills at nine loops. “Give us N=8 supergravity to seven loops, or N=4 super Yang-Mills to nine loops,” he wrote. Von Hippel argued that these problems are hard in a computational sense: each loop represents an increase in complexity, in calculations that typically scale exponentially or even factorially in the number of loops. In principle, he wrote, amplitudes researchers could solve any of them with no new ideas, using known methods, but they would need access to far more computing power. At the end of August 2026, Liam Fitzpatrick and Siddharth Mishra-Sharma, two physicists at Anthropic, contacted von Hippel to say they had tackled one of the challenges, and after verifying the result with longtime amplitudes researcher Lance Dixon they walked him through the work. The post’s disclosure states that Anthropic invited von Hippel to write the guest post and compensated him for his time, that Anthropic staff gave feedback on drafts, and that the content and opinions are his own. The Physics Problem Scattering amplitudes are formulas that let physicists use the momenta and energies of subatomic particles to calculate how likely they are to react in particular ways. They are hard enough to compute that physicists almost always work with approximations cut off at a specific number of loops: the more loops included, the closer the result comes to the real answer and the harder the calculation becomes. Most scattering amplitude formulas have been calculated only to two loops,…

Yes, Claude can do Nine Loops

Fri, 25 Sep 2026 16:58:00 +0000

Science In this guest post, physicist and science writer Matt von Hippel shares what happened when he issued a challenge to AI companies regarding a problem in his former subfield of theoretical physics. It’s not often that you issue a challenge, only to see it beaten a month later. But we’re living in unusual times. Let me introduce myself: I’m Matt von Hippel. I used to be a theoretical physicist; these days I’m a science writer. Throughout, I’ve been a blogger, writing weekly at 4gravitons.com about physics and the people who do it. More and more, blogging about physics has meant blogging about AI. That’s a problem, because I’m definitely not an AI expert. I’ve dabbled in it, sure. I probably know more than your grandma. But I mostly have to step back and trust the experts. And frustratingly, the experts disagree! I’ve heard from smart, well-informed people who are confident that AI is a few years away from superintelligence, and that superintelligence will be capable of truly terrifying things. And I’ve heard from smart, well-informed people who are equally confident that LLM-based AI is close to a ceiling, that models like Claude won’t even be able to do impressive work in physics, let alone conquer the world. I’ve been reluctant to make my own predictions. Before forming an opinion, I wanted to see an LLM make progress on something familiar, something I knew was hard to do because I’d tried to do something similar myself. In addition to that, I wanted to see an LLM do something that I expected to be computationally hard. LLMs have made impressive strides in math, certainly, and this month alone has likely changed many people's minds. But progress in math comes from new ideas, and ideas are mysterious things: one never quite knows how hard they are to find until they’re found. Computation felt more solid. I wanted to see an LLM tackle a challenge that seemed out of reach not because researchers didn’t know how to do it in principle, but because doing it seemed like the kind of thing that would take more computers and time than the researchers reasonably had access to. I wanted to see if those researchers were wrong: if a smarter, artificial researcher could use the same computers, and solve the problem anyway. So, I issued a cha…

When compression techniques don’t just add up: Interaction effects in hybrid LLM compression

Fri, 25 Sep 2026 13:26:03 +0000

What happens when you shrink a language model twice — once by removing some of its weights, once by storing what’s left with fewer bits? You’d guess the damage just adds up. It doesn’t. The problem: does compression just add up? Small language models are increasingly used for on-device apps that need to run in tight memory budgets [Zhou et al. 2025]. To shrink them enough, people often combine two tricks: pruning, which removes some of the model’s weights, and quantization, which stores the remaining weights using fewer bits. Recent work has combined both, often finding the mix beats either trick alone [Kang et al. 2025]. But most of these studies only look at the combined result — they don’t ask whether the two tricks are simply adding their effects together, or doing something else entirely. We wanted to find out. A simple test: does it just add up? To check, we borrowed a simple idea from statistics. For each benchmark, we measure four things: the original model, the pruned-only model, the quantized-only model, and the hybrid. Then we compare: If the two tricks were simply adding up, would land close to zero. If is positive, the hybrid does better than expected. If it’s negative, the hybrid does worse. We tested this on three small models — Falcon3-1B-Base, LLaMA-3.2-1B, and a quick check on Qwen2.5-1.5B — at different pruning levels and with both 4-bit and 8-bit quantization, all on a single consumer GPU (an RTX 3080 Ti). What we found: truthfulness up, reasoning down The results split sharply by task. On TruthfulQA, the hybrids consistently beat expectations — by about +7 points on Falcon and +12 to +14 points on LLaMA. It’s as if combining the two tricks accidentally made the models more careful about truthfulness. On reasoning and knowledge tasks, the opposite happened. GSM8K (grade-school math) and MMLU (general knowledge) dropped far more than expected — by 16 to 20 points on Falcon. In several hybrid setups, GSM8K accuracy fell all the way to zero. The twist: more pruning doesn’t mean more damage We assumed these effects would get worse the more we pruned. They didn’t. Instead, they peaked at moderate pruning (20–30%) and faded back toward normal at 50% pruning — both the TruthfulQA boost and the reasoning drop. More pruning didn’t mean more damage; it meant the surprise effect faded. We also found the order barely mattered — quantizing before pru…

Lila Sciences' AI Lab Identifies Six Palladium Catalyst Families for Green Hydrogen

Fri, 25 Sep 2026 07:00:00 +0000

AI Models & Platforms Lila Sciences’ AI Lab Uncovers Palladium Catalysts for Green Hydrogen Add Unite.AI to your preferred sources on Google Lila Sciences said on September 25, 2026, that its AI-directed laboratory proposed, synthesized and screened 2,942 catalysts for green hydrogen in three months, identifying six palladium-based material families for the acidic oxygen evolution reaction at the heart of hydrogen production. The research team detailed the work in a preprint submitted to arXiv on September 24, 2026: “AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution,” by Ken J. Jenewein and 20 other authors. Producing hydrogen from water requires two half-reactions: the hydrogen evolution reaction at the cathode and the oxygen evolution reaction, or OER, at the anode. OER runs at high potentials in strongly acidic environments that dissolve most metals. In today’s commercial electrolyzers, iridium oxide is the catalyst best able to survive those conditions while remaining active; ruthenium is a less stable option. Both are among the rarest elements on Earth, and nearly all iridium is produced as a byproduct of platinum mining, at only a few tonnes per year worldwide. The Palladium Result While screening 2,942 oxides across 53 material systems and 26 elements, the platform began synthesizing and testing palladium compositions. According to the company, palladium had been considered a dead end for OER because it was assumed to underperform in acidic conditions. The model’s candidates were modified palladium compositions rather than the pure metal, with small additions of other elements transforming the metal’s behavior. During the second and subsequent campaigns, the program identified six palladium-based material families on or near the Pareto front for OER, meaning an optimal balance of activity and stability. After repeated activity tests and more than 1,000 hours of stability testing, Lila said the top metal composition performed comparably to ruthenium, an industry-standard OER catalyst. John Gregoire, the company’s chief autonomous science officer, said his former group at Caltech explored catalysts for this reaction for 12 years and that he would have advised a student proposing this combination of elements to pursue a more promising direction. “This combination defies all t…

Science Podcast: How AI language evolves inside swarms, how baby orangutans learn to forage, and an astrophysicist on loss

Thu, 24 Sep 2026 18:00:00 +0000

First up on the podcast, Staff Reporter Tom Howarth joins host Sarah Crespi to talk about the language artificial intelligence agents evolve inside their swarms . Next on the show, producer Elah Feder talks with Revathe Thillaikumar , a behavioral ecologist and postdoctoral fellow at the Max Planck Institute of Animal Behavior. The two discuss the value of social learning as baby orangutans learn to find their own food over the longest dependency period of any animal, except humans. Finally, from our series on scientific autobiographies, books host Angela Saini talks with astrophysicist Sara Seager about the moving account of life after losing her husband in her book, The Smallest Lights in the Universe . This week’s episode was produced with help from Podigy . About the Science Podcast Image: inkoly/istock Learn more about your ad choices. Visit megaphone.fm/adchoices

Anthropic Says Claude Spotted a Previously Unknown Enzyme System

Thu, 24 Sep 2026 07:00:00 +0000

Anthropic touts AI-led biology discovery Andrew Zinin Chief Editor U.S. artificial intelligence startup Anthropic on Wednesday unveiled the first discovery from its molecular biology lab, a previously unknown enzyme system spotted by its Claude model and touted by the company as proof that AI can speed up basic research. Enzyme systems found in nature have led to some of biology's biggest breakthroughs, from the tools that launched the biotech industry to the DNA-copying method behind many medical tests. According to the company, some 950 Claude-powered software agents combed through a database of DNA sequences for 21 hours. The agents found more than 200,000 genes for one type of enzyme and picked out 20 worth a closer look—work that would take an expert scientist weeks or even months. Anthropic said its researchers' involvement was limited to the initial instructions and the lab experiments, with the AI agents working autonomously to decide which leads to pursue. One of the agents spotted something in viruses that infect bacteria that scientists appear to have missed: a string of short, repeating pieces of DNA sitting next to two genes, one for a known enzyme and one for a mystery protein. The setup looks like CRISPR, a natural defense system in bacteria that scientists turned into a gene-editing tool now used in medicine. Anthropic chief Dario Amodei, who recently called for slowing AI development because of its risks, cited the discovery on Wednesday in remarks to the U.N. Security Council, which he addressed by video link. The finding is "preliminary," he said, but "may constitute a new gene editing mechanism that could have applications in gene therapy." He predicted "profound progress in the near future on diseases that have plagued humanity for millennia." The function of the newly identified system, dubbed ART, remains unknown, Anthropic said. The company added that its initial experiments suggest the system could, like CRISPR, be programmable. The results, published as a preprint, have not yet been reviewed by other scientists. Feng Zhang, one of the pioneers of CRISPR and a professor at MIT and the Broad Institute, called the identification of the repeated DNA fragments "genuinely intriguing" and said it "merits further investigation" in remarks released by Anthropic. Amodei also said on X that a team at Stanford University had independently desc…

Anthropic says Claude found new enzyme system ART that may point to a new gene-editing mechanism

Thu, 24 Sep 2026 05:00:00 +0000

Anthropic touts AI-led biology discovery - Anthropic's AI model Claude discovered a new enzyme system called ART by analysing DNA sequences, potentially revealing a new gene-editing mechanism similar to CRISPR. - The AI autonomously analysed over 200,000 genes in 21 hours, accelerating research that usually takes weeks or months. - The discovery, still preliminary and unreviewed, could lead to advances in gene therapy and disease treatment, with experts calling for further investigation. AI generated SAN FRANCISCO – US artificial intelligence start-up Anthropic on Sept 23 unveiled the first discovery from its molecular biology laboratory, a previously unknown enzyme system spotted by its Claude model and touted by the company as proof that AI can speed up basic research. Enzyme systems found in nature have led to some of biology’s biggest breakthroughs, from the tools that launched the biotech industry to the DNA-copying method behind many medical tests. According to the company, some 950 Claude-powered software agents combed through a database of DNA sequences for 21 hours. The agents found more than 200,000 genes for one type of enzyme and picked out 20 worth a closer look – work that would take an expert scientist weeks or even months. Anthropic said its researchers’ involvement was limited to the initial instructions and the lab experiments, with the AI agents working autonomously to decide which leads to pursue. One of the agents spotted something in viruses that infect bacteria that scientists appear to have missed: a string of short, repeating pieces of DNA sitting next to two genes, one for a known enzyme and one for a mystery protein. The set-up looks like CRISPR, a natural defence system in bacteria that scientists turned into a gene-editing tool now used in medicine. Anthropic chief Dario Amodei, who recently called for slowing AI development because of its risks, cited the discovery on Sept 23 in remarks to the UN Security Council, which he addressed by video link. The finding is “preliminary”, he said, but “may constitute a new gene-editing mechanism that could have applications in gene therapy”. He predicted “profound progress in the near future on diseases that have plagued humanity for millennia”. The function of the newly identified system, dubbed ART, remains unknown, Anthropic said. The company added that its initial experiments suggest t…

Tapio Schneider on understanding and trust in AI for mathematics, science, and engineering

Thu, 24 Sep 2026 02:06:58 +0000

[This is a guest post by Tapio Schneider . This blog post was initially written in a different file format and converted using AI. — T.] [This will be cross-posted on the CliMA blog .] The apparent proof of finite-time blow-up of the forced Navier-Stokes equation, announced by OpenAI on September 8, has brought into focus a debate about the role of AI in mathematics. The proof was produced with a system of some 10,000 AI agents that explored many approaches in parallel; it was then formalized and verified in Lean. The formal verification supports its correctness, but mathematicians are still working to digest it. A proof settles the truth value of a statement, but Terry Tao and others have argued that this is only part of the point of proofs; the other part is to advance our conceptual understanding of “ basic structures of shapes, numbers, and natural phenomena .” As Yehuda Rav put it a quarter-century ago, “ theorems are the headlines, proofs are the inside story .” A proof that is correct but incomprehensible, or undigested by the mathematical community, gives us the headline without the story. The issue is not whether the prover is a human or a machine, but whether the reasoning advances our collective understanding of methods and structures, which can spur new thinking and further advances. As Timothy Gowers has noted, the inadequate AI-generated write-ups of proofs are likely a temporary annoyance. I want to argue that the same point about understanding holds in the natural sciences and engineering, where it also has intrinsic value and, in addition, acquires instrumental value when predictions must be trusted before they can be verified empirically. One role of science is what ancient philosophers called episteme , roughly explanatory understanding; here, understanding is the goal itself. Another role is techne , roughly the craft of predicting and making: forecasting how natural or engineered systems will behave under circumstances not yet observed. Whether the two roles can be separated depends on how easily predictions can be checked. When they can, techne can stand on its own. A black-box AI weather prediction model can predict tomorrow’s weather, and we can trust it because its forecasts can be checked every day. Techne can then also serve episteme as an instrument. For example, AlphaFold predicts 3D protein structures without providing explanat…

Where Does Retrieval-Based Open-Ended Evaluation Fail? Automatic Taxonomy Induction from Long-Form Medical Answer Factuality Verification

Thu, 24 Sep 2026 00:00:00 +0000

Retrieval-based factuality evaluation, where LLM-generated claims are verified against evidence from authoritative medical corpora, has become the dominant paradigm for scalable hallucination detection in high-stakes clinical settings. Despite the urgency of reliable and transparent medical fact verification, most systems measure performance with aggregate metrics like F1, which obscure where and why failures occur. Existing RAG diagnostics require gold answers or annotated gold evidence, neither of which exists in this regime. We introduce two comprehensive taxonomies, grounded in a case study on the open-ended MedExpert dataset and 3 closed-ended datasets, decomposing failures into retrieval-stage errors along five quality dimensions, and verifier-reasoning errors into six consecutive steps. We adapt an automatic pattern induction pipeline using LLM-as-Judge to label evidence quality and classify verifier reasoning errors at scale, and then stress-test our findings across 4 retrieval methods and 6 frontier verifier models. Our analysis reveals that scaling model size, adding reasoning effort, expanding to authoritative web sources, and applying medical fine-tuning do not resolve these failure modes, demonstrating that they represent fundamental limitations of the retrieve-then-verify paradigm in open-ended medical settings rather than artifacts of outdated systems. We release our code and data at https://anonymous.4open.science/r/Medical_RAG_eval-4AB5 for the full reproducibility of our results.

FutureHouse and Edison Scientific Assemble 12 Millennium Problems for Biology

Wed, 23 Sep 2026 22:46:50 +0000

Drawing inspiration from the Millennium Prize Problems in mathematics, Edison Scientific and FutureHouse have assembled a set of Millennium Problems for Biology. Each problem is intended to be very difficult to solve, but easy to validate in a simple laboratory environment. For this reason, grand challenges like “Solve Aging” that are very important and challenging but very hard to validate are excluded. Solving any one of these problems would represent a major advance in biology or biotechnology, and most of them would contribute materially towards curing disease. These are, in some sense, the “last reasonable eval” for AI in biology. The initial list of 12 problems was developed by Sam Rodriques and Michaela Hinks, with feedback from the FutureHouse and Edison Scientific teams. You can read more about each on the Millennium Problems for Biology webpage. - Origins of life - Cryopreservation - The reverse translatase - Improve Rubisco - The quadruplet cell - Somatic limb regeneration - Bacterial production of gene therapies - Programmable proteases - Cell-penetrating protein binders - Protein amplification chain reaction - 5' polymerases - New nitrogenases This is a living project maintained by the FutureHouse team and may be refined or expanded as science advances. We welcome your input – submit your comments on the current list of problems or propose new ones through our submission form. Stay tuned for further updates in the coming months as we iterate on the initial set. Questions can be directed to millenniumproblems@futurehouse.org.

Claude discovers a novel enzyme system with CRISPR-like repeats

Wed, 23 Sep 2026 16:06:00 +0000

Science Sep 23, 2026 We’re introducing a new life sciences research group and laboratory at Anthropic. Our focus is on fundamental biology research using Claude: exploring datasets of DNA to identify uncharacterized protein families, generating hypotheses at scale, and testing them through experiments in the lab. This post introduces the team behind this work and shares early results in which Claude discovered a novel enzyme system with properties reminiscent of CRISPR, with only high-level direction from our scientists. Many discoveries that have revolutionized biology and medicine started with a scientist noticing something odd in the staggering diversity of molecular machines found in nature. Restriction enzymes, proteins that cut DNA at specific short sequences, were found in bacterial immune systems, where they destroy the DNA of invading viruses. Researchers realized they could use these enzymes to cut DNA at chosen places and splice genes from one organism into another, which launched the biotechnology industry. Taq polymerase, an enzyme that copies DNA at high temperatures, was identified in a bacterium in a Yellowstone hotspring. It became the basis for PCR, the DNA-copying method used in much of modern diagnostics. CRISPR was first noticed as an unusual repeat sequence in the DNA of certain bacteria, and is now the foundation of gene editing-based medicines. In the spring of 2026, we formed a research group to see whether general AI models can systematize and accelerate such discoveries. We believe that this acceleration will come from establishing a new way of doing biology research, in which agents collaborate with humans in every step of the process. Developing this new way of working required that we build our own lab and a single team working on everything from training Claude in biology to running experiments in the lab. Today, we’re sharing early results from one of our first research programs, in which Claude autonomously discovered a novel enzyme system that is associated with an array of DNA repeats, a pattern reminiscent of CRISPR. Although we don’t yet know its function, the system that Claude discovered has a set of characteristics that have only ever been found together in a handful of other systems, all of wh…

Bio-security Is an AI Arms Race: Radical Numerics CEO Eric Nguyen on Genomic Language Models

Wed, 23 Sep 2026 13:27:18 +0000

Radical Numerics co-founder Eric Nguyen discusses genomic language models and biosecurity. He helped develop Evo and Evo 2; a separate Arc/Stanford team later used those models to generate and synthesize functional bacteriophage genomes. The interview distinguishes three sequence scales: an average human gene is about 60K, long genes can reach 2.3M, and the whole human genome is around 3B. Nguyen argues that models which increase biological capability can also help defense keep pace. The 2.3M figure describes a long gene, not the whole genome.

Guiding large language models to predict edit sequences for molecular synthesizability optimization

Wed, 23 Sep 2026 00:00:00 +0000

Nature Machine Intelligence, Published online: 23 September 2026; doi:10.1038/s42256-026-01304-x Computational drug design often generates molecules that cannot be synthesized. Li and Lai show how large language models can predict precise structural edits to fix these molecules, outperforming traditional methods and preserving key features.

John Platt on AI for Science: Google's ERA and the Scoreable Task

Tue, 22 Sep 2026 21:07:39 +0000

How often do you get to talk to a guest who has both an Academy Award and who invented textbook machine learning algorithms? John Platt has an Oscar , two textbook algorithms , two named asteroids, and an Erdos-Bacon number of 6. This was easily the most fun bio of all the guests we’ve read to date. And the result was an epic and fun chat covering Google’s Empirical Research Assistance (ERA), how AI can help battle climate change, and tons of great stories about the co-evolution of science and AI. John’s colleague Dave Bacon likes to tease John that his career has been defined by being twenty years early to the next big thing. This may be convolutional neural networks (some credit him with coining the term), fusion research, quantum computing. John and Google have been working on solving some of humanity’s hardest problems with AI and computation for well over a decade now. Recently John and his team set their sights on using AI to solve any scientific problem that can be written down as a score. Google’s Empirical Research Assistance (ERA) John’s team has taken on many hard scientific problems over the years. In solving these, they noticed a pattern, many scientific problems can be reduced to what John calls a “scoreable task”. Once you have the score function, the goal is to find some code that maximizes the score. The hard part is in formulating the score, but once you have the score finding the maximizer can still be quite a lot of effort. John’s team set out to automate solutions to this general problem. This came out of the idea of an “auto-Kaggle” AI, which can solve any Kaggle problem you can throw at it. Kaggle is owned by Google, so all the data was ready and easily available to them! The result is Google’s Empirical Research Assistance or ERA ( paper , github , blog ). 1 ERA is surprisingly simple conceptually. Gemini (or your LLM of choice) keeps a running tree of past experiments (notebooks) and where they’re going. It’s a close cousin of Monte Carlo Tree Search : at each iteration the Upper Confidence Bound rule picks which notebooks are most promising to mutate. This is optimistic, not greedy, so sometimes even the fifth-best notebook gets chosen. Gemini then proposes mutations for each one, about ten at a time. The history of each branch is shared, so different leaves can learn from each other. “It’s almost like having a hyper-eager grad stu…

XtalPi AI Enables Seven-Month PCC Nomination for Viva-Thera's MPER-Targeting HIV-1 Program

Tue, 22 Sep 2026 19:29:18 +0000

**media[1229066]**Viva-Thera makes its public debut with four modalities pursuing HIV prevention, treatment and cure, supported by XtalPi's platform capabilities.Viva-Thera combines its first atomic-resolution MPER-TMD-CT structure with XtalPi's AI and automation platform to advance a small-molecule PCC.MPER vaccine immunogens elicit antibody levels approximately five times those of the study comparator in rhesus macaques; antisera retain antiviral activity at a 1:1,000 dilution.BOSTON and SHENZHEN, China, Sept. 23, 2026 /PRNewswire/ -- XtalPi (HKEX: 2228) announced that Viva-Thera, a biotechnology company it incubated and backs as a strategic shareholder, has nominated a preclinical candidate (PCC) targeting the membrane-proximal external region (MPER) of HIV-1 gp41. The partners completed discovery and optimization in seven months by combining Viva-Thera's atomic-resolution structural research with XtalPi's AI drug design and automation platform. In its public debut, Viva-Thera also reported functional vaccine results in rhesus macaques and progress in targeted gene delivery. The company is pursuing HIV prevention and cure through four modalities-vaccines, long-acting small molecules, gene therapy and cell therapy-with XtalPi providing computational design, predictive modeling and experimental automation across the portfolio.

Anthropic’s Biology Lab: Secondary Robotics Report and Official Lab-Work Clarification

Tue, 22 Sep 2026 09:47:13 +0000

This September 22 secondary report describes Anthropic’s Bay Area biology laboratory and reports plans for Claude-directed robotic experiments. In its September 23 announcement, Anthropic explicitly states that all lab work in the laboratory it describes is performed by human scientists. Claude searches genomic data, proposes candidate systems and assists analysis. The two sources differ in date and claim; the earlier report does not establish an operating autonomous robotic laboratory. The function of the ART enzyme system described in the official announcement remains under investigation.

First-in-Human Phase I Data for Insilico's AI-Designed Pan-TEAD Inhibitor ISM6331 to Be Presented at ESMO 2026

Tue, 22 Sep 2026 07:00:00 +0000

Rapid Oral presentation will report initial Phase I data for ISM6331 in patients with advanced solid tumors FDA Fast Track- and Orphan Drug-designated program reflects Insilico’s strategy to advance AI-designed medicines against difficult-to-treat, age-associated diseases CAMBRIDGE, Mass. — September 22, 2026 — Insilico Medicine ("Insilico"; HKEX: 3696), a clinical-stage generative artificial intelligence (AI)-driven drug discovery company, today reminded the oncology community that initial Phase I data for ISM6331, its investigational pan-TEAD inhibitor, will be presented in a Rapid Oral session at the European Society for Medical Oncology (ESMO) Congress 2026, taking place October 23–27 in Madrid, Spain. The presentation will provide the first reported clinical findings from the global, multicenter first-in-human study of ISM6331 in patients with advanced solid tumors. ISM6331 is designed to inhibit all four TEAD family transcription factors and disrupt tumor-promoting signals driven by dysregulation of the Hippo pathway, a central regulator of cell growth, survival, tissue homeostasis and regeneration. Presentation details Title: First-in-human Multicenter Phase 1 Study of ISM6331, an AI-Designed Pan-TEAD Inhibitor, in Patients with Mesothelioma or Other Advanced Solid Tumors Abstract number: #997 Session: Rapid Oral Presentation: Developmental Therapeutic Format: Rapid Oral presentation Presenter: Melina E. Marmarelis, MD, Hematology and Oncology, Abramson Cancer Center, University of Pennsylvania Date: Sunday, October 25, 2026 Time: 8:30–10:00 a.m. CEST Location: Cordoba Auditorium – Hall 4 “The ESMO presentation is an important opportunity to share the first clinical findings from ISM6331 with the global oncology community,” said Halle Zhang, PhD, Vice President, Clinical Development – Oncology at Insilico Medicine. “Patients with advanced mesothelioma and other Hippo pathway-dysregulated tumors have a significant unmet medical need. We look forward to presenting the emerging safety, pharmacokinetic and antitumor findings from this first-in-human study and discussing the next stage of the program.” From generative chemistry to clinical validation Insilico nominated ISM6331 as a preclinical candidate in June 2023. Its novel, non-covalent scaffold was generated with support from Chemistry42, the company’s generative chemistry platform, using structure-b…

Intuitive enzyme design with LLM agents

Tue, 22 Sep 2026 00:00:00 +0000

A collaborative system of large language model agents brings protein engineering closer to natural-language interaction, lowering the barrier between human intent and biomolecular design. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription 27,99 € / 30 days cancel any time Subscribe to this journal Receive 12 digital issues and online access to articles 111,21 € per year only 9,27 € per issue Rent or buy this article Prices vary by article type from$1.95 to$39.95 Prices may be subject to local taxes which are calculated during checkout References - Shao, Q. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-026-01049-y (2026). - Wiesinger, J., Marlow, P. & Vuskovic, V. Agents. Kaggle https://www.kaggle.com/whitepaper-agents (2025). - Ma, Z. et al. J. Chem. Inf. Model. 65, 6599–6612 (2025). - Ferruz, N., Schmidt, S. & Höcker, B. Nat. Commun. 13, 4348 (2022). - Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. Cell Syst. 14, 968–978.E3 (2023). - Ghafarollahi, A. & Buehler, M. J. Digit. Discov. 3, 1389–1409 (2024). - Ponnapati, M. et al. ProteinCrow: a language model agent that can design proteins. In Generative AI for Biology Workshop, Int. Conf. Machine Learning https://icml.cc/virtual/2025/51218 (ICML, 2025). - Durumeric, A. E. P. et al. J. Chem. Inf. Model. 66, 5721–5735 (2026). - Dolorfino, M. et al. Assessing the generalizability of machine learning and physics-based methods with DNA-encoded libraries. Preprint at bioRxiv https://doi.org/10.64898/2026.04.18.719394 (2026). Ethics declarations Competing interests The author declares no competing interests. Rights and permissions About this article Cite this article Sztain, T. Intuitive enzyme design with LLM agents. Nat Comput Sci (2026). https://doi.org/10.1038/s43588-026-01052-3 - Published: - Version of record: - DOI: https://doi.org/10.1038/s43588-026-01052-3

Computational active materials and embodied intelligence in extreme conditions

Tue, 22 Sep 2026 00:00:00 +0000

Nature Computational Science, Published online: 22 September 2026; doi:10.1038/s43588-026-01051-4 Embodied intelligence has enabled machines to act autonomously, yet intelligence remains largely confined to digital models, leaving robotic bodies as passive executors. In extreme environments, such as the human brain, where tissues are mechanically and thermally sensitive, conventional electronics can become limiting. Here, we discuss how active materials, which embed intelligence directly within their structure, can expand robotic capabilities in these settings, with a particular focus on the role of computation-aided design.

Recursive self-improvement of AI research agents

Tue, 22 Sep 2026 00:00:00 +0000

AI agents are beginning to automate research and development across the AI stack, from improving training efficiency to optimizing inference. A natural next step is to improve the research efficiency of the agents themselves. When an AI research agent's own code is the object of optimization, each accepted rewrite becomes the agent that the next round edits. We refer to this loop as recursive self-improvement. Its significance lies in a long-standing trend, in which increased cumulative spending on R&D yields diminishing returns. Sustained self-improvement offers a way to counter this trend. We present AIDE^2, a system that implements this loop for a frontier AI research agent. It proposes changes to its own code, benchmarks modified versions of itself on a suite of AI R&D tasks, and keeps the changes that perform best on hidden evaluations. In an autonomous 8-day run, AIDE^2 discovered seven successive improvements, ranging from a new search policy to memory mechanisms that compress and manage the agent's growing context. These gains generalize to four held-out benchmarks spanning machine learning engineering, heuristic algorithm engineering, and physics-based weather forecasting, the last of which is out of distribution from the selection tasks. On all four, the strongest discovered agent matches or exceeds a human-engineered production research agent that ranks among the strongest on FML-Bench. On a separate held-out task family, the discovered agents also exhibit reduced reward hacking, a property the loop never explicitly optimized for: the rate falls from 55% to 32% during the run, 7 percentage points below the human-engineered agent. Together, these results show that an AI research agent can improve its own research efficiency through recursive self-improvement, and that these gains transfer to tasks and domains the loop never encountered.

Did OpenAI Solve the Wrong Navier-Stokes Problem?

Mon, 21 Sep 2026 07:00:00 +0000

Two weeks ago OpenAI claimed a solution to one of the biggest open problems in math—the Navier-Stokes problem—an achievement worth a $1-million prize from the Clay Mathematics Institute. The proof ignited a powder keg of concern over artificial intelligence companies’ race to disrupt the subject. But with the dust still far from settled, a different controversy is emerging: Did OpenAI even solve the right Navier-Stokes problem? Generated by an internal large language model (LLM), OpenAI’s proof relies on an approach that many experts find unnatural. It solves a variant of the problem that mathematicians say is disconnected from reality and thus less interesting. In a sense, the LLM found and exploited a loophole in the framing of the question. On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. “The most important problem is unsolved,” says Luis Silvestre, a mathematician at the University of Chicago. “The Clay problem is settled, but the main problem for the Navier-Stokes equations is not.” Furthermore, last Thursday, three mathematicians posted a proof of their own that showed that OpenAI’s method can never be extended to solve the full problem. In other words, the loophole will never be closed, barring some completely new idea. The Navier-Stokes equations are supposed to describe how fluids flow, but mathematicians doubt whether they can always be trusted. The million-dollar problem is about whether the equations ever “blow up,” which would mean they’d allow the flow to be infinitely fast at points—something that can’t happen in the real world. But there’s one piece of the equations that’s optional—sometimes it’s there; sometimes it isn’t. The “it” here is an external force such as gravity that affects how a fluid moves. “All fluids we know of are under some kind of external force,” says mathematician Diego Córdoba. “So to have the force makes complete sense.” When most experts think about the Navier-Stokes problem, though, it’s without this force. They want to find a purer, more fundamental way for the equations to blow up by using only the intrinsic forces within any fluid—not by applying some specific, precisely contrived external f…

EdgeGen: Improving Tool-Calling Agents Beyond Happy Paths with Synthetic Edge Case Generation

Mon, 21 Sep 2026 00:00:00 +0000

Tool-calling LLM agents are increasingly deployed in enterprise applications. However, effective evaluation and optimization require high-quality, diverse task datasets that are often difficult to obtain due to privacy and other constraints. Existing synthetic task generation methods often produce generic tasks that ignore an agent's underlying state or database and fail to reflect real-world usage diversity. We propose EdgeGen, a synthetic task generation framework that extracts compliance rules from an agent's specification and uses them to generate database-grounded edge-case tasks designed to violate these rules. When combined with existing synthetic data generation techniques, EdgeGen enables agent improvement through finetuning and harness optimization. The resulting pipeline forms a fully automated closed-loop system that requires no human annotation. Finetuning on data generated by EdgeGen yields a consistent mean progress improvement of 2 percent to 42 percent on tau2bench airline domain, while other baseline methods show degradation for some models. On the other hand, for harness optimization, our method shows a mean progress improvement of 10 percent and 30 percent over the human-curated and base harnesses, respectively, for the Gemma-4-e4b model.

Emergent Collusion in Long-Horizon LLM Agent Interaction

Mon, 21 Sep 2026 00:00:00 +0000

LLM agents are increasingly deployed in collaborative settings, yet long-term interaction may give rise to undesirable coordination. We study the emergence of collusion in a long-horizon multi-agent environment: two agents repeatedly complete individual tasks, share task logs, verify each other's work, and receive rewards. We introduce realistic constraints that make compliance with the verification protocol incompatible with reward maximization, and find that agents increasingly deviate from the protocol over repeated interactions. Collusion emerges in 94% of trajectories across 10 models, and more capable models within the same family reach it earlier. Controlled peer interventions show that collusion is shaped by peer behavior, while ablations reveal additional effects of reward structure, the verification feedback agents receive, and their interaction history. In particular, restricting the amount and scope of interaction history available to agents reduces collusion. Overall, our findings show that long-horizon interaction can reshape how agents coordinate in ways that create safety risks.

SAIR Announces Open Model Initiative for the Mathematical Community

Fri, 18 Sep 2026 15:06:20 +0000

Last year I co-founded the Foundation for Science and AI Research (SAIR) with some private donors to create a non-profit organization that could support responsible uses of AI in mathematics and the other sciences, independent of the major AI companies. Initially, our resources were rather limited, and our main activities consisted primarily of podcasts , short events , and competitions . However, we have been slowly trying to expand in scale, ambition, and logistical capacity; for instance, we were very grateful to receive recent support from XTX markets and Susquehanna to fund our next round of SAIR competitions. Furthermore, we have been negotiating with several key partners from academia and industry towards an initiative to build open weight models for science, as well as open source tooling for using both open and closed source LLMs for scientific applications. We had initially planned a gradual rollout of this initiative, starting with a few pilot projects over the next few months, but given current events and the high demand for open models, we are accelerating our schedule and moving our initial announcement of the initiative to today , despite the fact that some key planning is still underway. In particular we are now ready to collect expressions of interest in the initiative, as we are seeking partners who can contribute funding, compute, expertise, or community building to our efforts. (Expressions of interest can be registered at the bottom of the announcement page .) I have replicated the text of our initial announcement below: we plan to provide more updates and information on the initiative shortly, including an announcement of our partners, as well as future plans to integrate this initiative into our existing portfolio of podcasts, events, and competitions. Building Open Models for the Mathematical Community An Invitation from SAIR Foundation Mathematics advances through discoveries that others can examine and build upon. Generations of researchers have created a shared body of knowledge, sustained by open exchange and respect for each other’s contributions. As AI becomes part of mathematical research, these principles should shape the tools we use. SAIR and its partners are commencing an initiative to be at the forefront of shaping and implementing these new tools. Let’s work together to build Math 2.0. Models Shaped by the Community Thro…

Insilico Medicine's Alex Zhavoronkov bets China and AI can deliver the drug industry's next breakthrough

Fri, 18 Sep 2026 07:00:00 +0000

Alex Zhavoronkov, co-CEO of the AI drug discovery startup Insilico Medicine, dresses the same way at all his public engagements: a black padded vest with a small white badge that keeps count of the company’s clinical pipeline. When Fortune sat down with him in Hong Kong in July, he made sure to point out the badge’s latest revision: a small circle beside “Phase 3.” The badge is a provocation as much as a point of pride. “My China team loves it,” he said. “In other geographies, they absolutely hate it, because displaying something like this means you have to compete.” Just a few days earlier, Insilico had announced that rentosertib, its drug for idiopathic pulmonary fibrosis, a fatal lung-scarring disease that leads to breathlessness and coughing, would start Phase 3 trials in China. Insilico used AI to both identify the disease target and develop the molecule to treat it; it claims it’s the first such AI-discovered drug to make it to large-scale clinical trials. (The first patient in the trial was dosed on Sept. 10.) The news has only kept building from there. On Sept. 7, Insilico published a study in Nature Biotechnology reporting that some blood samples from 42 rentosertib patients showed signs of reversed biological age. Insilico executives cautioned that the effect is small, and may not be sustained over a longer period of time. Yet it’s still among the first clinical hints that a drug invented by AI might slow, or even reverse, aging. “We believe aging is related to fibrosis. When people get old, there’s more and more fibrosis happening,” co-CEO Feng Ren told the Fortune Leaders Forum in Macau on Sept. 8, the day after the study’s publication. (Fibrosis refers to an excessive buildup of scar tissue). “ If we can stop the fibrosis, then we might have a chance to stop biological aging.” Insilico is also trying to shape how AI’s usefulness in longevity research gets measured. On Sept. 17, it launched LongevityBench, a test of large language models’ grasp of aging biology; in a study featured on the cover of Cell, the company said smaller, aging-trained models beat several frontier systems on the new metric. That leaves Insilico running the drug industry’s most consequential experiment–or, more accurately, three of them. First, whether an AI-discovered molecule can survive a Phase 3 trial; second, whether AI drug discovery can make money; and finally, whet…

A Lie Detector Test for Language Models: Reading Knowledge a Model Won't Reveal

Fri, 18 Sep 2026 00:00:00 +0000

Large language models can hold knowledge they do not report. A model may sandbag on a capability evaluation, or answer against what it internally knows, and its outputs alone cannot tell whether it is hiding an answer or simply does not have one. We borrow the Concealed Information Test, a forensic method that identifies guilty knowledge by presenting a suspect with the true detail among plausible decoys and measuring a stronger response to the item they recognize. Our method, Probe of Internal Recognition (PIR), does the same inside a model. It presents a question with its candidate answers and reads, from the model's internal states, which candidate the model recognizes as correct. PIR is reference-free, needing no honest reference model and no labeled truth corpus. Across eight models from five families (Gemma, Qwen, Llama, Mistral, and Phi), PIR recovers the recognized answer at 0.70 to 0.87 balanced accuracy, well above the 0.28 to 0.40 unknown-item baseline and the 0.25 chance rate. It stays readable across every form of concealment we test, from prompted deception and trained sandbagging to external password-locked and circuit-broken checkpoints, with recognition between 0.85 and 0.93. When the model hides a known answer, recognition stays high. When unlearning removes the knowledge, recognition drops to the level of a question the model never knew. PIR therefore separates a model that will not answer from one that cannot, which supports sandbagging audits and unlearning verification. The signal is causal, adds information beyond black-box behavioral cues, and extends from multiple-choice questions to free-form generation.

Grid2Text Dataset Aligns Meteorological Grid Data with Expert Reasoning Chains

Fri, 18 Sep 2026 00:00:00 +0000

Abstract Accurate meteorological forecasting is fundamental to public safety and economic stability, but it heavily relies on numerical weather prediction (NWP) models, whose complex outputs are often inaccessible to non-experts. Although large language models (LLMs) show potential in translating structured data into natural language to automate forecast reporting, progress has been constrained by a critical scarcity of datasets that bridge the modality gap and capture the implicit reasoning of human experts. To address this, we present Grid2Text , a rigorously developed dataset aligning ERA5 meteorological grid features with expert-verified textual forecasts, encompassing explicit reasoning chains for temperature trends, wind vector transitions, humidity ranges, and precipitation types. Furthermore, we establish validation benchmarks derived from a human-in-the-loop workflow to ensure the physical consistency and logical accuracy of the data. We anticipate that this open-access dataset will catalyze the development of interpretable LLM approaches for weather forecast discussions, ultimately advancing the field of scientific text generation.

Large language models as human proxies

Fri, 18 Sep 2026 00:00:00 +0000

Nature Computational Science, Published online: 18 September 2026; doi:10.1038/s43588-026-01060-3 Large language models can potentially serve as human proxies across various roles, enabling nuanced simulations of behavior. This Review categorizes their applications, stressing distinct evaluation methods and validity criteria essential for accurate scientific claims and practical implementations.

GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills

Fri, 18 Sep 2026 00:00:00 +0000

Skills can improve the performance of Large Language Model (LLM) agents by providing task-specific procedural guidance, while skill optimization further improves their effectiveness through iterative refinement. However, existing skill optimization methods typically represent skills as unstructured natural-language instructions, creating two key challenges: 1) Unstructured skills often lack explicit workflow-level guidance and contain substantial redundancy, making them difficult for LLMs to execute; 2) the vast search space of unconstrained natural-language skills makes skill optimization ineffective. To address these challenges, we propose representing skills as graph-structured natural-language artifacts. In graph-structured skills, each node represents an execution step together with its operational guidance, while directed edges encode context-dependent transitions between steps. Compared to unstructured skills, graph-structured skills can provide clear workflow-level guidance. Moreover, the proposed graph-structured skill can also facilitate skill optimization. Building on this structured representation, we introduce GraphSkillEvo, a population-based evolutionary optimization framework with mutation and crossover operators for graph-structured skills. By maintaining multiple candidate skills and combining effective components, GraphSkillEvo enables broader and more comprehensive exploration of the structured skill space than purely LLM-based iterative self-refinement. Extensive experiments across five agent benchmarks demonstrate that GraphSkillEvo consistently outperforms the strong skill optimization baseline SkillOpt, improving average accuracy by 4.01% on GPT-5.4-nano and 1.76% on GPT-5.4. Our code is available at https://github.com/ruisun7/GraphSkillEvo.

How Claude is uplifting biomolecular modeling

Thu, 17 Sep 2026 18:44:00 +0000

Claude made the open-source models that scientists use to predict and design biomolecules faster and more memory-efficient. Claude optimized more than 30 of these models in just under four weeks, speeding them up roughly 4x on average. It also created a low-memory mode that enables the accurate prediction of biomolecular systems larger than 10,000 tokens (amino acids, nucleotides, and atoms from small molecules and ions) on a single NVIDIA GPU node.

Insilico Medicine Releases Open Longevity AI Toolkit in Cell Study

Thu, 17 Sep 2026 15:44:02 +0000

AI Models & Platforms Insilico Medicine Releases Open Longevity AI Toolkit in Cell Study Add Unite.AI to your preferred sources on Google Insilico Medicine on September 17, 2026 announced the publication of a study in Cell introducing an openly released AI toolkit for aging biology: the LongevityBench benchmark, a family of five compact Longevity-LLMs, and the Longevity Claw agentic research platform. Publication in Cell The paper, “An open benchmark and language models for AI in aging biology,” appears in Cell volume 189, issue 19, at pages 5980–5994.e8, dated September 17, 2026. It was published open access under a Creative Commons Attribution 4.0 license with the DOI 10.1016/j.cell.2026.08.026, and it lists 13 authors, including Alex Zhavoronkov, Vladimir Naumov, Denis Sidorenko, Alex Aliper, Ramin Hasani, Alexander Amini, Vadim N. Gladyshev and Fedor Galkin. Insilico said the study was selected as the cover feature of the journal’s September 17, 2026 issue and was conducted with researchers from Liquid AI, the Buck Institute for Research on Aging, and Harvard Medical School and Brigham and Women’s Hospital. According to the announcement, the Cell publication follows Insilico’s September 7, 2026 study in Nature Biotechnology, which reported that rentosertib, the company’s AI-discovered and AI-designed drug candidate for idiopathic pulmonary fibrosis, reduced biological age across six independent proteomic aging clocks in a Phase IIa clinical trial. An Open Benchmark for Aging Biology The study introduces LongevityBench as an open suite of 17 tasks spanning five biodata domains: clinical data, genetics, epigenetics, transcriptomics and proteomics. In the paper’s summary, the authors write that no existing benchmark evaluated whether AI systems can interpret these heterogeneous data types in the context of aging biology. According to the announcement, the benchmark was designed to reduce the likelihood that models could succeed through recall of information encountered during training, testing instead the ability to analyze biological data, recognize meaningful patterns and solve problems relevant to aging research. The authors used LongevityBench to assess 18 frontier AI systems from six developer teams, which the announcement identifies as OpenAI, Google, Anthropic, xAI, DeepSeek and Moonshot AI. The paper reports that no single model dominated all tasks…

An expert-level generalist AI for abdominal CT diagnosis

Thu, 17 Sep 2026 13:01:00 +0000

Artificial intelligence (AI) in radiology aspires to deliver expert-level diagnosis across diverse clinical tasks, yet existing supervised strategies remain limited in scope. We developed RADAR, a generalist vision-language model trained on more than 400,000 contrast-enhanced abdominal computed tomography (CT) examinations and 15 million anatomy-wise image-text pairs, learning directly from clinical reports without manual annotation. Throughout internal and external evaluations across multiple centers and varied clinical scenarios, RADAR achieved high diagnostic performance and robust generalization for 18 anatomical structures and 146 imaging findings. In a reader study, RADAR assistance increased the diagnostic sensitivity of 26 radiologists by ~10%. RADAR offers a scalable, versatile, and interpretable solution for abdominal CT, demonstrating that generalist AI can match human experts in general and complicated radiology tasks.

Multi-organelle signatures map cell-state diversity and metabolic adaptation in tissues

Thu, 17 Sep 2026 13:01:00 +0000

Cell-state diversity drives tissue adaptability, repair, and disease resilience, but capturing this complexity is a challenge. Current approaches rely on transcriptional profiling and overlook organelle structure, a key indicator of metabolism and stress. We developed spatial Organellomics (sOrganellomics), an imaging workflow that integrates automated segmentation with machine learning to classify and spatially map cell states from multi-organelle signatures. In liver and pancreas, these signatures distinguished broad cellular classes. In liver, sOrganellomics revealed that zonal position did not fully explain organelle-defined hepatocyte categories. Instead, hepatocytes formed intermixed communities within canonical zones, supporting a refined subzonal diversity model. Nutritional stress reshaped this organization. Intravital imaging linked fasting-induced organelle remodeling with altered mitochondrial membrane potential in vivo, supporting multi-organelle architecture as a structural readout of tissue adaptation.

What a crowdsourced game revealed about steering Olmo 3

Thu, 17 Sep 2026 08:00:00 +0000

AI models need to do more than produce correct answers. How they respond matters too: whether they’re helpful, fair, safe, respectful, and responsive to the people using them. For model builders, the challenge is knowing whether those “prosocial” behaviors hold up in practice—and whether evaluations capture how a model behaves when people interact with it in unexpected ways. Northeastern University MS student Soham Padia used Olmo 3 to test whether crowdsourcing an evaluation of prosocial behavior could expose weaknesses that a small research team might miss. Padia had developed an evaluation that measures how strongly text steers a model toward more prosocial responses. Steering Arena turned that evaluation into a sort of game—players submit short text prefixes designed to influence the model, see how strongly each one shifts Olmo 3 in that direction, and compete for the top spot on the leaderboard. Olmo’s openness made the project possible—Padia could see how submitted text changed Olmo 3’s internal activity instead of inferring those effects only from the responses it generated. That access became the foundation for both his evaluation and Steering Arena. From open access to a public challenge Padia chose Olmo 3-32B so he could study prosocial steering in a relatively large model. Through the National Deep Inference Fabric (NDIF), a U.S. National Science Foundation (NSF)-supported platform for experimenting with large open models, he could access Olmo 3-32B remotely without owning the GPUs needed to host it himself. That effort to make advanced AI research more accessible aligns with Ai2’s work with NSF. Through the OMAI project, Ai2 is developing fully open models and infrastructure designed to help more researchers study, reproduce, and build on sophisticated AI systems. "Open weights alone would not have been enough," Padia says. "Olmo documents its data and its post-training, so when I find a prosocial direction inside it I know whether I am looking at something the pretraining put there or something a later fine-tune installed. On most models, that question simply has no answer." Padia’s evaluation uses 135 pairs of contrasting text responses spanning 15 qualities, including empathy, fairness, safety, privacy, and respect. (Each pair starts with the same prompt and contrasts a more prosocial response with a less prosocial one.) By comparing the mode…

Insilico Medicine's Abu Dhabi Team Reports Novel BTK Degraders in Journal of Medicinal Chemistry

Thu, 17 Sep 2026 07:00:00 +0000

Research published in the Journal of Medicinal Chemistry showcases a novel PROTAC series from Insilico Medicine UAE, advancing local research in targeted protein degradation - Insilico Medicine AI R&D Center in Abu Dhabi adds a new discovery milestone to the company’s UAE operations, with research on BTK degraders for potential applications in blood cancers. - Chemistry42 supported the design of novel molecular building blocks, yielding lead compounds ISM-PR25 and ISM-PR44 with picomolar BTK degradation activity and oral bioavailability in mice. - The research builds on the UAE-based discovery of PRMT5 inhibitor ISM0387, demonstrating the breadth of Insilico’s local drug discovery capabilities. ABU DHABI, UAE, September 17, 2026. Insilico Medicine (“Insilico”, HKEX: 3696), a clinical-stage tech-bio company powered by generative artificial intelligence, today highlighted novel BTK degraders discovered by its UAE team as part of its growing research presence in the Middle East. First published online in the Journal of Medicinal Chemistry (a premier international journal in medicinal chemistry) on August 14, 2026, the study represents a scientific milestone for Insilico Medicine UAE and demonstrates its capabilities in targeted protein degradation. “Our ambition in Abu Dhabi is to build a team that can originate new medicines and contribute to international drug discovery,” said Alex Aliper, PhD, President of Insilico Medicine. “Following our UAE-based discovery of the PRMT5 preclinical candidate, this BTK degrader publication demonstrates the range of scientific problems our local team can tackle. It is another step in building lasting drug discovery capabilities here in the UAE.” Developing a new approach to BTK drug discovery Blood cancers such as chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL) affect B cells, a type of immune cell. Bruton’s tyrosine kinase (BTK) supports signaling that helps these cancer cells grow and survive, making it an established therapeutic target. Although BTK inhibitors have improved treatment options, resistance mutations can limit their effectiveness. Proteolysis-targeting chimeras (PROTACs) offer a different mechanism: they recruit the cell’s protein degradation machinery to remove BTK. Developing selective orally bioavailable PROTACs remains challenging. Insilico’s team used its proprietary generative AI plat…

Insilico Medicine Opens AI Longevity Discovery Toolkit to Researchers in Cell Cover Study

Thu, 17 Sep 2026 07:00:00 +0000

The paper introduces three interconnected resources for the global scientific community: - LongevityBench, the first open benchmark designed to rigorously evaluate AI reasoning across multiple domains of aging biology; - Longevity-LLMs, a family of compact, open-source language models specifically trained on clinical and multi-omics aging data; and - Longevity Claw, an open-source agentic research platform that autonomously integrates specialized longevity tools to identify and prioritize potential therapeutic targets. CAMBRIDGE, Mass., September 17, 2026 — Insilico Medicine (“Insilico”; HKEX: 3696), a clinical-stage biotechnology company powered by generative artificial intelligence (AI), today announced the publication of a landmark study in Cell introducing an open AI toolkit, incluiding LongevityBench, Longevity-LLMs and LongevityClaw designed to accelerate research and therapeutic development in aging and longevity. The study, selected as the cover feature of the journal’s September 17, 2026 issue, was conducted in collaboration with researchers from Liquid AI, the Buck Institute for Research on Aging, and Harvard Medical School and Brigham and Women’s Hospital. The publication follows Insilico’s September 7, 2026 study in Nature Biotechnology, which reported that rentosertib — Insilico’s AI-discovered and AI-designed drug candidate for idiopathic pulmonary fibrosis — reduced biological age across six independent proteomic aging clocks in a Phase IIa clinical trial. Together, the two studies connect clinical evidence from an AI-discovered therapeutic with an open research infrastructure intended to help scientists identify and develop the next generation of longevity interventions. “Insilico is at the forefront of longevity research. Our recent work on rentosertib, published in Nature Biotechnology, demonstrated that an AI-designed drug can modulate biological aging signatures. In our Cell cover paper, we build on this work by empowering the global scientific community with tools such as LongevityBench, Longevity LLM and Longevity Claw, helping transform AI into an autonomous engine for longevity discovery. The longevity community is moving beyond static aging clocks toward foundation models capable of generating measurable, actionable insights. We are developing benchmarked, agentic systems that can evolve into personalized longevity assistants and lo…

ScienceIDE: Turning the World's Scientific Codebase into Agent-Learnable Environments

Thu, 17 Sep 2026 00:00:00 +0000

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments 摘要 Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conventions, and specialized correctness criteria make this knowledge difficult to convert into reliable learning experience—a challenge we call the scientific experience bottleneck. We introduce ScienceIDE, infrastructure for turning the world’s scientific code into programmable environments for scientific agents. Guided by expert-defined scientific cases and acceptance criteria, agents transform repositories into executable environments that support task generation, execution, and scientific verification. These environments provide a shared foundation for supervised fine-tuning, reinforcement learning, and evaluation. Using verified interaction trajectories, we train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities. ScienceIDE lays the foundation for an integrated workspace for agent learning and scientific practice, making humanity’s scientific software a shared substrate for developing scientific intelligence. 作者 * 共同一作期刊/会议引用 BibTeX @article{geng2026scienceide, title={ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments}, author={Geng, Hejia and Huang, Zesen and Li, Haoyang and Li, Wenbin and Wu, Koutian and Zhou, Zihan and Pang, Yuanbo and Liu, Weihao and Xu, Zigong and Li, Zhiping and Zhang, Zongzheng and Dong, Chuanfei and Sun, Jiankai and Zheng, Tianzhe and Xie, Fengyu and Ma, Yue and Shi, Yueheng and Wang, Junkai and Xie, Tong and Di, Zonglin and Liu, Xianrong and Gao, Qucheng and Liu, Yimin and Pan, Jiaming and Huang, Sheng and Ma, Xiao-Han and Yuan, Lanqing and Zhu, Zhenlin and Liu, Ziang and Xu, Ziyang and Liang, Kangkai and Xian, Jiayi and Zhao, Zehong and Ma, Taigao and Chang, Fuming and Hu, Yuanzhe and Xu, Liuwei and Tang, Xianzhe and Xie, Jingxu and Zhang, Peijin and Gao, Qiang and Xing, Chengyi and Zhao, Zhe and Wang, Xi and Wang, Leo and Wu, Fang and Xing, Yaopeng and Meng, Xing and Yin, Zhenfei and Wu, Yingcheng and Yang, Ling}, journal={arXiv preprin…

Revisiting molecular descriptors with TDiMS for interpretable intramolecular interactions based on substructure pairs

Thu, 17 Sep 2026 00:00:00 +0000

Abstract Molecular descriptors play a crucial role in representing the structural features of molecules for machine learning-based physical property prediction. However, current descriptors either consider only local aspects of molecular structures or fail to effectively learn nonlocal structural features involving long-distance intramolecular interactions. Here, to address this issue, we present a descriptor named TDiMS. TDiMS effectively summarizes the enumerated pairwise topological distances between molecular substructures, thus capturing nonlocal interactions. Our evaluation shows that TDiMS successfully identifies essential features of large structures and outperforms other representative descriptors in predicting properties for which distances between substructures are a primary factor. In addition, these identified features are highly interpretable for experts in materials discovery.

OpenAI's Navier–Stokes claim sparks debate over who gets credit in AI-era mathematics

Thu, 17 Sep 2026 00:00:00 +0000

OpenAI’s announcement that it used an artificial-intelligence model to solve a major puzzle in fluid dynamics — the Navier–Stokes problem — is likely to change how mathematics is done forever. The claim, made by the AI company in San Francisco, California, on 8 September, has also sparked controversy about whether OpenAI tools have learnt information from human mathematicians who were also racing to solve the problem using AI tools. The widespread use of AI models across most fields of research, and the difficulty of tracing the origin of the information used to train models, could put the very notion of scholarly credit into jeopardy, researchers warn. “It is quite possible that academic researchers have not fully grasped the consequences of uploading data and knowledge to a personal AI model account,” says Luke McDonagh, who studies intellectual-property law at the London School of Economics and Political Science. In an open letter decrying AI companies’ entry into solving mathematical problems, 25 winners of the Fields Medal — which is often considered mathematics’ equivalent to the Nobel Prize — write that AI tools muddy the ability to give appropriate credit: “As in all creative professions, this raises severe attribution and plagiarism questions.” Chatbot controversy The day before OpenAI confirmed rumours that it had solved the Navier–Stokes problem — one of the highest-profile questions in mathematics — one researcher was already raising the alarm. Tristan Buckmaster, a mathematician at New York University in New York City, and his collaborator Levent Alpöge, a mathematician at Harvard University in Cambridge, Massachusetts, had been working on an aspect of the Navier–Stokes problem using tools from OpenAI as well as Anthropic. They were told that OpenAI was preparing to announce that it had solved the problem. Buckmaster wrote on social media that the company had jumped on the problem after hearing about the work by him and Alpöge, and that OpenAI's model could have been learning from their interactions with ChatGPT. An OpenAI spokesperson told Nature: “After investigating, we can say with full confidence that no user inputs past July 3rd could have influenced this system in any way.” The company said that it started working on the problem on 1 September and that it had not seen “any of their work through any means until they released it publicly”.…

When2Think: Learning Difficulty-Aware Length Control for Efficient Hybrid Reasoning Models

Thu, 17 Sep 2026 00:00:00 +0000

Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and underthink hard ones. Existing approaches based on uniform length penalties or rigid routing incur an efficiency tax, trading reduced computation on easy instances for accuracy loss on hard instances. We formulate efficient reasoning as an instance-adaptive computation allocation problem and propose When2Think, a post-training framework for hybrid reasoning that dynamically allocates computation based on problem difficulty. Our method introduces Instance-level Difficulty-Aware Control (IDAC), a reward-shaping mechanism that leverages pre-computed reference statistics (accuracy and token usage) to regulate reasoning depth. Combined with verifier-based rewards and batch-wise standardized advantages, IDAC enables stable critic-free optimization without learned reward models or online reference-model queries. When2Think encourages direct answering on easy instances while preserving extended reasoning on hard instances, thereby learning when to use System 1 (NoThink) versus System 2 (Think). Experiments on mathematical benchmarks demonstrate improved accuracy-efficiency trade-offs: on AIME24, Pass@3 increases by 10.0% while token usage is reduced by 27.9% relative to the base model, and on AIME25, When2Think achieves 40.0% Pass@3, outperforming compression and routing-only baselines.

When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation

Thu, 17 Sep 2026 00:00:00 +0000

Large language models (LLMs) increasingly participate in scientific evaluation, both as automated reviewers and as assistants to human reviewers. As model-generated reviews enter public data and future training corpora, AI peer review can become recursive: later reviewers learn from judgments produced by earlier models. We study one step of this feedback loop in a controlled setting. Starting from Llama 3.1 8B, we first fine-tune a reviewer on official ICLR reviews from 2018--2023 and then train four successor models on ICLR 2024 data with systematically varied mixtures of official and model-generated reviews. Our study shows that introducing synthetic reviews compresses rating distributions and reduces both same-paper and corpus-level semantic diversity. We call this pattern scientific-judgment collapse. To mitigate this failure mode, we introduce TrustReviewer, an open-source LLM-based system for generating peer reviews of AI and machine learning papers. TrustReviewer intervenes at two complementary stages. For training-time prevention, we train the core reviewer in a single stage on a curated corpus designed to reduce low-quality and semantically degenerate supervision. For test-time correction, paired activation steering aims to further mitigate residual tendencies toward collapsed judgments without further training or additional expert annotation. Together, these results characterize a concrete risk of recursive reviewer training and provide practical interventions for preserving judgment diversity and improving recommendation alignment in AI-assisted scientific evaluation.

JEPA-Anything: Learning Predictive Models across Different Worlds

Thu, 17 Sep 2026 00:00:00 +0000

JEPA-Anything: Learning Predictive Models across Different Worlds 摘要 World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization (OPF). Extending joint-embedding predictive architectures, OPF decomposes latent targets into complementary factors, learns them through dedicated pathways, and recombines them within a shared predictive design. We evaluate JEPA-Anything across seven domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather. Experiments span representation learning, intervention prediction, out-of-distribution generalization, and long-horizon dynamics, including 10 matched dynamics tasks, forecasting of over 1,000 clinical events, and 100-step molecular rollouts across four systems. Against matched JEPA baselines, JEPA-Anything improves reported metrics on all 10 dynamics tasks and reduces single-intervention prediction error on Interventional Pong by 34.8%. It achieves the lowest one-step and 100-step molecular errors among compared methods in all four systems. Beyond prediction, a factor-nominated biological intervention receives experimental support in cell co-cultures, patient-derived organoids, tumor fragments, and mice; latent orbital modes recover the Keplerian scaling exponent with a fitted slope of −1.4991. These results support a common factorized predictive principle across heterogeneous worlds, connecting world modeling with intervention and experimentally grounded scientific discovery. 作者期刊/会议引用 BibTeX @article{cui2026jepa, title={JEPA-Anything: Learning Predictive Models across Different Worlds}, author={Cui, Taoyong and Wang, Zhongyao and Xu, Xinyue and Liu, Weiyang and Yu, Zhaochen and Zhang, Yuying and Gao, Qiang and Yang, Mengyue and Ouyang, Wanli and Heng, Pheng Ann and Wu, Yingcheng and Yin, Zhenfei and Yang, Ling}, journal={arXiv preprint arXiv:2609.20800}, year={2026} }

What Does Privileged Information Add to On-Policy Self-Distillation?

Thu, 17 Sep 2026 00:00:00 +0000

On-policy self-distillation (OPSD) lets a language model learn from a frozen copy of itself that sees an answer or a worked solution. Giving the teacher this extra information seems to offer the student more to learn, but how much does it add beyond distillation itself? To isolate that contribution, we construct AMPLE-Math, a reusable suite of 5,319 mathematical problems with six reasoning views that share the same answer, and compare each view with matched reference-free distillation. With a thinking-enabled teacher supervising direct-response rollouts, reference-free distillation accounts for much of Qwen3-1.7B's improvement under thinking-enabled evaluation, both in domain and on external benchmarks. Evidence for an additional reference benefit is modest in Qwen, strongest for a polished solution, whereas complete traces add two percentage points in SmolLM3-3B at step 50. These benefits depend on the student being trained. At the same checkpoint, replacing short direct-response rollouts with long thinking-enabled rollouts turns gains into losses in both families while the problems, references, and evaluation stay fixed. Teacher profiles and matched loss interventions in Qwen further show that changing token-level supervision can leave student behavior largely unchanged. Together, these findings suggest that OPSD can improve access to existing reasoning capabilities through parameters shared by direct-response and thinking-enabled inference. The value of a privileged reference is what it adds to this cross-mode transfer, not how much of the solution it reveals.

Changing minds: How AI is transforming the life sciences

Thu, 17 Sep 2026 00:00:00 +0000

Artificial intelligence (AI) is changing the way biomedical research is conducted by making it possible to integrate diverse types of biological data and tackle questions that were previously difficult to address. At the same time, its growing use also raises important questions about data quality, reliability, interpretability, and how computational predictions should be combined with biological knowledge and experimental validation. In this Voices piece, researchers from diverse disciplines share their perspectives on how AI is advancing their respective fields and highlight both the transformative opportunities and the key challenges that will shape the future of the life sciences.

How a team of AIs discovered a promising lung-cancer drug

Thu, 17 Sep 2026 00:00:00 +0000

It’s a pharmaceutical-company executive’s dream: tens of thousands of employees working day and night, uninterrupted by meals, sleep or distraction, to find the next blockbuster drug. A sophisticated artificial-intelligence system called the Virtual Biotech, which is described today in the journal Science1, is a step towards this vision. The Virtual Biotech comprises as many as 37,000 agents — AI systems that autonomously interact with large language models (LLMs) or with each other and are capable of performing multistep tasks. It uncovered a molecular signal that could help to predict clinical-trial success. And with some human oversight, it identified a promising lung-cancer treatment. "We want to see how far these agent teams of AI scientists can help us to really accelerate drug discovery and development,” says James Zou, a computer scientist at Stanford University in California who led the effort. But other scientists note that the Virtual Biotech has not been vetted in the crucible of real-world drug discovery, and its predictions were not validated through experiments, let alone clinical trials. Organizational chart In the past year or so, ever-more-capable AI scientists have taken hold in various fields. In biomedicine, they have shown aptitude for complex tasks ranging from genomic data analysis to hypothesis generation and experimental design. To test these systems’ ability to find new drugs, a job that involves numerous interrelated tasks, Zou assembled a team of agents, mirroring the staff of a biotechnology company. In the Virtual Biotech’s set-up, a chief scientific officer (CSO) agent directs ‘employees’ in different divisions, each with their own subspecialities, such as target identification and clinical-trial design. For the Science study, Zhou’s team used versions of Claude — developed by Anthropic in San Francisco, California — as the underlying LLM that powered the agents. But he says that any advanced LLM will do, including open-source models that researchers can run on their own computers. To test the Virtual Biotech’s capabilities, Zou’s team tasked it with analysing the published results of more than 55,000 clinical trials that have been run for drugs for a wide range of conditions. The CSO assigned 37,075 agents to each tackle a single later-stage trial. Other virtual biotech employees searched for predictors of success in data se…

How fast are you ageing? Ask AI

Thu, 17 Sep 2026 00:00:00 +0000

Understanding why people age — and how to slow that process down — is an obsession that has stretched across millennia, from mythic quests for immortality to modern science’s hunt for drugs that lengthen lifespan. Now, that quest has entered the AI era. A report1 published today in Cell unveils a bespoke artificial-intelligence system that aims to turbocharge longevity research. The system introduces large language models (LLMs) that the authors trained on ageing-biology data. It also includes a suite of 17 tasks that can serve as benchmarks to gauge how well these and other LLMs perform on ageing-related projects. Finally, it includes an interface that brings the models and other ageing-related research tools together with AI assistants called agents to aid analyses. Gene clock predicts time to death in humans — and assesses ‘biological’ age The authors used their benchmarks to evaluate both their specialist LLMs and much larger cutting-edge commercial models produced by companies including OpenAI and DeepSeek, that are trained on larger, more diverse data sets to handle a broad range of tasks. In many — although not all — of the tests, the LLMs tailored to ageing research outperformed the large LLMs. “The paper makes a very important contribution to the longevity and ageing field,” says Marinka Zitnik, a computer scientist at Harvard Medical School in Boston, Massachusetts, who was not involved in the work. “It can inform the development of next-generation AI models.” The work also tackles a key question for the field: how can researchers train AI tools to understand ageing when scientists have not yet defined the concept for themselves? “For certain disease areas, such as cancer, the AI tasks can be very crisply defined,” says Zitnik. “Ageing is a very different beast. It is hard to define.” Clock collectors Researchers have spent decades gathering data to define that beast. One result has been a slew of ‘clocks’ aimed at measuring biological ageing, or the rate at which a person’s body exhibits signs of ageing. Such clocks measure biological age by assessing characteristics such as facial features, protein levels, gene activity and brain scans, all with the goal of quantifying whether the effects of ageing are outpacing or lagging behind someone’s chronological age in years. How quickly are you ageing? What molecular ‘clocks’ can tell you about your hea…

VA-Bench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control

Thu, 17 Sep 2026 00:00:00 +0000

Spatial intelligence requires more than describing object locations. Under incomplete observation, models must identify and acquire missing evidence, interpret it in a common spatial frame, and act on it. We introduce VA-Bench to evaluate the complete observe-reason-act-revise loop. General-purpose MLLMs learn procedural context from RGB-only demonstrations, actively select camera viewpoints, issue metric Cartesian commands, and revise them from execution feedback. Models receive no privileged object poses, oracle trajectories, or learned action heads. A fixed model-agnostic controller executes only model-specified targets. VA-Bench contains 14 base task families (11 single-arm and three dual-arm), seven held-out geometry/layout variants, and a long-horizon five-object composition track. We evaluate 12 primary model conditions in three independent runs over the same 20 physically verified seeds per base task, reporting terminal success, nine trajectory-level behavioral diagnostics, and subtask progress. First, the best-performing model scores 100.0% on target localization and 78.9% on spatial relations in the annotated run. Its three-run macro-average task success is only 53.93+/-3.17%. Second, active camera control significantly improves task success over passive multi-view observation. In one matched comparison, success rises from 27.86% to 57.50%. Third, held-out geometric transfer can reduce task success by over 30 percentage points. No model completes a strict long-horizon episode, despite substantial partial progress. VA-Bench thus tests whether general-purpose MLLMs can turn visual demonstrations and actively acquired evidence into successful embodied action.

scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository

Thu, 17 Sep 2026 00:00:00 +0000

scBaseCount is presently the largest public single-cell RNA-seq repository, containing over 502 million cells across 27 organisms and 75 tissues. An AI agent autonomously discovers, annotates, and uniformly reprocesses all 10× Genomics datasets in the SRA, creating a harmonized, continually updated resource for studying the diversity of cell biology and training AI models.

AI dives into a sea of data, from plankton to pollution

Wed, 16 Sep 2026 10:34:53 +0000

By Tom Cassauwers When asked why Jean-Olivier Irisson, a scientist at Sorbonne Université in Paris, decided to dedicate his life to studying microscopic creatures in the sea, his answer was simple: “They are beautiful.” Beauty may not be the first thing that comes to mind when we think of plankton – organisms that drift in water and come in an extraordinary variety of shapes and sizes. But images by Irisson’s team tell a different story. Shown in striking blues and oranges, as well as black and white, they reveal an unfamiliar and strangely beautiful world. “This one served as the model for the head of the creature in the Alien movie franchise,” Irisson said, pointing to one particularly unusual specimen. Plankton are not just fascinating to look at. They are fundamental to marine ecosystems, and play a crucial role in regulating Earth’s climate by helping remove carbon dioxide from the atmosphere. “Half of the photosynthesis in the world is done by plankton,” said Irisson. “They also form the foundation of the food chain at sea. Without them, there would be no fish.” Yet studying plankton is painstaking work. Researchers collect water samples and examine them under microscopes. They can contain huge numbers of organisms that all need to be identified and classified – a slow and demanding task when done manually. AI is now helping to speed things up. “We already use software to capture and process images, to identify every individual organism,” said Irisson. “For a single dataset we have 2.2 million of these images of individual organisms. That’s impossible to classify manually. With AI, we can, however, do this very fast. That’s opening up new possibilities in our research.” Drowning in data Irisson is far from alone in facing this problem. Underwater cameras and environmental sensors mean modern monitoring of oceans, rivers and lakes now generates far more data than researchers can realistically analyse by hand. “Faster and cheaper monitoring means problems like plankton decline, litter accumulation, oil spills and coral degradation can be picked up and acted on sooner.” Gergely Sipos, iMagine Making sense of that flood of information was the goal of iMagine, an EU-funded research collaboration between aquatic scientists and digital experts from 11 European countries. Running from 2022 to 2025, the project developed AI tools to help researchers process an…

Can MiniMax-H3 Reason About the Physical World? An Evaluation of Omni-Modal Generative Model

Wed, 16 Sep 2026 00:00:00 +0000

Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3 exemplifies this transition by combining multimodal context understanding with joint audio-visual generation in a shared latent framework. Its unified architecture raises a fundamental question: Can multimodal alignment improve the model's world reasoning, and what new evaluation paradigms do omni-modal inputs enable? To investigate this question, this work introduces a comprehensive evaluation framework organized around four complementary dimensions of physical world reasoning. Unlike existing evaluation frameworks for video generation and world models, which are often constrained by limited input modalities and evaluation settings where prompts closely match the target video content, our evaluation is specifically designed to exploit the multimodal inputs of Omni-Model. We construct a diverse set of novel tasks that require models to integrate complementary information across modalities. Specifically, we consider four scenarios, including implicit prompts paired with multiple frames, audio-image, prefix-videos, and audio-video inputs. Every single modality provides only partial evidence about the underlying event, requiring the model to jointly reason over the complementary semantic cues to infer latent event states and future dynamics. Across 517 evaluation instances, MiniMax-H3 achieves an overall success rate of 41.97%. Video-based Decision Reasoning yields the highest success rate at 56.00%, while Audio-based Disambiguation Reasoning is the weakest, reaching only 27.40%. These results indicate that effective multimodal integration remains key to fully exploiting the benefits of diverse input modalities. The project is available at https://github.com/gulucaptain/MiniMax-H3-Reason.

In-Context Robot Learning with VLM Agents

Wed, 16 Sep 2026 00:00:00 +0000

Enabling robots to adapt to unfamiliar environments as readily as humans remains a moonshot goal of embodied AI. No finite collection of demonstrations can cover every task and situation a robot will encounter, making the ability to learn from context at deployment essential for generalization. Such in-context learning (ICL), however, remains largely beyond the reach of existing robotic policies. The broad agentic capabilities of commercial vision-language models (VLMs), such as GPT-6 Astra, raise a compelling question: can these models learn from demonstrations, examples, and interaction feedback, then translate that information into executable and verifiable robot behavior from a new initial state without gradient updates or persistent changes to task-specific parameters? We introduce GPT-Policy, a general-agent framework for in-context robot learning. GPT-Policy integrates a context compiler that preserves task-relevant visual transitions, a VLM that proposes robot-tool actions, and a constrained controller that verifies and executes each action and reports its outcome. We evaluate its reliability and limitations through task success and efficiency metrics, matched comparisons across models, and controlled context ablations. In real-robot trials, human video demonstrations improve task completion even without robot action labels, while aligned action references yield further gains on contact-sensitive tasks. These findings position GPT-Policy as a step toward robot adaptation through in-context learning, providing an empirical foundation for translating the general-purpose capabilities of VLMs into physical behavior and clarifying the challenges that must be overcome for reliable deployment.

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

Wed, 16 Sep 2026 00:00:00 +0000

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents 摘要 ScienceBuddy is an interactive scientific research workspace that brings continually improving agents into a researcher's everyday work. It supports researchers in carrying out scientific tasks while turning their requests, feedback and execution evidence into tasks and evaluation rubrics for continual learning. At its core is recursive-in-recursive self-improvement, which couples harness evolution with model reinforcement learning: the inner recursion improves the harness with the model fixed, and the outer recursion trains the model under the improved harness. Case studies cover researcher interaction, harness refinement and model learning across four families of scientific task. 作者 * 共同一作期刊/会议引用 BibTeX @techreport{xue2026sciencebuddy, title = {{ScienceBuddy}: {Recursive-in-Recursive} Self-Improvement for Interactive Scientific Agents}, author = {Xue, Shuhan and Zhong, Jianyuan and Nan, Ziyuan and Li, Wenbin and Yu, Zhaochen and Ding, Jinchao and Gao, Qiang and Zhan, Pengyu and Zhang, Yuntong and Cheng, Tian and Yin, Zhenfei and Wu, Yingcheng and Yang, Ling}, institution = {PhAI Labs}, type = {Technical Report}, number = {PHAI-TR-2026-02}, month = {September}, year = {2026}, url = {https://phai-labs.com/papers/sciencebuddy/}, note = {Version v1. Equal contribution: Shuhan Xue, Jianyuan Zhong, Ziyuan Nan} }

OpenAI's 88-Hour Math Proof: A Result Being Correct vs. Being Understood

Tue, 15 Sep 2026 11:15:00 +0000

On September 8, OpenAI said 10,000 of its agents, running for 88 hours, had produced a 166-page proof regarding the Navier-Stokes equations — which describe fluid movement and have remained an open question since 1934. It's one of the seven Millennium Prize Problems established by the Clay Mathematics Institute in 2000, each carrying a $1 million prize; only one has ever been awarded. OpenAI has said it won't be claiming the money, which is just as well, since outside estimates put the compute bill between $10 million and $40 million. Clay now says the problem has "apparently been settled," while the formal evaluation proceeds. The institute calls that process "deliberately unhurried." Before Clay will consider paying, a proof has to be published in a refereed journal of worldwide repute and then survive two years of mathematicians trying to break it. If you check Clay's website, it now labels Navier-Stokes "Active": neither unsolved nor solved. Those conditions were written in 2000, and you'll notice they don't ask whether the argument is formally correct; they ask whether mathematicians have read it, argued with it and failed to break it. It seems the machine has produced the proof much faster than the mathematical community can absorb it, and while the cost of producing the answer is visible, the cost of turning it into understanding is only starting to be calculated. Navier-Stokes is the sharpest case so far, but it isn't really a story about fluid dynamics. The interesting story is the gap between a result being correct and a result being understood. Earlier this week on September 11, 25 Fields Medalists signed a declaration saying the way AI labs chase mathematical problems is damaging mathematics. The next day Terence Tao, who signed it, gave his blog over to two philosophers of mathematics, Silvia De Toffoli and Eamon Duede, who explained the claim. They separated two concepts that had rarely needed separating before: a logical proof (deductive validity, which a machine can verify) and an intelligible proof (understanding why something is true in a form humans can explain and build upon). Historically, these came together because no human could construct the first without possessing the second. AI pulled them apart. Their verdict on the Navier-Stokes result: "What OpenAI has given us is an answer. But it is not clear that they have delivered a fruit…

Interview with Yash Saxena: how is external knowledge used in AI systems?

Tue, 15 Sep 2026 09:18:57 +0000

In a new series of interviews, we’re meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) . In this first article of the series, we caught up with Yash Saxena to find out how he is trying to make it easier to understand how external knowledge is used in AI systems. Tell us a bit about your PhD – where are you studying, and what is the topic of your research? I am a PhD student in Computer Science at the University of Maryland, Baltimore County (UMBC), advised by Dr. Manas Gaur in the Knowledge Infused AI and Inference (KAI2) Lab. My PhD focuses on making AI systems that use external information easier to understand and audit. Many AI systems now look through external documents before answering a question. My research follows what happens to that evidence as it moves through the system. I study where it came from, why certain evidence was selected, whether it actually supports the claim being made, and whether the context needed to interpret that evidence correctly is preserved along the way. I call this broader research direction IKnowFlow , or Interpretable Knowledge Flow . The aim is to keep the evidence trail visible so that someone can inspect a generated claim and understand how the system arrived at it. This is especially important in areas such as law, science, and healthcare, where an incorrect AI-generated claim can have serious real-world consequences. Could you give us an overview of the research you’ve carried out so far during your PhD? I started by studying source attribution, or how an AI-generated claim can be linked back to the source that supports it. In REASONS (REtrieval and Automated citationS Of scieNtific Sentences), we evaluated retrieval and citation generation across more than 20,000 scientific papers from 12 research areas. I later compared two ways of adding citations to large language model responses: generating them together with the answer or assigning them afterward. In our human evaluation, adding citations afterward was more accurate, but this also showed that a citation alone does not necessarily reveal how the model arrived at its answer. From there, I started looking earlier in the process. Interpretable Modular Retrieval Neural Networks (IMR…

Insilico Launches AI-driven Longevity Vaccines Research for Aging-related Diseases and Human Longevity

Tue, 15 Sep 2026 07:00:00 +0000

The program applies generative AI and programmable RNA medicine to design single-administration, self-limiting in vivo cell therapies that clear the earliest culprit cells of age-related disease. - The new research initiative aims to develop programmable, self-limiting preventive treatment to eliminate early cellular drivers of age-related diseases. - Powered by Pharma.AI and aging foundation models, the approach targets senescent, fibrotic, and autoreactive cells, starting with immune system rejuvenation. Insilico Medicine (“Insilico”; HKEX: 3696), a clinical-stage generative artificial intelligence (AI)-driven drug discovery company, today announced Longevity Vaccines, a research initiative developing treatments that direct a patient’s own immune cells to eliminate the earliest cellular drivers of age-related disease. This initiative expands the company’s dual-purpose, aging-oriented discovery strategy from small molecules into RNA-encoded, in vivo cell engineering. "We built Insilico to treat aging and disease together, and to prove that AI can design medicines that deliver real patient impact ," said Alex Zhavoronkov, PhD, Founder and co-CEO of Insilico Medicine. “Longevity Vaccines initiative extends that same rigor to preventive medicine—delivering programmable, single-dose therapies that clear the root-cause cells of age-related disease, starting with the immune system itself." Targeting the Culprit Cells at the Outset Many aging-related diseases are initiated and sustained by discrete cell populations with well-studied surface markers. Examples include senescent cells that secrete pro-inflammatory proteins (the senescence-associated secretory phenotype, or SASP), activated fibroblasts that drive fibrosis, and autoreactive lymphocytes that erode self-tolerance. Rather than treating downstream pathology, Longevity Vaccines are designed to eliminate these initiating cell populations within a preventive window and on a transient basis. The approach builds on a growing body of peer-reviewed research showing that engineered T-cells can clear such cells and deliver durable functional benefits from a single therapeutic course. A Programmable, Self-Limiting Chassis The initiative utilizes a single, programmable delivery modality: a short-lived genetic instruction, encoded in circular mRNA (cmRNA) and packaged within a targeted lipid nanoparticle (LNP). Deliv…

Former OpenAI Post-Training VP Responds to Terence Tao: Don't Frame AI's Future by Today's Limits

Tue, 15 Sep 2026 05:39:10 +0000

A statement signed by 25 Fields medalists, posted by Terence Tao on September 11 under the title 《人工智能在数学中的严重错位》, argues that AI companies' focus on solving famous mathematical problems seriously diverges from the community's goals of conceptual understanding, method development, and knowledge transmission. Former OpenAI VP of post-training Liam Fedus responds that today's AI limits should not frame future possibilities: with broader training goals, AI could offer insights and build conceptual frameworks mathematicians can extend — a view he links to Noam Brown's judgment that AlphaGo raised human players' levels. The article recounts that in May an internal OpenAI model rebutted the nearly 80-year-old Erdős unit distance conjecture, and in early September about 10,000 autonomous AI agents found a proof of singularities in the three-dimensional Navier–Stokes equations in about 88 hours, formalized with Lean, pending confirmation by the mathematical community. Disputes over authorship and priority followed.

LabAgent: Customize Any Research Hubs for Scientific Discoveries Using AI Agents

Tue, 15 Sep 2026 04:00:00 +0000

Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-depth scientific questions. However, the change of lab staff, such as student graduation, leads to a lack of personnel capable of replicating methods. Methods that have been developed with significant effort and resources cannot be continued. To address these limitations, we propose LabAgent, a reproduce and discovery harness tailored for a lab's continuous work. LabAgent employs two mechanisms to guarantee that all skills can be executed and verified and to record the corrective methods and experiences, allowing for direct correction or avoidance of similar errors. We applied LabAgent to drug property prediction, biomedical problem analysis, protein variant effect prediction, and statistical genetics in life science domains. LabAgent ranks first over commercial generalist agents in every domain, and demonstrates accurate reproduction of a published figure. Overall, these results demonstrate that LabAgent can effectively integrate and reasonably expand laboratory knowledge.

RAGCell: Retrieval-Augmented Generation as Supervision for Versatile Single-cell Analysis

Tue, 15 Sep 2026 04:00:00 +0000

Single-cell foundation models (scFMs) are transforming computational biology by enabling generalizable, task-agnostic representations for versatile single-cell analysis. Despite their progress in facilitating rapid deployment for downstream tasks, off-the-shelf scFMs still have some overlooked concerns: (I) (Pretraining Cost.) Pretrain-based scFMs necessitate pretraining on a vast volume of cells, rendering it draining resources in applications. (II) (Heterogeneous Gap.) Large Language Models (LLM)-based scFMs ignore the tremendous heterogeneous gap between LLM textual and raw cellular spaces, leading to insufficient capability when facing downstream tasks. To this end, we introduce RAGCell, a versatile single-cell analysis framework that achieves a double-win in both cost-effectiveness and high performance. The success of RAGCell lies in two key aspects: Leveraging LLMs to construct cell-level and feature-level knowledge databases, which serve as supervision signals for training the cell model and significantly reduce the training cost ($>$pretrain-based scFMs). Aligning cell representations with text embeddings from the bi-level knowledge databases, enabling knowledge transfer from textual spaces to cellular spaces and effectively mitigating the heterogeneous gap ($>$LLM-based scFMs). Through extensive experiments on six downstream single-cell analysis tasks, we demonstrate that RAGCell achieves outstanding performance compared to state-of-the-art scFMs while operating at less than $\sim$1/10 the cost of pretrain-based scFMs.

A PB–SAV Optimizer with Momentum and Adaptive Mobility

Tue, 15 Sep 2026 04:00:00 +0000

Objectives in scientific machine learning are often prescribed as a sum of several terms, such as the residual, boundary, initial, and data losses of a physics-informed neural network. In the pullback-corrected scalar auxiliary variable (PB--SAV) method, one scalar tracks the shifted objective while the component gradients build a positive semidefinite curvature correction of rank at most the number of components. We carry that correction into an optimizer with momentum and an adaptive mobility, applying it to the gradient and the stored momentum in a single implicit solve. A mobility that is nonincreasing in the Loewner order yields an exact modified energy law, covering Euclidean and AMSGrad-type choices; the corresponding identity for momentum appended after the solve carries a cross term of indefinite sign. For a fixed mobility we give a necessary and sufficient condition for local stability at a stationary point, depending on the Hessian minus twice the correction, and show that it also gives local geometric convergence for every scalar relaxation sequence. The implicit solve reduces to a dense system whose order is the number of components. In the forward Burgers comparison, four components reduce the mean tail objective by 64.7% and the final solution error by 50.2% relative to one component at the same learning rate and momentum settings.

Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?

Tue, 15 Sep 2026 04:00:00 +0000

Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. In this work, we explore the feasibility of building a self-adaptive physical AI agent that manages long-term physical tasks in a zero-shot manner and adapts to environmental changes without human intervention. We design a multi-agent framework that integrates planning, tool calling, observation, and verification, and evaluate it on agricultural tasks against reinforcement learning (RL) agents under different weather patterns. Our results show that zero-shot LLM agents can achieve comparable management outcomes to RL agents under the same weather pattern and adapt more effectively than RL when evaluated under a shifted environment, highlighting a promising path toward self-adaptive physical AI agents.

Verifiable Social Reasoning for LLM Assistants

Tue, 15 Sep 2026 00:00:00 +0000

LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (i) it requires setups where the assistant learns about social situations from subjective user narratives, and (ii) social properties, such as others' intentions, typically lack verifiable ground truth. To address these challenges, we introduce Fuse, a multi-agent simulation framework for studying user-mediated social reasoning. In Fuse, a target agent with a hidden motive interacts with other agents including one representing the user, who then consults the evaluated assistant to infer the target's motive, providing verifiable ground truth by construction. Simulation faithfulness is validated through a human study with 24k annotations. We apply Fuse to 12 LLMs and demonstrate its analytical utility by systematically isolating key factors, showing that (i) user mediation compounds the inherent difficulty of social reasoning; (ii) LLMs exhibit systematic sensitivity to biased user framing; (iii) models can require more details than humans need to reach a correct prediction; and (iv) longer conversations do not always improve performance despite providing opportunities for clarifying questions. We open-source Fuse and a dataset with 21k examples.

PhAI Labs introduces the DFM research framework

Tue, 15 Sep 2026 00:00:00 +0000

PhAI Labs announced its DFM research framework, published the DFM technical report and research-map repository, and said ScienceBuddy, ScienceIDE and JEPA-Anything were scheduled as separate releases on September 16–18.

Embedding Physics Priors in Robot Learning: A Survey

Tue, 15 Sep 2026 00:00:00 +0000

The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remarkable success in computer vision and natural language processing, robotics remains constrained by limited data, complex real-world interactions, and the need for reliable operation. These challenges have motivated the exploration of physics-embedded robot learning, which embeds physics priors into learning algorithms. By encoding the underlying physical laws and constraints, physics priors can complement limited data with robotics-specific inductive biases, potentially improving generalization, interpretability, and sample efficiency. However, the literature on physics-embedded robot learning remains fragmented across terminology, methodologies, and application domains, making it difficult to assess this growing body of work. This survey reviews physics-embedded robot learning across a broad range of physics priors, robotics applications, and machine learning models, from single-layer perceptrons to generative foundation models. We adopt a unified taxonomy that classifies existing approaches according to their physics embedding: physics-guided inputs, data, and representations; physics-encoded model architectures; and physics-informed training loss functions. Building on this taxonomy, we review methods for robot dynamics learning, trajectory planning, prediction, control, and estimation, together with the corresponding open-source software ecosystem. We identify key open challenges, and outline promising future research directions. Overall, we argue that physics priors provide a particularly relevant robotics-specific inductive bias, complementing rather than replacing data-driven learning, and paving the way toward more generalizable, data-efficient, and trustworthy robotic systems.

Discovery Foundation Models: Toward Open-Ended Discovery Intelligence

Tue, 15 Sep 2026 00:00:00 +0000

Discovery Foundation Models: Toward Open-Ended Discovery Intelligence 摘要 Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome feedback. We argue that the next frontier is a further transition: from solving and acting within problems specified by humans to participating in the process by which new problems, representations, explanations, and knowledge are created. We refer to this capability as Discovery Intelligence. We formulate Discovery Foundation Models (DFMs) as general-purpose model systems for open-ended discovery. A DFM operates over a revisable research state and supports seven coupled capabilities spanning problem discovery, formulation, representation construction, hypothesis formation, intervention, evidence-grounded revision, and continual discovery improvement. We instantiate this framework with Zetema, which couples explicit research-state dynamics, verification and experimental gating, external grounding, and cross-task Discovery Skill evolution. We further ground the framework with GALILEO, a real therapeutic-discovery system in which Dry-Lab reasoning, robotic and hands-on Wet-Lab experimentation, external biological evidence, and iterative hypothesis and design revision form a closed physical discovery loop. We then formulate a unified approach to capability formation and process-centered evaluation, enabling discovery behavior to be trained, improved, and measured beyond final-answer performance. Together, these components establish discovery as a learnable, executable, and evaluable capability of foundation-model systems. We view this shift as a broader progression in intelligence scaling: from learning over existing knowledge, to learning from action outcomes, and ultimately to participating in the construction, testing, and revision of the structures through which new knowledge is discovered. 作者期刊/会议引用 BibTeX @article{yang2026discovery, title={Discovery Foundation Models: Toward Open-Ended Discovery Intelligence}, author={Yang, Ling and Yin, Zhenfei and Wu, Yingcheng}, journal={arXiv preprint arXiv:2609.15973}, year={2026} }

DFM scientist collaboration program launches

Tue, 15 Sep 2026 00:00:00 +0000

PhAI Labs opened a scientist collaboration program focused first on life science and biomedicine, inviting collaborators with scientific questions, data, computational or experimental environments, and real validation conditions.

Why Don't Machine Learning Research Agents Overfit?

Mon, 14 Sep 2026 16:32:23 +0000

Machine learning, at its core, is about generalization, not memorization. You hand your learning algorithm a pile of training examples and use them to fit a model. But the goal is not to perform well on the training examples — that's easy, you could just memorize the answers. The goal is to perform well on new examples that you have never before seen. If a model does well on the data it was trained on but poorly on fresh data, it hasn’t actually learned anything; you have only fooled yourself into thinking it has. This failure mode has a name: overfitting. Anyone who has taken an introductory statistics or machine learning class knows the standard defense. You hold out some of your data and refuse to train on it. In practice, this held-out data plays two roles. A validation set is one you consult repeatedly while building the model — to compare candidates, tune hyperparameters, and decide what to try next. A final test set (or holdout) is meant to be touched only once, at the very end: because the training procedure never saw it, strong performance there is a correct proxy for the new examples you will encounter in the wild. Machine learning, at its core, is about generalization, not memorization The “holdout” condition is crucial, though. The correct-proxy guarantee holds if the held-out set stays genuinely unseen. If you check your performance on it, tweak your training procedure in response, recheck, and iterate, chasing better and better numbers, that set is no longer unseen; it has become part of your training procedure. Do this enough times, and you can overfit it just as you might have overfit the training set, and you have lost your proxy for unseen data. This is true of any held-out set you reuse this way, including a validation set, which is reused by design. A puzzle at the heart of machine learning Real machine learning research looks exactly like the iterative improvement loop we just described. Everyone gauges performance using a handful of benchmark datasets that go unrevised for years. The research community repeats an enormous, distributed loop: evaluate a model on the benchmark, revise the training procedure, re-evaluate, publish, and let the next group eke out a little more improvement. This is precisely the kind of hill-climbing against a held-out set that, by the textbook account, ought to produce rampant overfitting. By now, the leader…

Rentosertib shows lower predicted biological age across six proteomic aging clocks

Mon, 14 Sep 2026 10:58:56 +0000

Insilico Medicine announced the publication of a study in Nature Biotechnology showing that its AI-discovered idiopathic pulmonary fibrosis drug candidate rentosertib produced reductions in predicted biological age across six independently developed proteomic aging clocks. The research analyzed longitudinal serum proteomic data collected during a Phase IIa clinical trial of rentosertib in idiopathic pulmonary fibrosis, or IPF. The analysis included 42 patients and measurements covering 2,841 proteins. Researchers applied six independently developed proteomic aging models, including ProtAge, OrganAge, PAC, ipfP3GPT and PAOPAC. Despite differences in model architecture, training data and methodologies, all six indicated a trend toward lower predicted biological age among rentosertib-treated patients compared with placebo. The strongest signal was observed at Week 4 among participants receiving 30 mg of rentosertib twice daily. The study reported an approximately three- to four-year reduction in predicted biological age across certain measures and as much as six years according to one aging clock. The findings remain exploratory and do not establish that rentosertib extends lifespan or reverses human aging. The study instead evaluates changes in proteomic biomarkers that have been developed to estimate biological aging. The work is notable because rentosertib was developed from the outset using artificial intelligence to identify both its therapeutic target and the molecule itself rather than being a previously approved drug repurposed for aging research. Insilico used its AI-powered target discovery platform to identify TNIK as a target associated with both fibrosis and aging biology. The company said TNIK scored highly across six hallmarks of aging, leading researchers to investigate it as a dual-purpose target for IPF and aging-related biological processes. Insilico subsequently used its generative chemistry platform Chemistry42 to design rentosertib, also known as ISM001-055, as a small-molecule inhibitor targeting TNIK. The program progressed from target identification to preclinical candidate nomination in approximately 18 months. Preclinical research on the program was published in Nature Biotechnology, followed by Phase IIa clinical results in Nature Medicine in 2025. The Phase IIa study met its primary safety endpoint and showed a dose-dependent effic…

When AI Art Has No Author: Study Finds Generated Images Often Can’t Be Traced to Training Data

Mon, 14 Sep 2026 08:22:55 +0000

MIT CSAIL researchers found that at large scales, you can often remove any single image from AI training data, every image by a given artist, or every photograph of a given person, and the generated output won’t change appreciably. At top left is an image generated by a model trained on public domain artwork created by 744 artists. The others are a sampling of images that would have been generated had any one of the 744 artists been omitted from the training set. Image credit: Collage courtesy of the researchers, showing images generated by AI. By Rachel Gordon When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility. New work from a team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the question may often have no answer. It’s not that the tools for finding it are inadequate. The connection itself has disappeared. The scientists identified a phenomenon they call attribution decay, where the more data a generative model is trained on, the less any individual training example matters to any particular output. It feels counterintuitive, but at sufficiently large scales, they find, you can often remove any single image from the training data, or every image by a given artist, or every photograph of a given person, and the generated sample doesn’t change. And if removing something changes nothing, the researchers argue, it can’t be said to be responsible for anything. “If you take away a piece of data and the output of the model doesn’t change, then that piece of data didn’t affect the output,” says Zheng Dai, former MIT CSAIL researcher and lead author on the work. “So it doesn’t make much sense to attribute the output to that piece of data. And if you then do this one at a time for every other piece of data and find that the output doesn’t change for any of them either, then it doesn’t make much sense to attribute the output to any one of them.” “All previous methods were approximate,” says MIT Professor David Gifford, who is an MIT CSAIL principal investigator. “They really could not absolutely show that deleting individual things did not cha…

Teaching future scientists to interrogate AI tools for scientific discovery

Mon, 14 Sep 2026 08:00:00 +0000

AutoDiscovery, our AI agent for scientific research, analyzes datasets, proposes hypotheses, runs experiments to test them, and ranks the results by Bayesian surprise—the gap between what the model predicted and what the data showed. In a University of Washington classroom this spring, it surfaced a possible flaw in how battery aging is measured, an unexpected pattern in silicon polymers, and a recurring feature in proteins that respond to light. But a surprising result is not necessarily an important one—or one that will withstand further testing. Students still had to scrutinize the evidence, compare the findings with published research, and decide whether each result reflected a genuine discovery, a coincidence, or a problem with the data. Luna Yue Huang, an associate teaching professor of materials science and engineering at the UW, invited students to explore this way of working with AI through the GenAI for Science: Ai2–UW Materials Challenge. Twenty-five teams proposed a dataset and research question; ten were selected to spend several weeks working with AutoDiscovery. A UW instructional team and Ai2 researchers reviewed the proposals based on the richness and scientific distinctiveness of the data, their potential to produce meaningful findings, and the overall diversity of topics represented. Ten projects were selected, with students from other proposals joining those teams. In some cases, instructors also helped narrow broad datasets to a more manageable scope aligned with the students’ research interests. Students tested AutoDiscovery’s hypotheses against the available evidence, identified gaps in its reasoning, and assessed whether it meaningfully reduced the amount of manual data analysis required. AutoDiscovery's performance varied considerably—in some cases, it surfaced leads the students might not otherwise have explored. Here are four representative projects from the class: Project 1: Lithium battery aging A group studying lithium-ion battery aging surfaced a counterintuitive lead: that the periodic diagnostic tests used to check a cell's health may themselves speed its degradation. This rested on a correlation in the data rather than proof that the tests caused the extra wear, so confirming it would take a controlled experiment—a clear and logical next step for follow-on work. Project 2: Estimating an atom’s charge Another group, drawing o…

HypoKG: Evidence-Disciplined Biomedical Hypothesis Generation Beyond Endpoint Knowledge

Mon, 14 Sep 2026 04:00:00 +0000

Large language models (LLMs) can generate biomedical hypotheses, but it remains unclear whether they truly reason from scientific evidence or simply produce convincing-sounding ideas. To study this, we combine three major biological databases: the Kyoto Encyclopedia of Genes and Genomes (KEGG), Rhea, and UniProt, into a unified biochemical knowledge graph and construct a benchmark of 550 paths connecting enzyme sources to rare disease endpoints, yielding 13,200 hypotheses from six LLMs under four conditions varying the biological information each model receives: source enzyme only, full biological path, or source and disease endpoint only. Hypotheses are scored using an expert-derived five-criterion rubric on a 1-5 scale per criterion. We find that models given both the source and disease endpoint often produce the highest-scoring hypotheses, showing that LLMs can generate compelling ideas from minimal information. However, these hypotheses are less grounded in the evidence. In contrast, models given the full biological path generate hypotheses more consistent with known mechanistic relationships. We call this evidence-disciplined reasoning. To confirm this effect, we shuffled intermediate path steps while keeping endpoints fixed. Evidence grounding dropped significantly (delta = -0.793, p < 0.001), confirming models genuinely used path structure during reasoning. Our findings show that knowledge graphs support hypothesis generation in two ways: they identify biological endpoint pairs absent from the literature, and their mechanistic paths guide how LLMs reason between them.

PhysBrain 1.5 Tops Open-Source Models on 28 Public Benchmarks, Nears GPT-6 Astra

Mon, 14 Sep 2026 03:18:42 +0000

The report says the Chinese company DeepCybo released the physical foundation model PhysBrain 1.5 on September 9, 2026. It scored 72.5 on 28 public benchmarks, ranking first among participating open-source models, with 14 first-place and 10 second-place open-source results. Its score is within 1 point of GPT-6 Astra (73.3) and Gemini 3.6 Flash (73.0), and above Hy-Embodied-VLM-1.0 (66.0) by about 6.5 points and RynnBrain 1.1 (63.1) by 9.4 points. On individual benchmarks, it scored 73.9 on RoboSpatial-Home, 84.0 on Part-Affordance and 89.8 on RoboRefit. The 2B and 8B weights, technical report and evaluation kit are open-sourced; the 2B version scored 66.6 on the same tests and is described under official rules as reference-only. The report also cites a Robocurve experiment in which GPT-6 Astra completed 19 of 20 block-in-bowl tasks, versus 8 for Claude Fable 5.1, but its success rate on a millimeter-level puzzle-insertion task fell from 95% to 10%.

Pick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks

Mon, 14 Sep 2026 00:00:00 +0000

Backdoor poisoning attacks add poisoned examples to otherwise-clean finetuning data, pairing a trigger with a target behavior that the model learns to produce when the trigger appears. Existing evaluations typically fix the number of poisoned examples and sample them at random from a candidate pool. We show that this can severely underestimate worst-case vulnerability: across three LLaMA-3-8B backdoor settings, holding the model, clean data, and poison count fixed, attack success ranges from 3% to 80% depending only on which poison set is chosen. We formalize poison selection as oracle-budgeted set optimization and introduce SAILS (Set-level Audit-Informed Iterative Learned Selection), which learns a set scorer from a few hundred finetune-and-evaluate runs, ranks millions of candidate sets, and audits only a small shortlist. SAILS improves held-out attack success by 30 percentage points on average over the strongest influence baselines, transfers from small-scale to full-scale finetuning, and extends to code-generation, agentic, and API-only backdoors.

'Multifunctional' brain implant translates speech and gestures in real time

Mon, 14 Sep 2026 00:00:00 +0000

Human communication relies on more than just talking; we nod, shrug, move our hands and point with our fingers. For people with severe paralysis, brain–computer interfaces (BCIs) can convey intended speech or movement. Now, researchers have shown that a single brain implant can do both at once. The device, described1 today in Nature Neuroscience, is the first to translate both verbal and non-verbal modes of communication, simultaneously and within seconds of the user’s intent. It uses artificial intelligence to convert electrical brain activity into text that appears on screen and to prompt a personalized animated avatar to move. Recent BCI technology has enabled people with paralysis to do tasks such as talking2 and controlling a cursor3 or a robotic arm4. “But most of these studies have focused on restoring one of these functions at a time,” says study co-author Samantha Brosler, a PhD student in bioengineering at the University of California, San Francisco. This system is a proof of concept but aims to be “a step towards more multifunctional BCIs that can capture more of the expressivity and communication that is possible” than with current technologies, she adds. “This gives us hope for neural prosthesis that will enter day-to-day usage,” says Christian Herff, a computational neuroscientist at Maastricht University in the Netherlands. Multitasking challenges The difficulty of decoding gestures and speech at the same time is that some of the neural signals that are involved overlap. And the patterns of neural activity can be slightly different during tasks that involve simultaneous speech and gesturing, compared with that during speech-only or gesture-only tasks. To distinguish between the two, Brosler and her colleagues worked with two participants. The device — a surgically implanted array of 253 electrodes placed on the surface of the brain’s cortex — “covers a fairly large surface area of the sensorimotor cortex, so it can capture signals related to both speech and body movements”, explains Brosler. The first participant had impaired speech and movement following a brainstem stroke. The researchers asked him to silently speak five phrases and attempt to wave, nod, shake his hands and clap. The second participant had a type of motor neuron disease called amyotrophic lateral sclerosis, which caused progressive speech paralysis and motor impairments. At…

Drug firms' secret data supercharge AI protein models

Mon, 14 Sep 2026 00:00:00 +0000

For drug discovery, protein-folding models such as AlphaFold have a data problem: there isn’t enough of it in public databases. Improving the performance of these artificial-intelligence-based tools will require extra data that provide examples of how proteins and drugs interact, some scientists argue. Protein structures— locked away by the thousands in drug company vaults— offer one promising source. Today, a consortium of pharmaceutical companies reports that using such data to train AI models of protein folding improves model performance markedly. The group used OpenFold3 — an open-source replication of AlphaFold 3 — to develop a new model trained on more than 20,000 proprietary protein structures. The system outperformed both comparable ones trained on public data alone and those trained on the siloed datasets of individual firms. The study, described in a blog post, has not been peer-reviewed, and the model is not publicly available. “You add all this data, and you get a pretty big bump in performance,” says Mohammed AlQuraishi, a computational biologist at Columbia University in New York City, who was part of the effort. AlphaFold is running out of data — so drug firms are building their own version The findings, he says, strengthen the case for generating similar publicly available datasets to supercharge protein-folding AIs. One such project, called OpenBind and supported by up to £8 million (US$10.8 million) in UK government funding, released hundreds of new protein structures last month, with thousands more in the works. An untapped vein The Protein Data Bank (PDB), an open repository of more than 200,000 experimentally determined protein structures, was the bedrock of AlphaFold 2’s training data. It enabled the tool to predict protein structures with startling accuracy — a breakthrough recognized with the 2024 Nobel Prize in Chemistry. The model’s successors, including AlphaFold 3, added the ability to predict how proteins will interact with other molecules, including potential drugs. But the PDB has relatively few examples of experimentally determined structures interacting with drug-like molecules — maybe just 10,000, says Paul Mortenson, vice-president for computational chemistry and informatics at Astex Pharmaceuticals in Cambridge, UK. That lack of data is a problem for drug-discovery efforts. Research has suggested that the accuracy of Alph…

RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

Mon, 14 Sep 2026 00:00:00 +0000

Digital agents must often adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. We introduce RSIAgent, a training-free multi-agent framework for recursive self-improvement through autonomous memory construction. RSIAgent coordinates curriculum, actor, and verifier agents to continually explore the environment, validate outcomes, and retain environment-specific knowledge, including reusable causal relationships between actions, conditions, and consequences. It further adopts a broad-then-deep exploration strategy, combining parallel broad recursive self-exploration for discovering diverse environment structures with focused deep self-exploration for uncovering hard cases, hidden constraints, boundary conditions, and previously unknown causal dependencies. The resulting memory is frozen and can be directly reused for downstream tasks without updating model parameters. Experiments on OSWorld-v2 and Agent's Last Exam show that RSIAgent substantially improves strong open-source models, enabling Kimi-K3 and GLM-5.3 to outperform frontier closed-source models including GPT-6.

An engineered nanopore identifies saccharides, amino acids, peptides and ribonucleotides

Mon, 14 Sep 2026 00:00:00 +0000

Simultaneous characterization of proteins, RNAs and glycans with a nanopore remains a substantial challenge. To advance toward this goal, we introduce a multifunctional adaptor, maleimido-C2-formylphenylboronic acid (maleimido-C2-FPBA), into the Mycobacterium smegmatis porin A (MspA) nanopore at the pore constriction site. Here we demonstrate that the engineered nanopore, MspA-FPBA, enables the simultaneous identification of a diverse set of analytes, including 21 proteinogenic amino acids, three post-translationally modified (PTM) amino acids, four canonical nucleoside monophosphates (NMPs), three epigenetically modified NMPs, four monosaccharides and five peptides. Combined with machine learning, this sensor achieves an overall accuracy of 98.7%. Furthermore, it generates predictable event signatures for specific analyte types, enabling the machine learning model to identify analytes without strictly relying on prearchived event features, as demonstrated by the nanopore analysis of yeast cell extract. Lastly, we apply MspA-FPBA for compositional analysis of G2 glycopeptides. Future integration of hydrolase with MspA-FPBA may further improve MspA-FPBA utility as a single system to identify diverse biomolecules. Modified nanopore simultaneously identifies diverse biomolecules and their modifications.

A multi-period dataset of retrogressive thaw slump susceptibility for Northern Hemisphere permafrost regions

Mon, 14 Sep 2026 00:00:00 +0000

Permafrost thaw accelerates the initiation and expansion of retrogressive thaw slumps (RTSs), with critical implications for the terrestrial carbon cycle and land-surface energy budgets. However, long-term datasets characterizing spatiotemporal variations in RTS susceptibility across Northern Hemisphere permafrost regions remain lacking. Here, we integrate 55,673 georeferenced RTS occurrence records with machine learning models to generate a 1-km spatiotemporal dataset of RTS susceptibility from 2000 to 2100 under multiple climate change scenarios. RTS susceptibility is defined as the relative occurrence probability of RTS under specified environmental conditions. We independently modelled RTS susceptibility for the Arctic and Tibetan Plateau and merged the individual outputs to construct a spatially harmonized dataset for the Northern Hemisphere. Model performance was evaluated using standard classification metrics, yielding mean area under the receiver operating characteristic curve (AUC) values of 0.96 ± 0.005. The dataset includes georeferenced RTS inventories and gridded susceptibility maps for the entire Northern Hemisphere permafrost domain, covering the baseline period and diverse future climate scenarios. These products provide a foundational resource for quantifying permafrost hazard risks, infrastructure exposure, and carbon-climate feedbacks under climate warming.

Continuous Diagnostic Indices from BanffNET Automated Lesion Scores for Kidney Transplant Pathology: Development and Evaluation of a Diagnostic Prediction Model

Sun, 13 Sep 2026 00:00:00 +0000

Background: Histopathological diagnoses in kidney transplant biopsies, as described by the Banff classification, suffer from substantial inter-observer variability and misinterpretation, making them prone to diagnostic errors. Continuous indices based on BanffNET automated lesion scores are proposed to quantify the continuous phenotypic spectrum of kidney transplant biopsies, complementary to pathologist assessment of biopsies. Methods: The BanffNET model was used to obtain continuous lesion scoring for a training cohort of 2544 biopsies, a large validation cohort of 3863 biopsies, and a multi-reader cohort of 36 biopsies scored by 67 pathologists. A penalized regression approach was used to condense 17 automated lesion scores into four indices, representing the most important dimensions of graft injury: a TCMR/TI Index, an AMR/MVI Index, an Activity Index and a Chronicity Index. The BanffNET Indices were evaluated against Banff diagnoses and histological indices in the training cohort and validation cohort. Since pathologist-assigned Banff lesions and diagnoses suffer from inter-observer variability, associations were also assessed with diagnoses in a multi-reader study, with time to kidney graft failure in the training and validation cohort and with biopsy-based molecular signatures in the validation cohort. Results: The BanffNET TCMR/TI Index, AMR/MVI Index and Activity Index showed excellent discrimination of Banff TCMR, Banff MVI and Banff Any Diagnosis respectively, with validation AUCs ranging from 0.80 to 0.92. In addition, the BanffNET Indices explained large amounts of variability in multi-observer reference standards, time to graft failure and molecular biopsy-based diagnostics. In the large majority of cases, variability explained by BanffNET Indices matched or exceeded the variability explained by pathologist-assigned diagnoses and histological indices. Conclusions: BanffNET Indices are reproducible descriptors of whole slide images that could complement and augment kidney transplant biopsy evaluation by pathologists. Associations of BanffNET Indices with multi-observer diagnoses and related outcomes suggest that they capture additional information on tissue injury type and severity on top of pathologist-assigned Banff diagnoses and histological indices.

E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning

Sun, 13 Sep 2026 00:00:00 +0000

Can financial vision-language models (VLMs) turn chart evidence into reliable action recommendations? Existing hallucination evaluations are mostly claim-centric; they assess whether generated statements are supported, but not whether evidence remains traceable through rationale, confidence, and final action. We introduce E2A-Bench, a 969-query benchmark for financial chart reasoning, constructed from 323 HS300 constituents under three input modalities with deterministic OHLCV-derived evidence anchors. E2A-Bench evaluates grounding, reasoning-action consistency, evidence-confidence calibration, and directional coverage through UCR, RCI, ECI, and NDR, where NDR measures coverage-aware evidence-to-action reliability rather than realized trading performance. Evaluating 20 VLMs reveals three failures hidden by scalar hallucination scores: the lowest-UCR model ranks near the bottom by NDR due to only 6.4% directional coverage; oracle-aided verification reduces unsupported claims but can collapse coverage; and financial fine-tuning amplifies the BUY:SELL ratio by factors of 4.21 to 4.68 across strict base-fine-tuned pairs. These results show that financial VLM evaluation should trace the full evidence-to-action chain rather than rely on a single hallucination score. Code and data: https://github.com/wanng-ide/E2A-Bench

Spontaneous thought transitions follow a foraging-like policy on high-order memory patches

Sun, 13 Sep 2026 00:00:00 +0000

When people engage in spontaneous thought under low external constraint, thought content changes continuously and can shift across topics. However, the cognitive computational principles governing these transitions remain unclear, as does whether similar dynamics emerge in artificial language generation. Here, we formalized spontaneous narrative dynamics using a foraging-inspired framework, constructing an agent based on the marginal value theorem (MVT) that balances exploration and exploitation while selecting among informational resources represented in a participant-specific, high-order patch network. Patches were defined as partially overlapping composite structures containing second-order paths and third-order interactions, with their available resources quantified using multilevel structure values. In a multi-theme free spoken narrative task, the model predicted the temporal organization of human patch activation substantially better than random-shuffle baselines, although the strength of model conformity varied across participant-theme samples. Model-predicted patch transitions corresponded to systematic semantic segmentation and reorganization across multiple representational levels. Transition rates were positively associated with the perceived importance and likelihood of the narrated events. These findings were replicated when the modeled memory environment was enriched with additional hub-related narrative resources in a supplemental experiment. Control-model analyses further showed that patch-network topology, resource depletion history, or either order of structure value alone could explain only part of the observed dynamics; the most stable human prediction required foraging-related decisions guided jointly by multilevel structure values. Narratives generated by large language models (LLMs) also showed detectable conformity with the same framework, but the full model captured LLM content less consistently, while simpler control models accounted for a larger proportion of their patch-level dynamics. Together, these findings support a computational account in which patch-level narrative dynamics are organized by value-guided foraging over structured memory resources, while revealing graded differences in the strength and stability of these dynamics across human and artificial language generation.

After Math: What Are We Pursuing in Mathematics?

Sat, 12 Sep 2026 21:30:58 +0000

[This is a guest post by Silvia De Toffoli and Eamon Duede . This blog post was initially written in a different file format and converted using AI. — T.] Silvia De Toffoli (University School for Advanced Studies IUSS Pavia) Eamon Duede (Princeton University and Purdue University) On September 8th, 2026, OpenAI announced that it had produced an AI-generated solution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems. The announcement kicked off debate over credit allocation and the respective contributions of humans and machines to the result. Moreover, the announcement intensified already circulating comparisons with earlier AI conquests in domains believed to otherwise exemplify human intellectual prowess. In a recent statement , Tristan Buckmaster, one of the mathematicians involved in the Navier–Stokes saga, wrote: “This is a Deep Blue–Kasparov moment.” Existential questions for mathematics follow naturally: if AI can now provide answers to questions at the very frontier of mathematics, is the discipline on the verge of being “solved” as many have said of chess and Go? Like chess and Go players, should mathematicians just “keep playing” and rearrange their practices? There is something right about the “keep playing” response. As philosopher C. Thi Nguyen ( 2019 ) has been insisting, the purpose of playing a game is not exhausted by its aim (winning). The real point is not only the outcome but the process. This is perhaps clearer with a party game such as Twister than with chess: the aim of playing Twister is certainly not winning. But something similar also applies to deep intellectual games, like chess and Go. For instance, playing a game of Go well can be an achievement in defeat. But in the context of mathematical practice, this feels like an unnecessary retreat. Instead, we can make a stronger move: reject the characterization of mathematics as a game that makes the retreat seem necessary in the first place . The question, then, is not simply what comes after math, once AI can answer its hardest questions. It is also what we are after when we do mathematics in the first place. The narrative that AI has “solved” mathematics rests on two assumptions, both seductive and plausible, but both wrong: AI really did solve a problem in mathematics. Mathematics is only about solving problems. The first assumption…

‘Immature playground boasting’: Mathematicians uneasy at OpenAI’s latest scalp

Sat, 12 Sep 2026 12:50:00 +0000

It was a week that left mathematicians reeling. Hot on the heels of a flurry of cases of artificial intelligence furthering the field, OpenAI declared a major scalp: its latest AI model had cracked a Millennium Prize Problem, a puzzle with a $1m reward that had defied human brains for decades. The achievement bore little resemblance to how mathematical problems normally fall. A near-trillion dollar private company had unleashed 10,000 agents – AI systems that carry out tasks autonomously – on the problem. The bill was estimated at $15m. The trajectory towards AI capable of superhuman maths has long been clear, but to nail such a substantial problem so swiftly sent shock waves through the field. Mathematicians are asking what will be left for them if works in progress are hoovered up and claimed by others, and how they should train the next generation when even fiendish assignments can be solved at the press of a button. “I feel slightly shell-shocked,” said Prof Colva Roney-Dougal, the head of pure mathematics at the University of St Andrews. At a recent public lecture, she said she believed that AI was unlikely to do anything remarkable soon. “Three months later, I’m totally wrong,” she says. “We’re all just waiting to see what happens. It’s coming so fast.” Prof David Silvester, a mathematician at the University of Manchester, said the field felt “very unstable” given the pace of change. “All the open maths problems could fall with enough resources,” he said. “This is irreversible. This is not going to change.” Prof James Robinson, a mathematician at the University of Warwick, said hard problems were the fuel of mathematics, driving creative approaches across successive generations. “It’s frustrating to see these big tech companies burning this fuel up just so that they can show off about how great their latest model is,” he said. “It seems like immature playground boasting writ large, underpinned by billions of dollars and the potential for significant environmental damage in an age when climate change is probably the biggest challenge we face.” One reason Silvester went into maths was the rush that comes from cracking a problem. For many mathematicians it is the weeks, months and years spent circling a problem, breaking it down, trying one approach after another, and never quitting that appeals. “Without that, the subject seems completely different to m…

GPT-6 Astra Clears the Last FrontierMath Tier 4 Problem, Prompting Epoch AI to Call the Tier Saturated

Sat, 12 Sep 2026 07:33:54 +0000

Epoch AI says FrontierMath Tier 4 is now "saturated": by its accounting, when attempts across different models and different time periods are accumulated, every Tier 4 problem has been correctly solved by an AI at least once. OpenAI reports a score of 97.6% for GPT-6 Astra, which solved the one problem that had never been cracked before. When Tier 4 launched on July 11, 2025, the top score on the board was only about 5%. Jay Pantone, an associate professor of mathematics at Marquette University and one of the problem setters, said earlier AIs tended to find numerical shortcuts, whereas this solution was quite close to his own. Tier 4 was later revised: a v2 released in June 2026 corrected 12 problems and removed 7, leaving 43. The project has since added Open Problems and FrontierMath Erdős, where Astra solved only 2 of 68 problems.

25 Fields Medalists, Including Terence Tao and Deng Yu, Warn of "Severe Misalignment" Between AI Firms and Mathematics

Sat, 12 Sep 2026 04:53:58 +0000

Twenty-five Fields Medalists, with Terence Tao and Deng Yu among the first signers, issued an urgent public statement arguing that the way AI companies are pursuing mathematics is now in "severe misalignment" with the goals of the mathematical community. The statement explicitly does not call for stopping AI or deny its achievements. It raises three concerns: AI is solving problems faster than the community can digest the knowledge; AI firms optimize for benchmarks while mathematics values methods, understanding and transmission of knowledge; and once open problems are solved quickly, priority, training and open culture need new rules. Triggering the letter was OpenAI's claim of a Navier-Stokes result produced by 10,000 agents running for 88 hours.

Thought without Systematicity? Evaluating Reasoning Models on Rule Induction Tasks

Sat, 12 Sep 2026 00:00:00 +0000

A central tenet of human cognition is systematicity, the principle that understanding one concept is inherently tied to understanding close variations of that concept. Do reasoning models robustly exhibit such systematicity? If so, we would expect consistent performance on structurally equivalent variants of the same task. Here, we extend established rule induction tasks from cognitive science to assess the systematicity of thought in current reasoning models. Each task family has compositional structure that we use to create structurally equivalent task variations through task isomorphisms such as recombination and substitution. We find that despite being able to correctly solve a task, models often fail on structurally equivalent variants of the same task. These findings suggest that many model behaviors lack systematicity, rendering it difficult to robustly establish the cognitive abilities of reasoning models beyond the particular contexts they were evaluated in.

SAIR competition: Andrews–Curtis challenge

Fri, 11 Sep 2026 16:00:52 +0000

[This is a guest post by Lucas Fagan . This blog post was initially written in a different file format and converted using AI. — T.] I am excited to announce the Andrews–Curtis Conjecture Challenge , which opens today. This challenge is a collaboration between the SAIR Foundation and the Math-AI group at Caltech , organized by Sergei Gukov, Terence Tao, and myself. The Andrews–Curtis conjecture is one of the most prominent open problems in combinatorial group theory and also has deep connections to low-dimensional topology. Its potential counterexamples are relevant to the search for exotic smooth four-spheres and the smooth four-dimensional Poincaré conjecture , as well as the Generalized Property R conjecture about surgery on links . Yet unlike many open problems at its level, Andrews–Curtis can be formulated as a combinatorial search problem with easily checkable solutions, making it ideal for a challenge of this form. At Caltech, we have been developing reinforcement learning and combinatorial search methods for this problem to resolve potential counterexamples (see What makes math problems hard for reinforcement learning: a case study and The Two-Hump Problem ). However, many important cases have resisted all our efforts. We hope that this challenge will lead to resolving these and more (or disproving the conjecture), especially given the recent progress in AI. We give more details about the different tracks of the competition below. To briefly introduce the problem: the Andrews–Curtis conjecture says that any balanced presentation of the trivial group can be transformed to the trivial presentation using the following moves (which do not change the underlying group): (AC1) Invert a relator: . (AC2) Multiply a relator by another: for some . (AC3) Conjugate a relator by a generator or its inverse: for some . Two presentations connected by these moves are called AC-equivalent ; a presentation that is AC-equivalent to the trivial presentation is called AC-trivial . As a simple example, is AC-trivial: It is generally suspected that the conjecture is false ; there are many simple potential counterexamples in which all computational efforts have failed to find a path to the trivial presentation. The most notable of these is the Akbulut–Kirby family whose AC-triviality is open for . Indeed, , with total relator length , is the shortest possible counterexample…

Controversy over OpenAI's claimed Navier-Stokes breakthrough

Fri, 11 Sep 2026 11:17:19 +0000

The controversy over OpenAI's maths miracle ‘Historic’ mathematical breakthrough by the tech company has been overshadowed by data privacy concerns of human researchers This year has been one of “unthinkable” progress for maths, said The Wall Street Journal. But OpenAI’s proof of the Navier-Stokes problem – one of the seven esteemed Millennium Prize Problems accepted as maths’ “deepest and most difficult unresolved questions” – “represents the most significant advance yet”. The tech company took around three days to find a solution with an internal model that is “significantly more capable” than its most advanced commercial model, said New Scientist. It first used 1,000 AI agents to crack a similar problem, then around 10,000 to complete the task. If a customer wanted to run the same problem, it would cost them “around $15 million” (£11 million). However, there has been a “dispute” about how the solution was reached. Two human researchers had announced they had “cracked three significant problems” linked to the Navier-Stokes problem, also using OpenAI and Anthropic large language models, which has raised questions over the privacy and integrity of the data integral to the breakthrough. The Week Escape your echo chamber. Get the facts behind the news, plus analysis from multiple perspectives. Sign up for The Week's Free Newsletters Join more than 350,000 subscribers and keep yourself informed with a selection of The Week’s most interesting, enlightening and entertaining stories - plus daily puzzles. ‘Potentially historic breakthrough’ The “Navier-Stokes existence and smoothness problem” essentially looks at equations which describe the movement of liquids and gases, said The New York Times. These equations are used to predict the weather and “model the behaviour of ocean currents or the climate as a whole”. The problem challenges whether, theoretically, the “laws of physics would break down under certain extreme conditions”, such that water, for instance, could “spontaneously explode”. This “potentially historic breakthrough” has been “shadowed by questions over unpublished research by outside mathematicians”, said Axios. New York University mathematician Tristan Buckmaster and Anthropic researcher Levent Alpöge had been using large language models, including OpenAI’s Codex, where information was stored privately. Just hours before OpenAI’s announcement, the…

The State of AI Antibody Design

Fri, 11 Sep 2026 02:29:45 +0000

I often say that I think that antibody design is one of the better use cases for AI drug discovery at present, because you start with a lot of inherent advantages: antibodies as a class are evolutionarily optimized for long plasma half-lives, and the long list of approved antibody drugs shows you that success is out there to be had. But this is a relative scale! I still believe that antibodies are one of the more realizable AI design stories, but that doesn’t mean you can just walk over and start scooping up the profits. There are still a lot of things that can go wrong, starting with picking the right target (the recent anti-IL-6 failure for cardiovascular indications is an example of that one). Then you have to worry about specificity for your target, because “It’s an antibody” is not a plan to get around that step. And there’s immunogenicity: having an antibody be recognized as a foreign protein and then setting off an immune response against it just seems unfair, considering how antibodies in general are supposed to be sort of a privileged class in that regard. But it most certainly does happen. And given the variability of the immune system across a large patient population, it surely will happen with some people. But how many? And how bad will the consequences be? That’s up to you to find out, and computational methods are going to be of limited help there. So it’s still hard and still expensive, and this recent paper is a good illustration of that. It’s a report on the AIntibody challenge , where 511 AI-designed antibodies (from 29 different organizations) were put through a series of rigorous tests. All of them target the SARS-CoV-2 receptor binding domain (RBD), about whose structure we know far, far more than anyone ever would have wanted to before the pandemic. There were three different sorts of challenge here. The first was affinity maturation of a known antibody sequence recognizing the RBD, using a set of improved sequences from yeast display assays. Only the parent antibody’s affinity was provided - it itself had been experimentally improved, but the matured sequence from that effort was not provided to the participants. The idea was to improve affinity without compromising other known developability factors. The results here was that the overall distribution of the AI submissions were worse than the experimentally-derived antibodies. Most (…

Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model

Fri, 11 Sep 2026 00:00:00 +0000

Visual goal and dynamics prediction can provide language-conditioned robot policies with both a target outcome and a representation of action-dependent scene changes. We bring these predictions into action generation and selection through a shared trajectory model. Dynin-Robotics implements this formulation on Dynin-Omni, an omnimodal masked-diffusion backbone, representing language, visual observations, goals, and actions as discrete tokens. By varying conditioning and target spans, the same model learns action prediction, action-conditioned next-observation prediction, terminal goal-state prediction, and trajectory-to-instruction reconstruction. These interfaces support test-time scaling through goal prediction, action-candidate evaluation, and joint refinement of action and future-state predictions. We continually pretrain the model on approximately 1.33 million trajectories from 48 Open X-Embodiment datasets and adapt it separately to downstream domains. On two VLABench tasks, robot pretraining improves adaptation within a fixed Stage-2 step budget, and the full objective mixture improves shifted-instruction success over Policy-only post-training under the same coupled decoder. Combining goal guidance with joint action-next-state denoising further improves shifted-instruction success over action-only decoding; the benefit depends on how the predictions are composed. Dynin-Robotics achieves competitive performance on LIBERO and zero-shot LIBERO-Plus, together with a 78.4% average success rate across four manipulation conditions on a Franka Research 3 robot. An optimized block-parallel implementation accelerates model-side action decoding by up to 29.2x relative to the base implementation under the reported profiling setup. These results support shared trajectory modeling as a common interface for learning complementary robot objectives and composing their predictions during control.

Expert-Space Exploration in MoE Reinforcement Learning

Fri, 11 Sep 2026 00:00:00 +0000

Reinforcement learning (RL) has become central to post-training of large language models. Recent advances in RL for Mixture-of-Experts (MoE) models have primarily focused on improving optimization stability and training efficiency, while treating the expert selection as a fixed component. Since routing determines the sparse computation paths that induce output distributions, expert selection offers an additional source of rollout diversity. Through empirical analysis, we find that perturbing expert routing effectively alters model output and increases rollout diversity, which is similar to increasing the decoding temperature. However, direct perturbation can activate unsuitable experts and substantially degrade rollout quality. Motivated by these observations, we introduce Expert-Space Exploration Reinforcement Learning (ESRL), an architecture-aware framework that explicitly explores the expert-routing space of MoE models. ESRL preserves high-confidence experts as anchors, and restricts stochastic routing to a plausible candidate pool, thereby retaining reliable computation paths. The perturbation strength is further adapted according to router entropy to avoid over-perturbation. To mitigate the routing mismatch introduced by perturbation, ESRL records the expert paths used during rollout and replays them during policy optimization. Experiments demonstrate that ESRL achieves the best performance across MoE backbones with top-K, top-1, and shared-expert routing, as well as across mathematics, science, and code tasks without additional sampling or computational cost. Specifically, ESRL on Qwen3-30B-A3B achieves the best among all compared methods, improving average Pass@1 and Pass@8 over GRPO by 3.2 and 4.5 percentage points, respectively. Further analyses of expert utilization and training dynamics provide insights into how exploiting MoE-specific routing structure benefits RL training.

Agent as Policy for Robotic Manipulation

Fri, 11 Sep 2026 00:00:00 +0000

We demonstrate that a general-purpose agent can directly drive a physical robot throughout task execution without any task-specific or environment-specific training. We introduce Agent as Policy (AGP), which places task planning and execution under the agent's control. Given a task and a robot interface, the agent interprets visual evidence, writes executable programs, issues motion commands, and revises its actions in response to physical outcomes. This brings the agent's reasoning and programming capabilities into continuous interaction with the physical world. We study AGP across multiple real-world manipulation tasks spanning precision manipulation, dynamic motions, and deformable objects. These include assembly from human videos, block construction from goal images, die reorientation, targeted throwing, and bimanual towel folding. AGP achieves success rates of 100%, 100%, and 80% on three block construction configurations. These findings establish a path for general-purpose agents to act as robotic policies, extending their autonomy to physical manipulation through runtime reasoning, programming, and interaction.

Never Give Up (NGU): Adaptive Sampling to Counter the Matthew Effect in RL for LLMs

Fri, 11 Sep 2026 00:00:00 +0000

We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an LLM is already good at solving, but small improvements on hard problems. We call this the Matthew Effect in RL for LLMs, after the phenomenon of cumulative advantage from economics and network science summarized as "the rich get richer". The naive explanation is that hard problems require more compute to find a solution. We argue that modern RL methods are exacerbating the issue by wasting too much compute on easy problems and instead should dynamically reallocate how they use compute. We introduce Never Give Up (NGU), a simple adaptive sampling method that keeps generating samples for a problem until one is correct. By leveraging asynchronous RL, this naturally uses fewer samples to filter out easy problems and allocates more compute to solving harder problems. We investigate the design choices that affect NGU, such as off-policy robustness, and develop a set of best practices. On the math benchmark Deepscaler, NGU improves performance per compute, especially on harder problems. On a recent coding task, Manufactoria, standard GRPO with a per-test reward fails to fully solve problems that have a range of easy and difficult tests. NGU iteratively improves, solving harder and harder tests, until it learns to fully solve coding problems.

MInTRL: Off-policy Intervention can boost On-policy RL

Fri, 11 Sep 2026 00:00:00 +0000

Reinforcement learning with verifiable rewards is typically performed on-policy, keeping training data close to the current policy but limiting learning to trajectories that the policy can discover itself. Off-policy methods such as supervised fine-tuning, on the other hand, can leverage external knowledge beyond the base model's capabilities, but may suffer from large distribution shift. The key challenge is thus to expand exploration without sacrificing learnability. In this work, we introduce Minimal Intervention Reinforcement Learning (MInTRL), which expands the exploration frontier through sparse, local interventions in otherwise on-policy rollouts. During generation, a judge-intervention policy periodically reviews the current policy's output, replaces erroneous suffixes with short corrections, and immediately returns control to the policy. During training, MInTRL adopts a sequence-level advantage-regression objective that eliminates the need for importance sampling. We show that sparse, local interventions can substantially improve coverage beyond finite-budget on-policy sampling while preserving the overall on-policy nature of the resulting trajectories. Across math and code benchmarks, MInTRL consistently outperforms standard on-policy and off-policy baselines. Ablations show that MInTRL remains effective with self-intervention and across different judge policies, while performance peaks at moderate intervention intensity, highlighting the importance of intervening minimally. These results establish minimal intervention as an effective paradigm for enhancing on-policy RL.

Self-similar study of stable singularity in unforced Euler equations on R^3

Thu, 10 Sep 2026 20:55:27 +0000

[This is a guest post by Anima Anandkumar . This blog post was initially written in a different file format and converted using AI. — T.] The last few days have been a flurry of activity. After months of sleepless nights and diving deep into different mathematical and computational tools to tackle the open problem of singularity in fluid dynamics, we were finally ready to send our work on Euler equations (without forcing) to Tom Hou and Terry Tao for feedback. Almost immediately I heard back from Terry encouraging us to release our work publicly, given that the NYU team had just released their results on forced Euler and OpenAI was rumored to release the next day. We scrambled and put together a blog post and attached our paper with code, which went live about 30 minutes after, in the evening of September 7, the day before the OpenAI’s announcement. A lot of mainstream media religiously followed their press release that fails to acknowledge our work even after we informed them. On the bright side, many researchers have reached out to me. I want to thank Terry for highlighting our work, as well as for giving me the opportunity to write this blog post. The starting point for us is different from the route that the NYU team and OpenAI pursued. Their initial solution has a specific construction with a multi-scale structure with discrete jumps in scales. On the other hand, we consider a self-similar ansatz, and so far, no one has been able to construct a self-similar singular profile for the Euler equations in without forcing. Modified cases such as the Euler equation with a boundary have been successfully solved before ( Chen and Hou ), where the boundary helps confine the singularity, but such techniques fail in free space. We wanted to see if AI could come up with a self-similar profile for Euler. LLMs are not suited for generating such singularity candidates without analytical closed forms. Physics informed neural networks (PINN), on the other hand, can be optimized to find such solutions. Although there have been prior attempts to use PINNs for this case, they were not successful. There are optimization and precision challenges: with standard ansatz and optimizers, a common failure mode is getting the trivial solution. The other challenge is getting precision to be high enough, so we can then attempt to convert the numerical solution into an analytical proo…

Insilico Medicine Doses First Patient in GENESIS-IPF-3 Phase III Trial

Thu, 10 Sep 2026 07:00:00 +0000

CAMBRIDGE, Mass., Sep 10, 2026 — Insilico Medicine (“Insilico,” HKEX: 3696), a clinical-stage biotechnology company powered by generative AI, today announced that it has dosed the first patient with Rentosertib (known as ISM001-055 / INS018_055) in GENESIS-IPF-3, a Phase III clinical trial (NCT07687459, CTR20262475) at Peking Union Medical College Hospital, while Shanghai Pulmonary Hospital has also enrolled its first patient on the same day.

AI-guided design of complete bacteriophage genomes

Thu, 10 Sep 2026 00:00:00 +0000

Artificial intelligence (AI) is increasingly being applied to the design of simple genetic systems, but its ability to generate whole functional genomes has remained untested. Writing in Science, King and colleagues report the generative design of complete bacteriophage genomes. The authors used the genomic language models Evo 1 and Evo 2, which were pretrained on large datasets of DNA sequences, including more than 2 million bacteriophage genomes. Bioinformatic benchmarking revealed that the AI models generated realistic sequences with diversity beyond that observed in nature. The authors then selected the lytic Microviridae bacteriophage ΦX174, which contains a genome of approximately 5.4 kilobases and 11 genes, as a design template. Using supervised fine-tuning of the models on approximately 15,000 Microviridae sequences, the authors generated ΦX174-like genomes, which were then computationally filtered on the basis of sequence quality, tropism specificity and evolutionary diversity, resulting in a curated list of 302 candidate bacteriophage genomes. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription 27,99 € / 30 days cancel any time Subscribe to this journal Receive 12 print issues and online access 251,40 € per year only 20,95 € per issue Buy this article - Purchase on SpringerLink - Instant access to the full article PDF. 39,95 € Prices may be subject to local taxes which are calculated during checkout Rights and permissions About this article Cite this article Ummarino, D. AI-guided design of complete bacteriophage genomes. Nat Biotechnol 44, 1448 (2026). https://doi.org/10.1038/s41587-026-03311-0 - Published: - Version of record: - Issue date: - DOI: https://doi.org/10.1038/s41587-026-03311-0

Segment anything in pathology images with natural language

Thu, 10 Sep 2026 00:00:00 +0000

Nature Computational Science, Published online: 10 September 2026; doi:10.1038/s43588-026-01042-5 This Resource introduces PathSegmentor—a foundation model that segments 160 pathological categories via natural language prompts, generalizes across datasets and clinical cohorts, and enables scalable, interpretable pathology image analysis.

A collaborative agent with two lightweight synergistic models for autonomous crystal materials research

Thu, 10 Sep 2026 00:00:00 +0000

Nature Machine Intelligence, Published online: 10 September 2026; doi:10.1038/s42256-026-01298-6 Shi et al. demonstrate a dual architecture approach for materials research, integrating two lightweight large language models for collaborative reasoning and scientific tool execution. The method achieves competitive performance while remaining affordable and locally deployable.

MutexaGPT: an intuition-to-design translator for physics-based enzyme engineering

Thu, 10 Sep 2026 00:00:00 +0000

Nature Computational Science, Published online: 10 September 2026; doi:10.1038/s43588-026-01049-y MutexaGPT turns plain-language enzyme-engineering intuitions into lead mutation designs through AI agent-orchestrated high-throughput molecular modeling, yielding experimentally validated improvements in enzyme specificity and cold activity.

Beyond Solver Verdicts: Generative Verification for Autoformalization

Thu, 10 Sep 2026 00:00:00 +0000

Neurosymbolic systems rely on mathematical solvers to guarantee reasoning correctness, yet solvers are fundamentally blind to whether a formal translation maintains strict reference-equivalence to a designated formalization. We formalize this vulnerability as Verdict-Preserving-Unfaithfulness (VPU): a failure mode where an incorrect encoding executes successfully and matches the expected verdict. We theoretically prove that structural, verdict-only verification heuristics are mathematically bounded to chance-level detection on these deceptively valid traces. To resolve this, we introduce Generative Verification (GenV), which distills an offline Z3-equivalence oracle into a reference-free, continuous reference-equivalence score by repurposing the language model's native vocabulary space. Mechanistic analysis via decision-projected logit lenses and sparse autoencoders shows this generative readout natively extracts precise spatial error coordinates without explicit localization training. Empirically, our oracle-mined verifier (GenV+HN) achieves 0.961 AUROC in reference-equivalence verification, generalizes zero-shot across unseen translators and divergent formal styles, and yields an 11.3-point downstream accuracy gain in agentic test-time compute allocation.

Deep learning models enable high-performance, real-time emotion decoding from intracranial brain activity

Thu, 10 Sep 2026 00:00:00 +0000

Nature Computational Science, Published online: 10 September 2026; doi:10.1038/s43588-026-01055-0 By integrating intracranial neural signals from gray and white matter, we developed personalized deep learning models that accurately decoded continuous valence and arousal states across image and video viewing tasks. The models generalized across tasks, revealed emotion-encoding brain subnetworks and enabled low-latency real-time decoding, bringing affective brain–computer interfaces closer to being suitable for closed-loop therapy.

Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence

Thu, 10 Sep 2026 00:00:00 +0000

In this technical report, we propose Pelican-Sim 1.0, a general world model simulator for embodied intelligence that predicts future observations from visual context and robot actions to support downstream learning and decision making. The model incorporates four key design features: (1) Unified action representation: a 28-dimensional action value space covering most mainstream embodiments, keeping one model valid across heterogeneous devices. (2) Action-visual injection: URDF- and camera-rendered action videos bridge actions and pixels, giving markedly better controllability across embodiments, scenes, and tasks (PSNR +0.904 over alternative fusion baselines). (3) Sparse mixture-of-experts (MoE): sparse MoE layers add capacity for heterogeneous dynamics and absorb the action modality while reducing inter-modality conflict (FVD -6.530 vs. the dense backbone). (4) Efficient rollout generation: causal adaptation and few-step distillation yield a four-step autoregressive simulator, achieving a 5.67-fold speedup over the 35-step model. Benefiting from these designs, we train on approximately one million real-world and simulated trajectories and obtain large gains in action controllability and video quality: PSNR improves over the strongest evaluated baselines by 4.636 on AgiBotWorld Beta, 2.080 on RoboMIND, and 10.343 on RoboTwin, with the adapted EWMBench DYN score up 0.426 on RoboTwin. Relying on this, four downstream applications on RoboTwin succeed: 500 generated trajectories added to 50 demonstrations per task raise policy success from 70% to 93%; policy evaluation reaches a Pearson correlation of 0.994 across five checkpoints; and relative success gains reach 47.7% for action selection and 20.3% for policy improvement. Qualitative generalization across trajectory, scene, object, embodiment, and viewpoint shifts highlights its potential as a general-purpose world model simulator.

Feature Recovery for Object Understanding After Irreversible Fire Damage

Thu, 10 Sep 2026 00:00:00 +0000

Objects in post-fire environments often undergo irreversible physical transformations that change their geometry, material state, and visual appearance. Detecting and identifying these remnants is critical for locating hazards, reconstructing pre-incident contents, and inventorying losses. Unlike standard image corruptions, these degradations affect the physical structure of the object itself. To study this setting, we introduce TRACE, a transformation-aware benchmark for post-fire object understanding. TRACE contains 21.4K real-image-grounded synthetic scenes and paired object-level pristine-to-degraded progressions spanning 499 object identities across 189 categories. We define five tasks targeting localization and pre-degradation understanding: degraded-object detection, pristine-state recovery and retrieval, original material recovery, pristine description generation, and functional reasoning. Existing models degrade sharply with severity. From the least to the most severe level, RF-DETR mAP decreases by 71% relative, while InternVL3.5 retrieval R@1 falls from 93.85 to 28.11. To address this, we propose the Feature Recovery Module (FRM), a plug-and-play module that maps degraded encoder features to pristine-aligned representations while keeping the host frozen. Trained only with paired feature supervision, FRM improves scene-level detection, CLIP/SigLIP2 feature recovery, and all four object-level VLM tasks, with larger gains under more severe degradation. Across VLM hosts and severity levels, relative gains average 12.5% for retrieval, 20.1% for material recovery, 13.2% for description generation, and 12.4% for functional reasoning.

Google DeepMind maps 9 billion DNA mutations: how AI could help solve rare diseases

Wed, 09 Sep 2026 07:00:00 +0000

Google DeepMind has created an AI-powered map of about 9 billion possible single-letter changes in human DNA, giving scientists a new way to search for genetic mutations that could matter for disease and biology. Called AlphaGenome Atlas, the platform contains precomputed predictions for every possible single-nucleotide variant in the human genome. DeepMind says testing billions of possible changes individually in laboratories would be practically impossible. The company has made the resource available for academic research through a free website, alongside its AlphaGenome API. What does AlphaGenome Atlas actually do? Think of it as a giant search map for DNA. The Atlas uses predictions from AlphaGenome, DeepMind’s AI model designed to predict how genetic variants affect biological processes. It covers both the part of DNA that produces proteins and the much larger non-coding portion that helps control gene activity. DeepMind says the Atlas is a 1-petabyte dataset, more than 30 times larger than the AlphaFold Database. It also introduces an AlphaGenome Variant Impact (AVI) score. The score gives researchers a single way to rank potentially important genetic changes and investigate what biological processes they may disrupt. Why could 9 billion DNA changes matter? The human genome contains an enormous number of possible variations. Finding the few changes that actually matter for a disease can be like searching for a needle in a haystack. DeepMind says researchers have already used AlphaGenome Atlas in rare-disease research and studies of common traits. Trending Stories In one example, researchers working with the Broad Institute used the AVI score to prioritise a variant linked to DNM1, a gene associated with epileptic encephalopathy. Experimental work then validated the predicted effect. Another study using data from more than 54,000 UK Biobank participants identified 22% more non-coding genetic associations by grouping variants according to their predicted molecular effects. What happens next? DeepMind says AlphaGenome Atlas is a starting point rather than an endpoint. As its AI models improve, the company expects the predictions to become more comprehensive and precise. The goal is not to replace laboratory research. Instead, the Atlas could help scientists decide which genetic changes deserve closer investigation first. That could speed up research into…

OpenAI-linked accounts claim ~10,000-agent AI effort produced a Navier–Stokes singularity result, drawing both interest and skepticism

Wed, 09 Sep 2026 05:04:51 +0000

Today was a tough news cycle to launch anything; we ordinarily promise to cover any new decacorn fundraises so Cognition’s $48B round and Mistral’s $24B round would normally have made it; we love imagegen so GPT Image 2.5 would have been its own headline; we covered the Dreamer story closely so their relaunch as Meta’s Muse agent should have made it; but.. yknow… the bar is higher these days. The summaries below capture the substantive facts; we recommend not looking too deep into the authorship drama as OpenAI and the authors have pretty much laid out enough detail to conclude that OpenAI’s achievement is real though the process is in some despute. AI News for 9/7/2026-9/8/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space . You can opt in/out of email frequencies! AI Twitter Recap OpenAI-affiliated accounts said an AI-assisted effort produced a Navier–Stokes result, and the reaction immediately split between technical interest, skepticism, and meta-drama. The most concrete public claim in the tweet set came from Ethan Knight, who said “The Navier Stokes solution was the result of a collaboration of ~10,000 agents working together,” adding that OpenAI had spent “the past year” training models to collaborate via “multiagent RL,” and that hard problems may yield to “huge amounts of unstructured parallel test-time compute” with models deciding how to organize themselves @ eknight . Multiple onlookers interpreted this as OpenAI claiming an AI-generated proof related to the Navier–Stokes Millennium Problem, specifically around finite-time singularity / blow-up; one satirical paraphrase framed it as OpenAI saying a smooth fluid can “blow up into a singularity,” claiming “10,000 agents” and “88 hours” were used, while explicitly noting that mathematical acceptance remained a “minor formality” @LearnOpenCV . Broader commentary treated the event as a possible stress test for the belief that frontier AI cannot do serious research or coding-level technical work; Theo Jensen called it the science world’s “‘AI can’t ACTUALLY code’ crash out moment” @theo . Hrishikesh / hrishioa framed the announcement as evidence of a “high compute regime,” arguing observers should “adjust your plans accordingly” @hrishioa . The announcement also triggered incident…

ReactHuman: A Physics-Grounded Benchmark for Testing Human-Like Reactive Decision-Making in Embodied Multimodal LLMs

Wed, 09 Sep 2026 00:00:00 +0000

Reacting to sudden physical hazards (catching a slipping plate, dodging a falling knife) is both a meaningful test of embodied intelligence and a hard requirement for deploying multimodal large language models (MLLMs) as the decision coreof household robots. Existing evaluations, however, probe intuitive physics passively through question answering over videos, or target deliberate, long-horizon tasks such as navigation and rearrangement; none measure whether a model can turn physical understanding into immediate, safety-critical action. We introduce ReactHuman, the first physics-grounded benchmark for human-like reactive decision-making, in which the evaluated MLLM acts as the brain of a simulated humanoid facing sudden household hazards; it spans 17 event families and over 1,000 bit-for-bit reproducible scenes with exact, annotation-free ground truth derived from 240 Hz rigid-body simulation, including adversarial objects whose appearance contradicts their physics (a foam anvil, a steel apple). We further design a five-metric suite that scores each reaction along three axes: reasonable, safe, and physically grounded. We physically execute every committed plan so that decisions have observable consequences. With this harness we evaluate seven representative MLLMs. Results show that reactive safety is far from solved: models mishandle roughly one hazard in three, act from fixed dispositions rather than the observed scene, trust appearance over motion, and miss interception points at meter scale even when the chosen action is correct; none of these failures shrink with model scale. ReactHuman thus offers both a fine-grained diagnosis and a scalable training signal toward physically grounded, safety-aware embodied agents. The benchmark can be found here: https://huggingface.co/datasets/Alan123/reacthuman-benchmark-scaled

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

Wed, 09 Sep 2026 00:00:00 +0000

A research idea may be novel, coherent, and scientifically plausible, yet its proposed method may remain insufficiently specified for faithful implementation. We study the codification readiness of implementation-facing research-method specifications, defined by whether they provide sufficient methodological information for a competent implementer or coding agent to construct the intended method without unsupported assumptions. We construct evidence-grounded specifications and their supported resolutions from papers, codebases, issue threads, and reproduction artifacts. We introduce IdeaAMBIG, a benchmark of 660 evidence-grounded instances: 163 real-world gaps from reproducibility reports and GitHub issues, and 497 controlled synthetic gaps injected into codification-ready references. IdeaAMBIG evaluates three capabilities: codification-readiness assessment, defect localization, and clarification action generation. Defect localization receives only the specification, whereas clarification additionally receives the annotated defect. Across 13 LLMs, the best model achieves 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% Macro Clarification Action Success Rate when given the defect. In an oracle study, supplying the gold resolution raises the downstream codification-ready rate from 14% to 98%. Across all evaluated models, defect localization is the main bottleneck, with stronger clarification given the defect.

AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome

Tue, 08 Sep 2026 14:00:15 +0000

AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome How predicting the molecular impact of every possible single-letter DNA variant in the human genome will help accelerate our understanding of biology. Today, we are introducing AlphaGenome Atlas: a platform (detailed in our paper) containing predictions for the effects of 9 billion single-nucleotide variants — every single-letter change possible — in the human genome. It is the most comprehensive catalogue of how genetic mutations affect molecular biology, and it is available for academic research through an intuitive and free-to-use website portal. DNA is the language of life. Mastering it is a grand challenge that could transform our ability to understand biology and treat disease. But progress has been limited by a fundamental problem: interpreting how genetic variations impact biology at a molecular level. With roughly 9 billion possible single-letter mutations in the human genome, testing each one in the lab is practically impossible. Google DeepMind has already made progress on this challenge with AlphaGenome, an artificial intelligence (AI) model that can predict how genetic variants impact biological processes. AlphaGenome is helpful for analyzing specific variants and has found widespread use in research, but we wanted to show researchers a big-picture view of variants across the entire genome. By precomputing AlphaGenome’s predictions at scale, we have created an easily accessible resource that vastly expands the model's reach. Just as an atlas is a collection of maps, linking together features of the land like altitude and location, AlphaGenome Atlas charts the molecular effects of DNA variants across the genome. To help scientists quickly find the most impactful genetic changes, we are also releasing the AlphaGenome Variant Impact (AVI) score. The AVI combines the strengths of AlphaGenome and AlphaMissense — our model for predicting the impact of protein-altering DNA variants — condensing both models’ predictions into a single number. Now, researchers can rapidly rank variants and interpret their molecular effects at the same time. Our trusted external collaborators have already used AlphaGenome Atlas to identify and experimentally verify key variants in unsolved rare disease research and find rare variants associated with common traits. AlphaGenome Atlas…

Google DeepMind publishes AI-powered predictions for effect of all 9B mutations to human DNA

Tue, 08 Sep 2026 07:00:00 +0000

Google DeepMind said Tuesday that it has used artificial intelligence to predict the biological consequences of all 9 billion possible single-letter changes to human DNA, and is making the resulting database available free to academic researchers worldwide. AlphaGenome Atlas, as DeepMind calls the database, is a precomputed catalogue of what each substitution of a single DNA base is likely to do to the machinery that switches genes on and off. Until now researchers had to run such a model one variant at a time or had to test variants in the laboratory, a process that was painstakingly slow. It would have taken many human lifetimes to discover the consequences of all 9 billion possible single-letter mutations. The Atlas promises to make the job of biologists and medical researchers considerably easier, potentially speeding up the understanding of genetic diseases and the hunt for possible cures. Pushmeet Kohli, DeepMind’s vice president for research and head of its AI for science team, told reporters on a briefing call that this was the first time any researcher in the world could reach a comprehensive map of human genetic variation “by simply opening a browser.” Kohli also framed the release as helping to complete the unfinished business of the Human Genome Project, which in 2003 succeeded in mapping the entire human DNA sequence. “As the saying goes, we bought the book,” he said, “but we did not understand how to read it.” Atlas is available for non-commercial use from today through a website Google DeepMind has set up for it. The company said it would be available for commercial use through a licensing arrangement through Google Cloud “soon.” Kohli said that Google DeepMind’s sister company, Isomorphic Labs, which is using AI for drug discovery, would have access to Atlas but that it would also require a commercial license for access. He did not specify exactly what the terms would be for commercial licensing. A paper describing the Atlas and how it was created is being released on bioRxiv, a repository for biomedical preprint academic papers. Helping understand mutations in DNA’s vast ‘non-coding’ segments DNA provides the recipe for the proteins a living cell can make. It consists of two chains of nucleotides, or molecules containing nitrogen, that coil around one another to produce a double helix. The nucleotides in DNA are formed of one of four differ…

Finite time blowup with smooth forcing term for the incompressible porous medium, Boussinesq, and incompressible Euler equations

Tue, 08 Sep 2026 05:13:17 +0000

There’s some exciting very recent work by Alpöge and Buckmaster , building upon prior work by Córdoba and Martínez-Zoroa , in the general topic around the infamous global regularity problem for the incompressible three-dimensional Navier-Stokes equations . It is now widely expected that it should be possible to construct smooth initial data and smooth forcing term that would make these equations develop singularities in finite time; and it should even be possible to do without the forcing term. While these authors do not quite achieve these goals yet, they have made enough of a breakthrough that it looks very feasible to complete these goals in the near future. In particular, they have demonstrated such finite time blowup for three simpler model equations: the incompressible porous medium (IPM) equation, the two-dimensional Boussinesq equation , and the three-dimensional incompressible Euler equations . (The first of these equations was already handled by Córdoba and Martínez-Zoroa, but Alpöge and Buckmaster found a variant of their method that also extended to the other two equations, and has a high likelihood of also extending to Navier-Stokes as well.) As is now remarkably feasible in the modern era of autoformalization agents, their work has also been formalized in Lean. As one may expect nowadays, the arguments here are heavily AI-assisted, but the authors have been working over the last few weeks to simplify and rewrite the proofs from what they literally call “the worst writeup we had ever seen in the history of mathematics” into something far more readable and of professional quality. This is still a work in progress: unfortunately, they were forced to release their preliminary preprints before they were completely digested and polished, due to external events that are documented on the above link. Nevertheless, the introduction to the Boussinesq paper at least is in pretty good shape, and can serve as an initial starting point. I was also fortunate to have Tristan Buckmaster explain the main ideas of the paper in a half-hour phone conversation, although I still need to work some more (probably with some combination of a blackboard and modern AI tools) to digest things more. For now I will try to write a quick summary of some of the main ideas, the highlighting of which I view as the main value of such work; the actual solving of these problems is o…

Flow matching for spatio-temporal downscaling with Anemoi

Mon, 07 Sep 2026 08:00:00 +0000

Machine-learning models can now produce highly skilful weather forecasts. For example, AIFS-CRPS (which now runs operational as AIFS ENS at ECMWF), GenCast, FourCastNet 3 and Aurora demonstrate that data-driven systems can generate skilful probabilistic predictions at medium-range timescales. At the same time, machine learning can increase the spatial and temporal resolution of existing forecasts. Spatial downscaling models such as Anemoi-D² learn to generate fine-scale atmospheric structure from coarse-resolution inputs. Temporal downscaling models such as HourGlass reconstruct realistic hourly evolution between forecast states that are available only every six to 12 hours. Forecasting, spatial downscaling and temporal downscaling can be treated as separate problems, allowing each component to be evaluated and improved independently. They can also be combined in different ways. Here, we explore one such combination: spatial and temporal downscaling are performed by a single machine-learning model, which can then be coupled to a separately trained machine-learning forecasting model. Adding detail in space and time The spatio-temporal downscaler is a flow-matching model, a generative machine learning technique, trained using the Anemoi framework, co-developed by ECMWF and several national meteorological services across Europe. The spatio-temporal downscaler transforms fields with low spatial and temporal resolution into higher-resolution, hourly sequences. It is trained on ERA5, ECMWF’s fifth-generation global reanalysis. Input data is provided to the model on an O96 grid, which corresponds to a horizontal resolution of approximately 100 km, at six-hourly intervals. The model then generates outputs on the N320 ERA5 native grid, which corresponds to a horizontal resolution of approximately 30 km, with one-hourly time steps, in chunks of five steps at a time. It therefore performs two tasks at once: - spatial downscaling from 100 km to 30 km - temporal downscaling from six-hourly to hourly fields Through flow matching, the model learns a conditional distribution of fine-scale spatial and temporal features that are consistent with the coarse input state. During training, it receives the coarse input together with a high-resolution ERA5 target that has been corrupted with Gaussian noise at a randomly selected noise level. It learns the direction in which this in…

Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning

Mon, 07 Sep 2026 04:00:00 +0000

This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a zero-shot framework, without labeled training data. The problem is formulated as a binary severity classification task across three forecast horizons (3h, 6h, 12h), using six years of outage records and high-resolution weather data for a utility service area in central Texas. Four zero-shot LLMs are benchmarked against two supervised classifiers across two input configurations: one using current weather observations and the other using weather forecast data. Results show that supervised models outperform LLMs on macro-F1 and precision, while newer LLM generations achieve competitive scores. Beyond accuracy, LLMs offer complementary strengths in actionable reasoning and geographic scalability, suggesting that combining them with supervised models may be the best practice.

Voltage-embedded equivariant machine learning potential for open system simulations

Mon, 07 Sep 2026 04:00:00 +0000

Modeling electrochemical interfaces under operational non-equilibrium conditions is vital for energy technologies but remains bottlenecked by the expensive cost of ab initio methods. Current machine learning potentials, largely designed for closed systems under homogeneous electric fields, are limited in open quantum transport applications. To overcome this, we present an E(3)-equivariant graph neural network that embeds voltage bias for open-system simulations. Our approach decouples the system energy and forces into zero-bias and bias-dependent contributions, assigning distinct vector encodings to electrode and scattering-region atoms to capture non-equilibrium conditions. Trained on limited discrete bias data, the model achieves high predictive accuracy and robust extrapolation transferability. When applied to a lithium/water interface, our model successfully captures the field-induced dynamic reorientation of water molecules and reproduces asymmetric electrochemical behavior at different electrodes.

The physical foundation beneath protein generative modeling

Mon, 07 Sep 2026 04:00:00 +0000

Physical equations in protein modeling appear to have been replaced by generative models trained directly on structure data. By learning a mapping from noise to data, sampling de novo protein structures has become much more efficient. However, such models can also learn non-physical features and break down with out-of-distribution settings which are typical in protein design campaigns. In this review, we highlight where the physics persists in protein generative modeling pipelines and the issues that linger when physically relevant components of a macromolecular system are left unmodeled. We present a perspective that respecting the underlying physics of macromolecular systems, increasingly through learned representations that are physically grounded and updating generative models with experimental data, is foundational to generative modeling for functional protein design.

La Agente Óptima: Towards Agentic Self-Driving Laboratories

Mon, 07 Sep 2026 04:00:00 +0000

Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate scientific discovery. Their operation nevertheless often depends on human specialists who translate scientific objectives into executable closed-loop campaigns. Specialists adjust them as data and operating conditions change. Here, we present La Agente \'Optima, an agentic framework that constructs and supervises Bayesian optimization campaigns across computational and experimental systems while maintaining a persistent optimization state. By separating large language model (LLM) reasoning from executed campaigns, \'Optima runs repetitive optimization loops consistently, returns control to the agent only when progress requires interpretation or campaign revision, and keeps every decision auditable. We evaluate \'Optima across ablation studies, five digital discovery tasks, and two physical platforms. Throughout, \'Optima maintained executable campaigns as both the scientific problem and execution environment evolved. In a closed-loop contact angle optimization campaign, \'Optima identified and corrected a mid-run measurement failure, bringing the contact angle from 71.4 to 67.8 degrees, just above the 64-66 degree range. From this result, \'Optima correctly inferred that the target was likely unattainable with the available reagents and recommended changing the formulation. In a five-day multi-objective flow-chemistry campaign, \'Optima increased the yield from 30% to 59% over 23 experiments. Despite substantial inference costs, it cost less and used substantially less starting material than a human-directed campaign, while selecting a more mass-efficient operating point. These results show that LLM-based agents can make rigorous, long-running optimization campaigns accessible to domain scientists without specialist setup, expanding the scope of SDLs.

Shallow neural network approximation in mixed Sobolev spaces

Mon, 07 Sep 2026 04:00:00 +0000

We investigate the best $L_2$ approximation of mixed Sobolev spaces by shallow neural networks with $n$ neurons and general activation functions. We first establish an activation-independent Fourier-block principle: if an activation has univariate approximation order $\rho$ in the sense of the Fourier-block property, then the global approximation rate has algebraic order $\min\{\alpha,\rho\}$ for target functions of mixed smoothness $\alpha$, up to explicit logarithmic factors. To verify this property for concrete activations, we introduce a structured univariate approximation condition that implies the Fourier-block property with explicit parameters. For $\mathrm{ReLU}^k$, a matching algebraic lower bound identifies $\min\{\alpha,k+1\}$ as the optimal algebraic approximation exponent in any dimension, up to logarithmic factors in the upper bound. The framework also yields the exponent $\min\{\alpha,k+1\}$ for cardinal B-splines and soft-$\mathrm{ReLU}^k$, and the full mixed-smoothness exponent $\alpha$ for ELU and cosine activations, again up to logarithmic~factors.

Audit of label leakage and input-invariant MetaGNN models for Recon3D metabolic reaction activity scoring

Mon, 07 Sep 2026 00:00:00 +0000

Context-specific genome-scale metabolic modeling begins with scoring which of the approximately 10,600 human reactions are active in a patient's tumor. Methods in this literature are routinely benchmarked against activity labels obtained by thresholding the same transcriptomic matrix that is supplied to the model as input. We report a self-audit of our own graph attention scorer, MetaGNN, evaluated on TCGA colorectal (n=624), breast (n=1,095) and lung adenocarcinoma (n=517) cohorts, in which two independent failure modes produced a near-ceiling benchmark score and a positive architectural result, neither of which survived inspection. First, under expression-thresholded supervision the framework reaches AUROC 0.9864 +/- 0.0008 on TCGA-BRCA. That figure partitions into 5,925 reactions whose labels are a deterministic threshold of the model's own input, where ranking by the cohort-mean input alone gives AUROC 1.000; and 4,675 reactions whose stored labels we reproduce bit for bit from a seeded pseudo-random number generator, where the model nonetheless reaches 0.9291 +/- 0.0030 by memorizing a patient-invariant label vector that patient-level splitting leaves fully visible during training. Second, on the cohort supervised independently of the input, the archived models never received patient data at all. Their released feature tensors are uniformly zero, and independently trained models show no agreement on which patient deviates where (|r| < = 0.004 on per-patient output residuals, against r = +0.32 between output and input residuals on expression-bearing reactions for a model with verified features). A dispersion ratio comparing between-patient output spread against Monte Carlo Dropout sampling spread sits at 1.02 to 1.03 for all three configurations, against a no-signal null of 1.02 and 2.44 for the verified model. We therefore withdraw a +0.105 AUROC gain attributed to relational edges in an earlier draft of this work. Retraining on rebuilt, verified features gives AUROC 0.5800 +/- 0.0017, below both the raw-expression baseline of 0.6342 +/- 0.0058 that we establish for this cohort and an information-free indicator baseline of 0.6085. Zero-shot transfer of the BRCA model is at or below chance on METABRIC microarray (0.4926 +/- 0.0113, n=200) and on same-platform CPTAC-BRCA RNA-seq (0.4986, n=106). We release the code, the curated colorectal cohort, a scrip…

From Clinical Free Text to Auditable Concepts: An Agentic Framework for Interpretable Prediction

Mon, 07 Sep 2026 00:00:00 +0000

Across application domains, predictive signals often sit in unstructured free text rather than structured fields, yet turning that text into useful and interpretable features is difficult. Running large language models (LLMs) over an entire corpus is costly and hard to reproduce, while end-to-end text representations can rely on surface cues that are difficult to inspect. We present an agentic workflow that takes a prediction task and a raw text corpus as input and produces an auditable feature layer. The first two agents use an LLM to derive a task-specific predictor taxonomy and weakly label a bounded text sample; routed local extractors then process the corpus, and a deterministic builder aggregates the evidence into a dynamic, longitudinal concept bottleneck. We evaluate the framework on medication discontinuation in a longitudinal oncology cohort and 30-day readmission in MIMIC-IV. With gradient boosting, the longitudinal bottleneck increases area under the receiver operating characteristic curve (AUROC) over coarse concept buckets from 0.700 to 0.761 for medication discontinuation and from 0.576 to 0.609 for readmission. The proposed framework achieves predictive performance comparable to direct BioClinicalBERT prediction on both tasks while additionally providing explicit, interpretable, and traceable task-specific concepts. LLM use is confined to a bounded weak-labeling stage costing $24.00 and $23.39, respectively, compared with projected costs of $10,648 and $11,519 for exhaustive sentence-level LLM processing of the full corpora, demonstrating the substantial cost efficiency of the proposed agentic system.

WGCNA+: AI-powered WGCNA for Integration of Multi-Omics Data

Mon, 07 Sep 2026 00:00:00 +0000

Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely adopted systems biology method to discover gene modules and module-trait associations, mostly from transcriptomics. Designed for a single layer, it cannot jointly analyze multi-omics layers, a consequential limitation in modern biomedical research. WGCNA modules are often hard to interpret, requiring vast follow-up for contextualization. Moreover, no integrated framework exists to visualize condition-specific, cross-omics relationships at module or feature level. Results: To address these limitations, we developed WGCNA+, a novel R package extending WGCNA to multi-omics. WGCNA+ offers key innovations: (i) a unified multi-omics pipeline for per-layer network inference and cross-layer module enrichment; (ii) SVD-accelerated topological overlap matrix calculation that greatly reduces computation time; (iii) a consensus framework identifying modules reproducible across independent datasets/conditions; (iv) LASAGNA, a companion R package for phenotype-conditioned, multi-partite graph visualization of cross-omics relationships; (v) AI-powered annotation and infographics offering immediate biological insight. We tested WGCNA+ across public transcriptomics, proteomics, and miRNA datasets. WGCNA+ detects biologically meaningful modules, cross-omics feature and phenotype correlations, and provides AI-powered interpretation that accelerates research. Conclusions: WGCNA+ addresses existing gaps with a principled, efficient framework for co-expression network analysis across omics. It detects cross-omics regulatory modules and their phenotype association to support basic research, biomarker discovery and pathway analysis. It uniquely offers AI-assisted interpretation and infographics, aiding hypothesis generation. Complementing WGCNA+, LASAGNA is a phenotype-aware multi-partite visualization framework to explore cross-omics relationships. Altogether, these features make WGCNA+ an innovative, powerful tool for clinical and translational research. Availability and implementation: WGCNA+ and LASAGNA are implemented in R language for statistical computing, version[≥]3.5. WGCNA+ and LASAGNA are fully and freely available with no restrictions (https://github.com/bigomics/WGCNAplus; https://github.com/bigomics/lasagna)

A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data

Mon, 07 Sep 2026 00:00:00 +0000

Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework. Accordingly, we propose a physics-informed hybrid gated recurrent unit (PI-HGRU) model for spatiotemporal dynamic prediction of rice sheath blight disease, caused by a fungus. Our model embeds differential equations describing disease transmission dynamics into a hybrid gated recurrent unit (HGRU) framework as mechanistic constraints, thereby enabling collaborative modelling between epidemiological processes and data-driven learning. We conducted model training and evaluation using a long-term, multi-source dataset spanning 17 years (2000-2016) and covering 16 major rice-producing provinces in southern China. These rich data include spatiotemporally aligned field disease observations, remote sensing data, meteorological data, and soil property data. In addition, to address the challenges of irregular sampling intervals and inconsistent sequence lengths in disease survey data, we adopted a sliding time-window-based prediction framework. We further conducted a time-window sensitivity analysis to determine appropriate configurations of the input time window and lag time, enabling the model to represent the cumulative and delayed effects of environmental factors. Our PI-HGRU framework substantially outperforms the purely data-driven HGRU baseline model, improving the squared Pearson correlation coefficient (r2) by 22.8% while reducing the root mean square error (RMSE) and mean absolute error (MAE) by 10.2% and 18.0%, respectively. Furthermore, analysis of the models intermediate variables showed that the transmission rate {beta}(t) exhibited interpretable relationships with environmental conditions within the input time window, providing a process-related link between environmental drivers and modeled disease transmission dynamics. Overall, our work demonstrates that integrating epidemiological mechanisms into deep learning models in a physics-informed manner can improve predictive accuracy and stability while enhancing model interpretability, highlighting its potential for large-scale disease forecasting and precision disease management.

Google's WeatherNext 3 Ditches Physics Simulations and Learns Weather Directly from Live Satellite Data

Sun, 06 Sep 2026 10:36:35 +0000

Google Research and DeepMind are releasing WeatherNext 3, a weather model that skips traditional physics simulations and learns directly from real-time satellite data. It produces hourly forecasts at up to five-kilometer resolution, five times more detailed than its predecessor. Google says regions in Africa, Latin America, and the Asia-Pacific that have lacked accurate forecasts should see the biggest gains.

The Coronary Artery Inflammation Controversy

Sat, 05 Sep 2026 15:18:58 +0000

Why did a big clinical trial that tested an anti-inflammatory drug fail to reduce heart events? Why did another pivotal trial of lowering Lp(a) fail to reduce heart events? In this edition of Ground Truths I’ll offer some explanations and introduce some new and relevant information. In part, this is about relying on surrogate endpoints like increased blood levels of interleukin-6 or Lp(a), which have been generally regarded as an accurate reflection of heightened risk for coronary artery events. That may not be true. That these biomarkers are actually indicative of heightened arterial inflammation could be assessed using non-invasive AI quantitative assessment (FAI) of inflammation in the coronary arteries. That has now been done for high-sensitivity CRP (hs-CRP) and the results are informative. An example below is for a right coronary artery (RCA) that is not obstructed (red arrow) by atherosclerosis seen via a dye injection study (angiogram) but is actually highly inflamed (high FAI score right panel]. Subscribe now We’ll address each of the trials separately. First some general background on coronary atherosclerosis and inflammation. Some Background The coronary arteries have been center stage in recent weeks. Presented at the European Congress in Munich last weekend and published at NEJM, a prospective study of over 16,000 people age d 18-70 years found “silent atherosclerosis” (←no symptoms) in 57%, with many starting to show this at the young age of 18 to 29 years: 8.7% in men, 6.7% in women. Prior studies suggest the prevalence in young adults is considerably higher (~4-5 fold using intravascular ultrasound). In JAMA last week, an assessment of calcium scores of the coronary arteries by CT scans questioned their overall utility compared with clinical criteria, recommending selective use. Both new studies underscore the high incidence of coronary atherosclerosis, cholesterol-laden plaque, that is not accompanied by symptoms. An important differentiation: coronary atherosclerosis per se is extremely common, but plaque rupture leading to heart attacks (MI, myocardial infarction) or unstable angina is relatively infrequent (about a 50:1 ratio, US data, Figure below, citation ). Why? Estimated number of Americans with coronary atherosclerosis vs those who have a myocardial infarction (heart attack) with a corresponding schematic of artery cross-sections t…

A Figure-Text Pair Dataset from Map-related Literature for Multimodal Model Research

Sat, 05 Sep 2026 00:00:00 +0000

Abstract High-quality multimodal datasets are essential for developing vision-language models, yet publicly available figure-text resources in specialized scientific domains remain limited. To address this gap, we present a large-scale figure-text pair dataset constructed from map-related scientific literature (FTPD-ML). The dataset was derived from 96,859 scientific publications in cartography, geography, remote sensing, and related disciplines, and provides 75,702 publicly redistributable figure-text pairs under Creative Commons Attribution (CC BY) licenses with multiple levels of textual representations, including original figure captions, standardized caption variants, and contextual paragraph descriptions, together with bibliographic metadata. To ensure compliance with copyright and licensing requirements, records from non-redistributable publications are represented by metadata only. The dataset supports a range of multimodal research tasks, including image-text retrieval, image captioning, scientific document understanding, and cartography-oriented vision-language studies.

Respect where respect is due: Anthropic says Claude completed the first fully computer-checked proof of Fermat’s Last Theorem in 11 days

Fri, 04 Sep 2026 22:50:16 +0000

Respect where respect is due: Anthropic says Claude completed the first fully computer-checked proof of Fermat’s Last Theorem in 11 days. Andrew Wiles proved the theorem in 1995. Claude’s achievement was turning an existing proof into a form where a computer can check every… https://t.co/FcRbJn5GYb pic.twitter.com/OOdfoI2AZD

Another Serious Win for AI in Mathematics: Claude Formalized Fermat's Last Theorem in 11 Days

Fri, 04 Sep 2026 20:00:50 +0000

Another serious win for AI in mathematics: Claude formalized Fermat’s Last Theorem in 11 days. AI may now finally be able to automate the extremely labor-intensive job of turning advanced human mathematics into proofs that software can check line by line. The process took 11… https://t.co/DT5R8t4PbH pic.twitter.com/hRuE9pFCRo

Claude Completes First Formalized Proof of Fermat's Last Theorem

Fri, 04 Sep 2026 18:50:48 +0000

Checking that a major mathematical proof is correct can take years. Formalization—converting the mathematical reasoning into a form computer proof assistants like Lean can verify—can help. Last month, Claude completed the first formalized proof of Fermat’s Last Theorem, one of… pic.twitter.com/pdT8zwlV4A

Formalizing Fermat's Last Theorem

Fri, 04 Sep 2026 15:27:00 +0000

Science Sep 4, 2026 We are sharing the first complete computer-checked proof of Fermat’s Last Theorem. Claude worked largely autonomously over 11 days to write the proof in the Lean programming language. Below, we describe how the formalization was done and share some thoughts about what this work could mean for research mathematics. Around 1637, Pierre de Fermat jotted down a claim in the margin of his copy of Diophantus’s Arithmetica that would become one of the most famous mathematical conjectures of all time: no positive integers a, b, c satisfy aⁿ + bⁿ = cⁿ for any n > 2. Fermat’s Last Theorem (FLT), as the conjecture became known, turned out to be incredibly difficult to prove. The first proof, from Sir Andrew Wiles in 1995, ran to 129 pages and required months of painstaking work to verify. A decade later, Dutch computer scientist Jan Bergstra proposed “formalizing” Wiles’s proof: converting the mathematical reasoning into a form computers can check automatically. Since then, mathematicians have been developing the methods needed to encode such a complex proof, including a multi-year community effort kicked off in 2024 by Kevin Buzzard at Imperial College London to complete the formalization using the Lean proof assistant. Recently, Tianyi Peng, an Anthropic researcher whose group at Columbia University builds tools for AI formalization, set out to test whether Claude could make progress on formalizing FLT.1 The result went further than he expected. In 11 days, working largely autonomously, Claude produced the first end-to-end, computer-checked proof of FLT. Along the way, it wrote 13 million lines of Lean and proved 29,500 intermediate theorems. We shared the resulting proof with Kevin Buzzard, who said: This extraordinary autoformalization achievement, which Anthropic researchers say only took 11 days, proves Fermat’s Last Theorem with no assumptions other than the axioms of mathematics. Along the way we see autoformalization of algebra, harmonic analysis, geometry and number theory, and we learn that AI autoformalization artefacts are now robust enough to be built upon; the proof is multi-layered. Automatically formalizing a proof as complex as FLT is a significant step towards a future in which all of mathematics can be re…

AutoGraphForge: Towards Automated Graph Theory Discovery

Fri, 04 Sep 2026 04:00:00 +0000

We report on our ongoing project to develop a computational pipeline, AutoGraphForge, for an automated graph-theoretic conjecturing-refuting-formalizing-proving system. Conjecture generation is counterexample-guided and runs in rounds: a Graffiti3 generator proposes conjectures over a small, evolving snapshot table $T$ (initially a few hundred graphs with their computed invariants) that grows only by counterexamples to its own conjectures. A novelty filter of $559$ classical and folklore relations, closed under transitive composition and linear identity substitution, decides via a linear program whether a candidate is already implied by known results. Surviving candidates are tested against a dataset of about $348,000$ graphs, unioning the complete House of Graphs invariant export, the exhaustive census of all connected graphs on at most nine vertices, several extremal families (strongly regular, minimal Ramsey, Cayley, cages, barbells, lollipops, spiders), and random models. Counterexample-search algorithms then attack the remainder. Run for several rounds on an HPC cluster, the loop yields $6,522$ conjectures that survived the refutation dataset, the novelty filter and every active-search run -- among them nontrivial relations between the annihilation number and the edge-cover number for bipartite and regular graphs, which we prove by hand. A subsequent formalization and proving stage deterministically translates each surviving conjecture into a Lean 4 statement skeleton; every candidate proof is kernel-verified against a pinned mathlib4 and our custom invariant preamble. This stage integrates two neural provers -- DeepSeek-Prover-V2-671B (served with vLLM) and the Lean-specialised OProver-32B -- behind the independent kernel check. It is implemented end-to-end and passes initial sanity checks, with the full pipeline currently running on the cluster.

Introducing SINFONIA: Symplectic, slimplectic and Magnusian (Neural) Flows for Orbital Numerical Integration and Acceleration

Fri, 04 Sep 2026 04:00:00 +0000

Long-duration gravitational-wave modelling must resolve fast orbital motion together with slow dissipative evolution while preventing small numerical errors from accumulating into secular phase drift. Here we ask whether the finite-time evolution map itself can be learned as an explicit, differentiable, structure-preserving object and then repeatedly composed through a complete inspiral. We construct three neural-flow architectures: a symplectic and slimplectic flow on Galley's doubled phase space, [SINFONIA-J0]; a Taylor-anchored flow, [SINFONIA-J1]; and a Magnusian flow that learns the finite-time dissipative correction in the interaction picture, [SINFONIA-J2]. Applied to a 2.5PN neutron-star inspiral, all three expose the same controlling mechanism: long-time accuracy is governed not by pointwise map error alone, but by its signed projection onto a single secular channel fixed by energy--angular-momentum balance. Encoding this structure allows the learned maps to remain accurate through $10^{2}$--$10^{5}$ window compositions to coalescence at timesteps of a full orbital period and beyond, reaching chained phase errors orders of magnitude below a benchmark slimplectic integrator at lower cost. The same secular structure can also be exploited for physics inference: when the channel is left unconstrained, the accumulated phase retains enough information to recover an un-modelled dynamical-friction-like force, both parametrically and as a learned function of separation. Network-off controls isolate the contribution of learning from the analytic structure already built into each map. These results establish a proof of concept for structure-preserving learned evolution maps as tools for fast long-duration integration and physics inference in gravitational-wave source modelling.

Influence of Extruded Filament Shape on Buildability in 3D Concrete Printing: A Geometry-Informed Deep Learning-FEM Approach

Fri, 04 Sep 2026 04:00:00 +0000

The geometric morphology of deposited filaments can significantly influence the structural performance and stability of 3D concrete-printed (3DCP) structures. However, most finite element (FEM)-based approaches for buildability assessment represent printed layers as simplified rectangles, potentially limiting predictive accuracy. This study proposes a geometry-informed modelling framework that integrates the deep-learning-based filament shape prediction tool ShapeGen3DCP with a layer-activation FEM approach to investigate the effect of realistic filament geometries on buildability. The framework generates geometry-aware numerical models directly from material and process parameters, eliminating the need for experimental filament characterization or computationally intensive fluid-flow simulations. Validation against experimental data and a parametric study of rectilinear walls demonstrate that extrusion parameters and the resulting filament geometry can significantly influence buildability predictions. Realistic filament representations are particularly important for free-flow deposition, whereas layer-pressing strategies are less sensitive to geometric simplifications. Among the investigated representations, an elliptical approximation provides an effective balance between geometric fidelity and modelling simplicity. When rectangular representations are preferred to enable regular computational meshes for faster simulations, defining their dimensions based on volume conservation improves prediction reliability compared with calibrating them using either the maximum filament width or the interlayer contact width. Overall, the proposed methodology demonstrates the importance of incorporating filament geometry into 3DCP simulations and provides practical guidance for selecting efficient and accurate geometric representations for buildability assessment.

Calibration of neural viscoelastic models via full-field data

Fri, 04 Sep 2026 04:00:00 +0000

We propose an unsupervised learning framework for calibrating a physics-augmented neural network (PANN) for small-strain viscoelasticity via full-field data. It only requires quantities that are directly accessible in real experiments for training, namely global reaction forces and surface displacements. The underlying PANN is embedded in the generalized standard materials theory, in which two scalar-valued potentials render the constitutive model thermodynamically consistent by construction, while invariant-based representations of the free energy and the dual dissipation potential additionally ensure material symmetry. Considering a thin specimen under the plane stress assumption, we formulate a constrained optimization problem based on the equilibrium gap method in combination with quasi-Newton optimizers and automatic differentiation. Thereby, the unknown out-of-plane strain follows from the plane stress condition and the evolution of the internal variables is captured by an implicit time integration scheme. The resulting system of nonlinear equations is solved via a local Newton iteration at quadrature point and time step. To drastically reduce the computational cost of training, the backward adjoint method is employed to compute the gradient of the target loss, instead of backpropagating through all Newton iteration steps. The proposed framework is demonstrated for synthetic data, including noisy displacements and forces, showing excellent agreement across a wide range of deformation rates and load paths.

Mol-JEPA - Multimodal molecular foundation model [R]

Thu, 03 Sep 2026 19:56:27 +0000

Hi everyone, I just quickly wanted to share a paper I was working on for around a year now. I created this summary website with key results: https://flogrammer.github.io/moljepa/ TL;DR: its a multimodal JEPA model for molecules. There will be more work to do to improve performance and I would be happy about feedback and ideas :) submitted by /u/TerribleAntelope9348 [link] [comments]

Introducing WeatherNext 3️⃣ — Google DeepMind and Google Research’s Most Advanced Global Weather AI Model Yet

Thu, 03 Sep 2026 16:10:22 +0000

Introducing WeatherNext 3️⃣— our most advanced global weather AI model yet from @GoogleDeepmind and @GoogleResearch With prediction capabilities that are up to 5x sharper than WeatherNext 2, the model generates a forecast with high spatial resolution in order to catch… pic.twitter.com/XNDjd7eoHw

Introducing WeatherNext 3, our most advanced and accurate global weather AI model

Thu, 03 Sep 2026 15:02:08 +0000

Introducing WeatherNext 3, our most advanced and accurate global weather AI model Every day, the weather influences billions of decisions. Some are as simple as grabbing an umbrella before heading out the door, but others are far more consequential. Wind, rain, and extreme weather events, like heatwaves and droughts, have cascading impacts across agriculture, global supply chains, clean energy production, and national economies. In recent years, AI has revolutionized weather forecasting, using historical records to make faster and more accurate predictions than traditional methods. Yet predicting highly local and rapidly changing weather has remained a challenge. Previous models often lacked sufficient spatial resolution, and struggled to incorporate real-time weather data from sources like satellites. Today, Google DeepMind and Google Research are introducing WeatherNext 3, the most advanced and accurate global weather model to date, according to independent live evaluations by Brightband. Our model learns directly from real-time observations, enabling it to provide timely and more localized predictions for the weather events that impact people the most. By using raw satellite data to produce a forecast every hour in high resolution, our model makes reliable forecasts accessible across Google products worldwide. Rapid weather prediction at unprecedented resolution A forecast's utility often comes down to detail and how finely it resolves both time and space. WeatherNext 3 generates hourly forecasts at multiple spatial resolutions, maintaining physical consistency from broad global wind patterns all the way down to local topography. With WeatherNext 3, we can visualize key surface variables — like temperature and moisture — at a 5-kilometer resolution, other surface variables at 10 kilometers, and atmospheric variables, like wind speed, at 25 kilometers. Overall, this provides a global weather picture roughly five times sharper than our previous model, WeatherNext 2, which produced forecasts on a 25-kilometer grid in 6-hour increments. Figure 1: The end-to-end WeatherNext 3 system architecture. The model ingests live 1-hour geostationary satellite mosaics alongside traditional historical analysis to feed a single, flexible Functional Generative Network (FGN) mesh transformer. It outputs dense gridded fields, discrete cyclone tracks, and predicts station-lev…

When Literature Data Mislead Artificial Intelligence in Materials Discovery

Thu, 03 Sep 2026 04:00:00 +0000

Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-derived datasets often assume that reported experimental values are internally consistent and directly reusable. Here, we analyze this assumption using solid electrolyte (SE) conductivity data as a representative materials-science case. By tracing values from source articles to curated datasets, we identify recurrent text-figure mismatches, ambiguous axis annotations, unit inconsistencies, and missing measurement context. These discrepancies are often numerically plausible and therefore difficult to detect through routine preprocessing, but they can propagate as structured label noise during database construction and machine-learning reuse. A cross-database example shows how ambiguous reporting can create a 100-fold conductivity error. Our analysis reframes data accuracy as an infrastructure requirement for artificial-intelligence-driven discovery and motivates traceable reporting, curation, and validation practices for reusable scientific data. Keywords: AI for science; Data reliability; Scientific databases; Structured label noise; Literature-derived data; Materials informatics; Solid electrolytes

Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage

Thu, 03 Sep 2026 04:00:00 +0000

Classical constitutive modeling of path-dependent inelastic materials relies on internal state variables whose evolution equations must be postulated based on domain knowledge and calibrated against experimental data. However, in many practical settings, the relevant internal variables are typically not measurable in experiments, and the constitutive response must be inferred entirely from measured strain-stress data without any prior knowledge of the material's internal state. We propose a data-driven constitutive modeling framework based on the concept of a material operator, which treats a deforming material as a functional mapping from its entire strain history to the corresponding stress response. In contrast to traditional autoregressive or recurrent formulations, the model is trained directly on full loading paths as function-to-function mappings, predicting complete stress trajectories in a single parallel forward pass. Temporal path dependence is enforced through a causally masked attention mechanism embedded within the operator, which restricts the model's attention to past material states while preserving computational parallelizability. Spectral convolutions provide discretization-invariant representations in the frequency domain, while causal attention captures highly adaptive, non-local history dependence. Furthermore, sinusoidal activation functions are used to resolve the strong nonlinear transitions inherent in inelastic regimes. The framework is evaluated across multidimensional, rate-independent material models exhibiting complex phenomena, with an emphasis on nonlinear plasticity and ductile damage accumulation. The results demonstrate accurate and robust predictions of irreversible deformation mechanisms while simultaneously achieving resolution invariance and excellent parallel efficiency.

Mus siliconus: A Neuro-Musculoskeletal Digital Twin of the Mouse Integrating Neural Dynamics, Biomechanics, and Tactile Sensing

Thu, 03 Sep 2026 04:00:00 +0000

Digital twin technologies could transform neuroscience and biomedicine by creating predictive computational representations of living organisms. However, most animal digital twins model neural circuits, anatomy, or biomechanics separately rather than integrating the processes that generate behavior. We argue that animal digital twins should instead be conceived as embodied dynamical systems that unify neural activity, body mechanics, sensory feedback, and environmental interactions. We propose a neuro-musculoskeletal digital twin of the mouse that combines multimodal anatomical reconstruction from X-ray CT, high-resolution white-light sections, and Scx-GFP imaging with biomechanical simulation, Bonhoeffer--van der Pol neural dynamics, and tactile feedback. This framework forms a closed sensorimotor loop in which behavior emerges through continuous interactions among the nervous system, musculoskeletal system, and environment. The Bonhoeffer--van der Pol model provides a computationally tractable dynamical foundation for large-scale simulation of these interactions. Neuro-musculoskeletal digital twins could provide a convergence point for computational neuroscience, biomechanics, artificial intelligence, and robotics. When coupled with adaptive learning and autonomous experimentation, they may develop from passive simulations into active scientific instruments that generate hypotheses, predict interventions, and guide experiments. Such embodied digital twins could advance the study of biological intelligence and support new adaptive biomedical and robotic systems.

HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design

Thu, 03 Sep 2026 04:00:00 +0000

Polymeric materials are central to modern technologies, with applications ranging from energy to health and transportation. Although AI has made significant advances in materials discovery, the hierarchical structure of polymers across multiple length scales makes them inherently difficult to represent in a unified and physically meaningful way. Here we introduce HiPoly, a polymer-native AI framework that processes complete polymer descriptions through a three-level hierarchical graph architecture built on the G2RINS representation. HiPoly encodes stochastic inter-monomer connectivity, composition, and molecular weight directly within its architecture, using physically motivated design principles that mirror the multi-scale nature of polymeric systems. The framework establishes an end-to-end AI-driven workflow from experimental formulation data to property prediction, generative molecular design, and physics-based validation through molecular simulations, all unified by a single polymer representation. We demonstrate state-of-the-art prediction accuracy for thermophysical properties of multi-component polymer systems, with ablation studies confirming that each hierarchical design choice contributes independently to model performance. As an example, the generative design pathway is applied here to the discovery of sustainable alternatives to persistent fluorinated polymers, where it is possible to identify and independently validate PFAS-free candidates with target surface-energy properties. This work demonstrates how polymer-native AI can accelerate discovery by linking representation, prediction, and design across complex polymer chemistries.

A Survey of Feedback-Driven Test-Time Intelligence for Self-Improving AI

Thu, 03 Sep 2026 04:00:00 +0000

The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a growing body of work studies how models can refine their behavior on the fly by exploiting test-time information and additional computation. These developments have largely evolved along two directions: methods that modify the model's state using test-time signals, and methods that improve predictions through extra inference-time resources such as more sampling and tool use. However, these directions are often studied in separate communities with different terminology, making their connections harder to see. In this survey, we present feedback-driven Test-Time Intelligence (TTI) as a unified perspective for understanding such deployment-time improvement. We use this view to relate test-time adaptation, test-time learning, and test-time scaling, highlighting both their distinctions and their growing overlap in hybrid systems. This unified framework helps connect previously fragmented ideas and provides a clearer conceptual foundation for studying inference-time self-improvement. We review major methodological paradigms, representative applications, and open challenges across vision, language, multimodal learning, generative models, robotics, and healthcare. Our goal is to provide a coherent foundation and research roadmap for the study of self-improving AI systems at test time.

Induction and Inquiry via Probabilistic Reasoning over Language and Code

Thu, 03 Sep 2026 04:00:00 +0000

How humans grow and maintain abstract knowledge from the sparse, streaming noisy data of experience is a longstanding challenge in cognitive science. Any computational account must satisfy at least three desiderata: It must be (1) data-efficient and compute-efficient, (2) capture gradations of uncertainty to support intelligent inquiry and information gathering, and (3) be flexible enough to mentally represent the endless range of concepts people can learn and think about. Here we introduce a computational model that captures these three properties, by encoding symbolic knowledge as mental programs that combine natural language with source code, and sequentially inferring mental programs using LLM-guided Bayesian learning algorithms. Across a range of behavioral studies this model successfully reproduces quantitative signatures of human inductive learning and active inquiry, such as anchoring, garden-pathing, and other effects. In contrast, pure LLMs and classic Bayesian models either fail at the underlying task, or do not reproduce human behavior, or succeed only at exorbitant computational cost. These results suggest that one way humans continually grow their knowledge is by mentally representing many hypotheses spanning language-like and program-like representations, then revising those hypotheses to approximate Bayesian updates, while a bottom-up neural mechanism (an LLM) makes inference both tractable and learnable.

WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling

Thu, 03 Sep 2026 04:00:00 +0000

Black-box optimization problems remain challenging because of large, weakly structured, and high-dimensional search spaces. Existing methods often suffer from poor sample efficiency because they rely on direct candidate generation or trial-and-error refinement. A natural way to improve search efficiency is to use world modeling, which can help identify promising optimization directions before costly evaluation. Large language models can predict the outcomes of these candidates with nontrivial accuracy because of their implicit knowledge. Motivated by this observation, we propose WMLLM, a self-evolving optimization-agent framework based on predict-then-act world modeling. The agent first predicts promising directions and then acts to generate candidates. Combined with agentic multi-turn refinement, population-based search, and reinforcement learning, WMLLM refines both its implicit world model and its optimization strategy during search. Experiments on black-box optimization tasks, especially multi-objective molecular optimization, show that WMLLM improves sample efficiency and final optimization performance. On the multi-objective molecular optimization benchmark, WMLLM achieves state-of-the-art results under a limited evaluation budget.

Machine-learning screening and first-principles lattice dynamics resolve the most stable structure of NaCa(BH$_4$)$_3$ from a data-driven candidate search

Thu, 03 Sep 2026 04:00:00 +0000

Equimolar NaCa(BH$_4$)$_3$ offers a theoretical hydrogen capacity of 11.24 wt.% and a decomposition enthalpy expected to fall between those of NaBH$_4$ and Ca(BH$_4$)$_2$, but it has never been prepared and no crystal structure has been reported. Predictions for it have so far been built within the perovskite family that its heavier homologues adopt. Here that assumption is removed. Six candidate frameworks were assembled from three independent sources --- experimentally determined ABX$_3$ borohydrides, a distorted perovskite from data-driven structure prediction, and an unbiased search over a structural database in which all 2238 generated candidates were relaxed with none excluded --- and at fixed charge-neutral composition the cation arrangement of every framework was enumerated exhaustively, 420 decorations reducing to 118 symmetry-inequivalent configurations, screened with a machine-learning potential and settled from first principles. The most stable structure is not a perovskite. It is a monoclinic framework of space group Cm, reached only by the unrestricted search, at $-$4.185640 eV/atom, 7.67 meV/atom below the best framework available beforehand; two chemically unrelated donor prototypes converge on it to 0.076 meV/atom, and its ordered cation arrangement is the ground state of its own series. Its phonon spectrum carries no imaginary mode at any wavevector the supercell resolves exactly and its relaxed-ion elastic tensor is positive definite, whereas the orthorhombic perovskite candidate is unstable to $-$2.401 THz. That contrast supplies a physical reason for the reported failure to obtain this composition in perovskite form, and the simulated diffraction pattern reported here identifies the predicted framework by seven reflections that neither parent phase nor the competing framework produces.

Learning and Predicting Nonlinear Variability in X-ray Binaries with the Koopman Operator

Thu, 03 Sep 2026 04:00:00 +0000

X-ray variability in compact-object binaries encodes the nonlinear dynamics of corona-jet interactions and accretion disk instabilities. Standard timing techniques characterize periodic and quasi-periodic variability well, but do not model underlying nonlinear dynamics or forecast their evolution. We apply Koopman operator theory and a data-driven approximation, extended dynamic mode decomposition (EDMD), to X-ray light curves for the first time. Koopman theory represents nonlinear evolution as an infinite-dimensional linear operator $\mathcal{K}$, whose eigendecomposition separates a complex system into independently evolving linear modes. We derive that each Koopman eigenfunction contributes a Lorentzian peak to the power spectrum, giving quasi-periodic oscillations a dynamical interpretation in which process noise damps modes and broadens their peaks. In both chaotic Duffing oscillator simulations and $\sim$30 yr of RXTE ASM and MAXI monitoring of the X-ray binary 4U 1705-44, the slowest-varying eigenfunction partitions state space into low- and high-flux regimes, changing sign days to weeks before transitions become visible in the light curve. Iterating $\mathcal{K}$ additionally yields flux forecasts on days-to-weeks horizons. The strengths of this framework are its generality across linear and nonlinear systems, its intrinsic interpretability through decomposed modes carrying explicit dynamical meaning, and its predictive power from propagating learned dynamics forward. These results establish Koopman operator theory as a new frontier of astrophysical timing and help advance interpretable machine learning for scientific discovery and understanding.

Machine Learning Local Potentials for Accelerated Electron-Phonon Interactions Calculations within the Projector Augmented-Wave Framework

Thu, 03 Sep 2026 04:00:00 +0000

Electron-phonon interactions govern carrier dynamics, transport, and many optical and quantum phenomena in solids, but finite-displacement calculations within the projector augmented-wave (PAW) framework require up to 6N self-consistent supercell calculations for an N-atom system, making them costly for large, low-symmetry, and disordered materials. We introduce MLLocP, a machine learning strategy that learns the self-consistent local potential on real-space grids and supplies its displacement derivatives to the evaluation of all-electron PAW electron-phonon matrix elements. Analysis of the PAW decomposition identifies the local-potential contribution as a natural target for machine learning, while the remaining PAW quantities are retained within the established VASP/PHELEL framework. Using targeted grid-point sampling and feature-diversity selection for efficient model training, we validate MLLocP for elemental Cu, polar BAs, and chemically disordered Cu3Au. The learned potentials and their resulting displacement derivatives closely reproduce direct density functional theory calculations, yielding transport coefficients within approximately 3.5% for BAs mobility, 8% for Cu conductivity, and 11% for Cu3Au conductivity. For the 32-atom Cu3Au special quasirandom structure, using 40 sampled configurations reduces the number of self-consistent calculations by about fivefold relative to the 192 displaced structures required directly; using 20 or 10 configurations increases the acceleration to approximately 10- and 20-fold, with conductivity errors of 14.3% and 18.6%, respectively. MLLocP thus provides a scalable route to PAW electron-phonon calculations in complex materials while preserving the underlying all-electron formalism.

Retrieval of binding sites across the AlphaFold human proteome using protein language model representations

Thu, 03 Sep 2026 00:00:00 +0000

Protein language models (PLMs) provide powerful representations of protein sequence, but their utility for proteome-scale binding-site retrieval remains unclear. Here, we present PocketScope, a training-free framework that represents cavity-lining residues using frozen ESM-C 600M embeddings and retrieves related binding sites through exhaustive late-interaction MaxSim, without pooling or approximate nearest-neighbor search. PocketScope identified 153,805 cavities across 37,682 proteins in the AlphaFold human proteome and recovered documented drug off-targets across a curated set of pharmacological pairs. On the ProSPECCTs benchmark, PocketScope ranks 1st of 23 methods by mean rank across the ten collections. PocketScope provides a practical framework for proteome-scale off-target prediction. PocketScope is open source and also freely available as a web server at https://www.bhargavaresearch.org/pocketscope.

Evaluating GPT-4o Model Proficiency and Clinical Reasoning for Antimicrobial Stewardship in Dentistry

Thu, 03 Sep 2026 00:00:00 +0000

Objectives: Dentists prescribe approximately one in ten antibiotics worldwide, yet antimicrobial stewardship (AMS) remains underemphasized in dental education. Large language models (LLMs) may support AMS training, but their proficiency and clinical reasoning in this context remain unclear. We evaluated GPT-4o's accuracy and clinical reasoning on dental antibiotic prescribing questions, stratified by question difficulty. Methods: We assembled 125 multiple-choice questions on dental antibiotic prescribing from eight peer-reviewed studies (2017-2023). GPT-4o answered each question and generated a clinical justification. Accuracy was assessed against source-study answer keys and examined across difficulty quartiles. Justifications were evaluated using an adapted 12-axis human-evaluation framework assessing scientific consensus, extent and likelihood of harm, inappropriate and missing content, bias, and both correct and incorrect comprehension, retrieval, and reasoning. Prophylaxis-specific questions were analysed separately. Results: GPT-4o correctly answered 72% of questions. Accuracy remained relatively stable across difficulty quartiles (78%, 78%, 65%, 70%). Experts rated 95.4% of justifications positively across the 12 axes. Comprehension, retrieval, and reasoning each exceeded 96.2% positive ratings. Missing content was the main weakness (7.8%), and 7.1% of justifications showed a moderate-to-severe potential for harm. Performance on prophylaxis-specific questions (98.1%) exceeded non-prophylaxis questions (93.0%). Conclusions: GPT-4o demonstrated moderate-to-high proficiency and clinically defensible reasoning in dental antibiotic prescribing questions. However, residual risks indicate that it is not suitable for unsupervised clinical use but shows potential as a supervised AMS educational tool.

Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction

Thu, 03 Sep 2026 00:00:00 +0000

Accurate identification of drug-target interactions is consequential for novel drug discovery and development. Deep learning methods for drug-target affinity (DTA) prediction have shown great promise in accelerating drug discovery and reducing development costs. Although graph neural networks have improved drug representation learning for DTA prediction tasks, many models still struggle to effectively and efficiently capture protein information, limiting overall prediction accuracy. In this work, we systematically evaluate the impact of pre-trained protein language models (PLMs) on the downstream task of predicting binding affinity between drugs and target proteins. We design multiple experiments across four different molecular representation backbones and assess the effect of incorporating PLM embeddings, comparing their performance to classical 1D convolution methods. We evaluate four families of PLMs which we integrate into PLM-GraphDTA, each built on distinct architectures and optimized for different tasks, including structure prediction, function prediction, and sequence unmasking. Additionally, we evaluate DeepGraphDTA, an architectural modification of the baseline convolution method designed to improve protein representation learning. The models are evaluated on two benchmark datasets, Davis and KIBA, using concordance index (CI) and mean squared error (MSE) as performance metrics. We further evaluate the generalization power of each model using cold-start train and test splits, and analyze the per-protein contribution to total CI. The results indicate simple architectural modifications to traditional convolution methods may be sufficient to bridge the gap to large pre-trained PLMs.

Puffin-World: Scaling a Unified Multimodal Model with Native 3D World States

Thu, 03 Sep 2026 00:00:00 +0000

We propose Puffin-World, a unified multimodal architecture that integrates physical understanding, spatial simulation, and 3D world generation and reconstruction without relying on external offline modules. To reliably construct and interact with 3D worlds, our framework jointly models three native world states: physics (gravity field and latitude), geometry (depth), and appearance (image), together with a unified Omni-Camera representation that supports diverse tasks and flexible motions. Beyond modeling these states, we introduce a strategy for propagating physical dynamics across future frames. By grounding absolute camera properties in the real world, Puffin-World enables physically consistent and visually stable world generation. We further couple appearance and geometry within a single generative process, jointly synthesizing each future view and reconstructing its underlying geometry. This unified paradigm enables interleaved closed-loop applications requiring synergy across multiple tasks, including mimic and self-calibrated world exploration. To scale Puffin-World to complex scenarios, we construct Puffin-16M, comprising 15 million vision-language-camera triplets and 1 million trajectories featuring various and challenging motions. To foster further research in this area, we released the code, models, and datasets.

Bio-Babel: autonomous cross-language reconstruction of agent-ready computational biology ecosystems

Thu, 03 Sep 2026 00:00:00 +0000

Scientific software is often locked in its source language, callable from others but not natively extensible or built upon. We present Bio-Babel (biobabel.stanford.edu), an autonomous framework that rebuilds software natively in target ecosystems and makes it agent-callable, shipping each package with an agent-readable contract of its usage. Reconstructing a hierarchical, interdependent R-to-Python single-cell stack, Bio-Babel reproduced the originals faithfully and revealed pancreatic differentiation dynamic defects under graded SWI/SNF loss.

Principia: Relational Physics Tests for Video Models

Thu, 03 Sep 2026 00:00:00 +0000

Evaluating physical reasoning in video models is difficult because absolute motion measurements depend on frame rate, object scale, and camera calibration, all of which are often ambiguous or unavailable in generated video. We propose a different approach. When two objects in the same scene obey the same physical law, their motions must satisfy predictable relationships, and these relationships hold independent of calibration. We introduce Principia, a benchmark that evaluates Newtonian physics through relational consistency between paired objects. Principia spans eight phenomena - gravity, restitution, friction, rotational inertia, projectile motion, momentum, pendulum, and mass-spring oscillation - across translational, rotational, collisional, and oscillatory dynamics, using real-world scenes recorded under controlled protocols. We also introduce a calibration-independent consistency score that quantifies physical violation directly in image space. Across thousands of generations from six state-of-the-art video generators, no model exceeds 0.42 on Principia despite all scoring around 0.8 on VBench. Vision-language models are evaluated on their ability to detect relational physics violations, with the best model achieving only 67% accuracy and most performing near chance level.

The Next Frontier of Ocean Governance: AI, Satellites and Transparency

Wed, 02 Sep 2026 15:51:00 +0000

Jennifer Raynor Dr. Jennifer Raynor discusses the rapid evolution of AI-enabled vessel tracking technologies and how this technology can drive better decisions at sea. As chair of Global Fishing Watch’s science advisory board and member of the organization’s board of directors, Dr. Jennifer Raynor has a front-row seat to the rapidly changing picture capturing human activity at sea. Advances in artificial intelligence, satellite imagery and vessel tracking are not only increasingly revealing where fishing happens but also spotlighting once-hidden gaps left by conventional monitoring systems. Raynor has helped shape that emerging field. An assistant professor of natural resource economics at the University of Wisconsin-Madison, her work has centered on how new sources of ocean data can inform conservation efforts and fisheries management in the name of greater transparency. Her latest paper, “Satellite-Based Fishing Fleet Tracking: A New Foundation for Fisheries Science and Management,” published in July 2026, examines how a decade of technological advances has transformed fisheries science, from AI models capable of identifying apparent fishing activity to satellite imagery that can detect vessels that do not appear in public tracking systems. We spoke with Raynor about the evolution of vessel tracking, the questions these technologies can now answer and how the next generation of AI-enabled ocean monitoring can bolster global efforts for ocean transparency. How has satellite-based vessel tracking evolved? There have been two critical breakthroughs in publicly accessible, global-scale vessel tracking datasets over the past decade, both led by Global Fishing Watch scientists. The first was the release of their flagship data on apparent fishing activity in 2018, which uses AI models to infer when and where a vessel is likely fishing based on its movement patterns. For example, the model may learn that vessels move in a straight line and at a certain speed when trawling. This was the first time that scientists, managers, policymakers, journalists and the public at large could access global-scale data on industrial fishing fleets. It offered a degree of transparency in human uses of the ocean that was previously unthinkable. Unfortunately, AIS data still has major blind spots. Captains can turn off or tamper with transponders, satellite reception is poor in many…

AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials

Wed, 02 Sep 2026 04:00:00 +0000

Machine learning interatomic potentials bridge the gap between quantum chemical precision and classical computational speed, enabling molecular dynamics simulations with first-principles accuracy. Their reliability is often improved through active learning, which iteratively expands the training set by identifying uncertain, out-of-distribution configurations. Existing uncertainty-quantification methods often involve a trade-off between computational cost and reliability, and generally cannot account for redundancy as an acquisition batch is assembled. Here, we introduce AdaptNTK, a single-model framework that measures uncertainty as a regularized Mahalanobis distance in empirical neural tangent kernel (NTK) feature space. With the NTK features fixed during acquisition, the uncertainty depends on the acquired configurations but not their reference labels. This allows the uncertainty to be updated recursively after each selection without retraining, reducing redundancy within an acquisition batch. On held-out rMD17 data, AdaptNTK achieves the highest mean correlations with force errors (Spearman 0.68, Pearson 0.71) and matches a three-member ensemble in error retention. In active learning experiments, AdaptNTK achieves the lowest force errors across rMD17 and Transition-1X, with particularly strong performance on transition-state configurations in Transition-1X. AdaptNTK provides a 2.6-fold speedup per Transition-1X cycle relative to the ensemble, providing efficient single-model uncertainty estimation with sequential updates for data-efficient active learning.

AutoXRD: Autonomous LLM Agents and Comprehensive Evaluation for Powder Diffraction Analysis

Wed, 02 Sep 2026 04:00:00 +0000

Powder X-ray diffraction (XRD) is central to materials characterization, yet reliable end-to-end automation remains challenging. An XRD agent must interpret diffraction evidence, operate refinement software, manage coupled parameters in a defensible order, and distinguish numerical improvement from physical validity. In this paper, we propose AutoXRD, an autonomous large language model (LLM) agent framework that organizes powder-XRD analysis as stepwise refinement, grounds actions in observed evidence, and applies deterministic crystallographic and physical checks before accepting results. We further introduce XRDBench with two complementary tracks. XRDBench-QA contains 100 bounded diagnostic tasks that isolate scientific reasoning and decision-making, whereas XRDBench-E2E contains 34 executable workflows that test whether agents can compose these capabilities into complete analyses requiring file inspection, crystallographic-software execution, iterative refinement, evidence preservation, and reporting. We evaluate ten recent LLMs across 1,340 model--task runs. Models average only 57.8 out of 100, falling from 61.9 on XRDBench-QA to 53.7 on XRDBench-E2E. They perform best on refinement-history assessment and result acceptance, but remain substantially weaker on refinement-action selection, phase quantification, indexing, and Rietveld refinement. GPT-5.6 Sol achieves the highest overall score of 81.1, GPT-5.6 Terra the highest XRDBench-E2E point estimate of 81.0, and GPT-5.6 Luna the best score--cost trade-off. Ablations show that all six AutoXRD components consistently improve performance, supporting the framework design. Finally, execution-trace analysis reveals recurring failures in coupled-parameter control, quantitative reasoning, evidence preservation, and workflow termination, motivating stronger scientific constraints, uncertainty-aware decisions, and more efficient planning.

EasyBCI Agent: Towards Universal Neural Data Preprocessing for Brain-Computer Interfaces

Wed, 02 Sep 2026 04:00:00 +0000

Brain-computer interfaces translate neural activity into device commands, yet their performance hinges on preprocessing that remains manual, expert-dependent and poorly reproducible. Large language model agents can automate scientific coding, but existing systems lack the modality coverage, raw-data isolation, experience accumulation and domain oversight that neural preprocessing requires. We introduce EasyBCI, a two-phase LLM agent that plans and executes preprocessing pipelines for six signal types. A Plan Agent profiles each recording into a text-only Data Fingerprint that never exposes raw data to the model and selects a literature-grounded operator sequence. An Execution Agent generates, runs and self-corrects code until quality criteria are met, while a quality-gated experience system retains validated strategies as reusable skills. A domain expert intervenes at two decision gates, retaining human judgement where undetected error can invalidate downstream analyses. Evaluation on EEG with a fixed linear classifier shows that all five EasyBCI backbones preserve more task-relevant separability than the manual pipeline. Under same-backbone comparison, EasyBCI outperforms general-purpose coding agents on both label schemes for four of five configurations. EasyBCI extends to five additional modalities spanning nearly three orders of magnitude in sampling rate, producing complete reproducible pipelines with recorded decision provenance. These results indicate that domain-specific orchestration can bring auditable preprocessing within reach of laboratories lacking dedicated expertise, illustrating design principles applicable to AI agents in other scientific domains.

Fourier Neural Operators for Composition-Driven Crystal Structure Discovery

Wed, 02 Sep 2026 04:00:00 +0000

Crystalline materials discovery is essential for energy, electronics, and catalysis, but the vast chemical and structural space makes exhaustive screening infeasible. Existing voxel-based methods are limited by the local receptive fields of three-dimensional convolutional neural networks and the posterior collapse of high-dimensional variational autoencoders. Here, we develop a Fourier Neural Operator (FNO)-based crystal-field solver that maps a prescribed chemical formula and lattice parameters to periodic number-density and electron-density fields. By operating on global Fourier modes, the solver captures long-range correlations in periodic crystal fields beyond conventional local convolutions. Building on this solver, we construct a coupled generation-solving framework in which a conditional variational autoencoder generates diverse candidate lattice parameters in a low-dimensional basis-coefficient space, followed by density-field prediction and atomic reconstruction through peak detection, position optimization, and weight optimization. The reconstructed structures are further screened using voxel-level filtering, machine-learning interatomic-potential relaxation, and first-principle calculations. The framework generates novel structures across 104 chemical formulas with competitive reconstruction accuracy, demonstrating high generative diversity and structural validity. By extending Fourier neural operators to periodic crystal fields and coupling them with composition-conditioned lattice generation, our approach provides a scalable route to crystal structure discovery from prescribed chemical compositions.

MiNER: Fine-Tuned Biomedical Natural Language Processing for Malaria Disease Entity Recognition in Clinical Texts

Wed, 02 Sep 2026 04:00:00 +0000

Malaria remains a significant global health burden, necessitating continuous research efforts to understand its complex molecular mechanisms, epidemiology, and potential therapeutic interventions. Extracting essential biomedical information from the vast and constantly growing malaria literature is a challenging task that demands innovative approaches. Recently, pre-trained language models have revolutionized natural language processing tasks, demonstrating remarkable capabilities in various domains. This paper proposes a fine-tuned pre-trained biomedical language model for biomedical information extraction from scientific literature on malaria disease. The proposed methodology selects and preprocesses a large corpus of scientific articles on malaria, and then annotates them with entities of clinical significance. It then leverages BioBERT, a state-of-the-art pre-trained language model, to encode the textual data into context-aware representations. We fine-tune the model using domain-specific annotations and supervised learning to enhance its ability to extract relevant biomedical named entities. Extensive experiments and comparisons with different encoding and machine learning algorithms show that the proposed approach significantly outperforms them in precision, recall, and accuracy. We also publish our human-labeled dataset for entity and relation extraction to enable other health informatics researchers to train advanced models for malaria information extraction.

Separating perception from reasoning in vision-language models: a model-free render ceiling for crystal structures

Wed, 02 Sep 2026 04:00:00 +0000

Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separating the two places a second model in the loop. We introduce the render ceiling, a model-free reference for benchmarks built by rendering known objects: inverting the frozen cameras and re-solving cross-view correspondence recovers exactly the answer the images support. We prove the ceiling fails only through an enumerable set of projection coincidences and certify that set empty on 2,160 rendered crystal structures, so every point of a model's deficit belongs to the model. Across fourteen vision-language models, supplying exact geometry as text lifts every model yet closes under half the gap for thirteen, while a supervised vision model with no language component reads the same images at 0.8952, above every vision-language model. The instrument exposes extraction-stage fabrication that downstream accuracy would misattribute to reasoning, yields camera-placement rules for benchmark builders, and transfers to any benchmark with an invertible forward rendering.

SCAFFOLD: A Large-Scale Structured Dataset of Computer Science Research Figures with Diagram QA and Chain-of-Thought Reasoning Traces

Wed, 02 Sep 2026 04:00:00 +0000

Computer science papers rely heavily on diagrams: architecture drawings, system flowcharts, and pipeline schematics that often carry more information than the text around them. There is currently no public dataset that pairs this specific kind of figure with captions, context, questions, answers, and step-by-step reasoning, which is exactly what is needed to train a vision-language model to understand them. We present \textbf{SCAFFOLD}\footnote{https://github.com/theranjitraut/scaffold}, a large-scale structured dataset of computer science research figures with diagram QA and Chain-of-Thought reasoning traces. This dataset consists of (image, caption, context, question-answer, chain-of-thought) tuples from arXiv computer science papers prepared using layout detection and PDF parsing, with an AI-assisted question-generation step. The resulting large-sized SCAFFOLD-157K dataset spans 3,058 papers with 29,887 figures (157,387 pairs), a medium-sized SCAFFOLD-37K dataset (36,797 pairs), and a small-sized SCAFFOLD-12K dataset (12,000 pairs). We used SCAFFOLD-12K for baseline experiments on Qwen2.5-VL-3B-Instruct.

Machine learning interprets genome sequences to map possible microbial symbionts

Wed, 02 Sep 2026 00:00:00 +0000

Nature Biotechnology, Published online: 02 September 2026; doi:10.1038/s41587-026-03212-2 A machine-learning framework called symclatron uses genome sequencing content to predict whether uncultivated bacteria and archaea live independently or in close association with hosts. Applied to a global genome collection, it suggests that host-dependent microbes are widespread across Earth’s biomes and microbial phyla.

Creating bottom-up RNA transfer vehicles from synthetic protein assemblies

Wed, 02 Sep 2026 00:00:00 +0000

Nature, Published online: 02 September 2026; doi:10.1038/s41586-026-10952-3 Synthetic virus-like protein architectures designed by artificial intelligence show superior ability to deliver RNA into cells, compared with their naturally occurring counterparts, by overcoming the evolutionary constraints limiting viral evolution.

Designing Physics Experiments with Artificial Intelligence

Wed, 02 Sep 2026 00:00:00 +0000

Progress in physics has long been driven by ingenious experiments conceived by human experts. Recently, design methods driven by artificial intelligence (AI) have begun to move beyond tuning a handful of parameters to proposing entirely new experimental layouts. The discovered configurations often challenge established design conventions while matching or even exceeding the performance of human-designed set-ups. We frame experimental design as a search for optima over a vast space of hardware configurations subject to practical constraints and organize this Review around four guiding questions: how can we (1) engineer expressive search spaces; (2) build fast and reliable simulators; (3) translate scientific goals into computable objective functions; and (4) develop AI-based exploration methods that can navigate both discrete and continuous design choices. These questions place AI-driven design on a scale from parameter tuning to de novo discovery, and highlight the trade-offs between computational tractability, experimental feasibility, interpretability and solution reliability. Looking ahead, simulators spanning several physics domains, combined with large suites of experimental objectives, could discover unorthodox experimental concepts that are difficult to arrive at with human intuition alone. Ultimately, AI-designed experiments might thereby open new ways to explore the Universe.

BanffNET, a Deep Learning System for Comprehensive Histological Lesion Quantification in Kidney Transplant Biopsies

Wed, 02 Sep 2026 00:00:00 +0000

Accurate, reproducible interpretation of kidney allograft biopsies is critical for diagnosis of graft injury to guide prognosis and management. The international Banff classification is a consensus diagnostic system based on semiquantitative histological lesion scoring on either extent or severity of kidney transplant biopsies. However, pathologist scoring is limited by substantial interobserver variability, constrained scalability, and the inherent nature of the scoring system itself. Here we present BanffNET, a weakly supervised, probabilistic deep learning framework that combines self-supervised feature extraction with a novel Bayesian multiple-instance learning framework to predict (continuously) the full spectrum of Banff lesion scores directly from whole-slide images (WSIs). Using lesion-specific aggregation functions tailored to localized (modeling lesion severity) and diffuse pathologies (modeling lesion extent), BanffNET generates interpretable, patch-level probability maps and calibrated slide-level scores. BanffNET's performance was assessed relative to consensus, biological correlates of rejection and clinical outcome, demonstrating superior consistency, transportability and generalization. Trained on 7,249 WSIs from three cohorts, BanffNET demonstrates consistent performance on 11,028 WSIs across five external test sets, performing on par or exceeding expert consensus across lesions. BanffNET scores align more closely than pathologist Banff scores with molecular profiles of rejection, offering a transparent, biologically grounded framework for computational pathology with relevance beyond transplantation.

High-Resolution Subtyping of Pediatric Low-Grade Glioma Using an Integrated Meta-Clustering Framework

Wed, 02 Sep 2026 00:00:00 +0000

Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, accounting for approximately 30% of all central nervous system tumors in children. pLGG has multiple molecular subtypes that differ in disease progression, recurrence patterns, and treatment responses. Conventional wet lab approaches including molecular profiling and histopathological studies for pLGG characterization are time consuming, costly, and laborious. Recently, methods based on artificial intelligence (AI) or machine learning (ML) have been widely used for pLGG molecular categorization, but most of them can only identify two or three pLGG subtypes. To more comprehensively characterize the molecular subtypes of pLGG and their potential biological and therapeutic significance, we develop an integrated meta-clustering approach, namely Meta-pLGG, that can explore high resolution molecular subtypes and their transcriptional heterogeneity for pLGG. Specifically, we first performed multiple rounds of random projection (RP) to generate dimension-reduced feature vectors from pLGG transcriptomics data, each of which was subsequently clustered by different clustering algorithms including hierarchical clustering, K-means, Self-Organizing Maps (SOM), Non-negative Matrix Factorization (NMF), Gaussian Mixture Model (GMM), and Spectral Clustering, as base clustering methods. Then, to yield robust clustering performance, we integrated the clustering results of these RP based individual clustering algorithms by adopting a weighted meta-clustering (wMetaC) approach. Results based on 532 pLGG patients suggested that our proposed approach demonstrated superior stability and discriminative powers for higher resolution pLGG subtyping compared to conventional approaches. Based on consensus matrix analysis, we identified two major pLGG mega-subtypes, with one further subdivided into three subgroups and the other into two. Then, we performed cluster specific differential gene expression analysis, molecular pathway analysis, and gene-drug-disease association analysis. The results showed that the identified five subgroups exhibited significant subtype-specific transcriptomic heterogeneity. In summary, our meta-clustering approach demonstrated much higher performance and robustness in identifying higher resolution molecular subtypes of pLGG, revealing the molecular heterogeneity within pLGG a…

The hard parts of AI-assisted science

Tue, 01 Sep 2026 08:00:00 +0000

AI can already assist with a surprising amount of scientific work: search literature, analyze datasets, write code, and generate and test hypotheses. The harder problem is building systems scientists can steer as a research project unfolds, especially when new evidence changes which hypotheses to pursue, experiments to run, or questions to investigate next. That challenge was the focus of an August 27 event at Ai2 marking an expansion of our work with the Paul G. Allen Research Center at Providence Swedish Cancer Institute. We’ve written separately about the cancer research that grew out of that collaboration. At the event, that work served as a starting point for a broader look at what today’s scientific AI still lacks. The conversation included: Kelly Paulson, MD, PhD, Lead, Center for Immuno-Oncology, Paul G. Allen Research Center & Medical Director, Melanoma and Cutaneous Oncology, Providence Swedish Cancer Institute Bodhisattwa Prasad Majumder, Senior Research Scientist, Ai2 Sasha Stanton, MD, PhD, Medical Breast Oncologist & Lead, Cancer Immunoprevention Laboratory, Earle A. Chiles Research Institute, Providence Cancer Institute Kyle Travaglini, Assistant Investigator, Allen Institute for Brain Health Accelerator Abraham Flaxman, PhD, Professor of Health Metrics Sciences and Global Health, University of Washington & Institute for Health Metrics and Evaluation Stephen Salerno, PhD, Assistant Professor, WashU Bursky School of Public Health Hoifung Poon, PhD, Chief AI Officer, Recursion Across the presentations and panel, five challenges kept resurfacing. 1. Leaving room for the human element Majumder described scientific taste as the judgment that helps a researcher distinguish an interesting result from one that’s trivial, implausible, already understood, or unlikely to lead anywhere. Today’s AI-for-science systems don’t reliably make those distinctions on their own. The goal isn’t necessarily to transfer that judgment wholesale to an AI system. The challenge is giving researchers better ways to bring their expertise, priorities, and changing understanding of a problem into the system so AI can assist without deciding on its own which directions are worth pursuing. Ai2’s work with Providence researchers made that distinction clear—AutoDiscovery could surface hypotheses that looked surprising statistically but made little biological or clinical sense wi…

AGRICAM: A Track-Mounted Crop Pollination Monitoring Robot

Tue, 01 Sep 2026 04:00:00 +0000

Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances in computer vision and deep learning have enabled detailed analysis of pollinator behaviour, but monitoring must trade-off detail against spatial coverage and human or technological resources. This paper presents the Automated Guided Robot for Insect and Crop Activity Monitoring (AGRICAM), a purpose-built robotic system designed to meet the requirements of large-scale pollination monitoring in protected cropping systems. AGRICAM operates autonomously on low-cost, easily installed track for movement along crop rows, without disrupting farm operations or insect behaviour. The platform integrates two RGB cameras, microclimate sensors, GPS and RFID modules, motion sensors, and 4G cellular network connectivity for data transmission. A web interface enables remote device configuration and scheduling. The system autonomously captures video and image data of insects' locations and local environmental conditions. These are transferred to the cloud and analysed using computer vision models to quantify pollinator visitation and spatio-temporal activity variation. We deployed the system on a commercial blueberry farm to demonstrate and test its capability. It successfully mapped insect pollination patterns across 80 m long industrial polytunnels over 30 hours. This data enabled spatial analyses of insect activity we used to confirm a uniform pollinator distribution within polytunnels, as desired by the farm management team. The data also highlighted variation of insect activity associated with time of day and microclimate. AGRICAM therefore has been shown to be a scalable, automated crop pollination monitor that can support data-driven decisions to enhance pollination management, thereby improving crop productivity and food security.

Domain-Grounded Tool Orchestration for LLM-Guided Scientific Analysis

Tue, 01 Sep 2026 04:00:00 +0000

Scientific analysis workflows encode deep domain knowledge through sequences of tightly coupled operations where correctness depends on tool selection, execution order, and parameterization. A CFD engineer investigating flow separation must extract wall shear stress, identify zero-crossings in skin friction, and confirm with boundary-layer profiles: a chain that requires both domain expertise and proficiency with visualization tools. Current approaches to LLM-assisted scientific visualization generate scripts that encode this knowledge implicitly, and often incorrectly, producing code that executes but yields wrong results. We present an architecture that separates intent interpretation (LLM) from execution (deterministic domain tools) from explanation (LLM), connected by the Model Context Protocol (MCP) and grounded by domain ontologies that constrain planning to valid analysis chains. We instantiate the architecture in two domains on the same ParaView server infrastructure: computational fluid dynamics post-processing and topological data analysis via the Topology ToolKit (TTK). Adding the second domain required only an ontology and tool wrappers around existing filters, with no change to the architecture, protocol, or deployment. By construction the design removes whole classes of failure that affect script generation (such as API hallucination and missing pipeline stages) and narrows the strategic errors that remain. An ablation across both domains locates the ontology's empirical effect: it does not change which tools the planner selects, which is already reliable, but corrects how the model interprets results, raising interpretation accuracy from 0.41 to 0.91, and only when the relevant fact is retrieved in scoped rather than bulk form. ParaView's client-server model carries analysis to production-scale datasets through a thin browser client.

Science sandboxes measure the scientific capability of AI agents

Tue, 01 Sep 2026 04:00:00 +0000

Scientific progress depends not only on finding solutions, but on learning the rules that explain why they work and using that understanding to design better experiments. We introduce science sandboxes, a framework for studying this capability in AI agents through repeated cycles of experimentation, feedback, and hypothesis revision. Science sandboxes invite an agent to query the natural world in different ways, ranging from "wet" physical experiments, to "damp" predictive models trained on empirical data, to "dry" invented rules. By establishing a common experimental loop and a protocol for evaluating agents within it, science sandboxes allow assessment of both quantitative performance on specific metrics and qualitative scientific reasoning, across a spectrum of empirical verifiability. Here, we instantiate this framework in two biological settings, models of regulatory genomics and protein fitness prediction, and examine the capabilities of frontier agents. Across these settings, we could see when agents successfully optimized a quantitative metric without understanding the rules underlying the system. In particular, their scientific reasoning deteriorated when they encountered systems whose rules fell outside familiar biological priors. By highlighting such failure modes, science sandboxes make the frontier of scientific capability measurable and provide a controlled setting in which to study and ultimately expand it.

DS-Lighting: Making Agent Harnesses Explicit for Data-Science Automation

Tue, 01 Sep 2026 04:00:00 +0000

Large Language Model (LLM) agents have shown promise for automating data-science workflows, yet their end-to-end performance depends critically on the agent harness that represents tasks, manages execution state, constrains output artifacts, and provides evaluation feedback. Existing data-science agents often leave this harness implicit, making results difficult to reproduce, compare, and attribute across heterogeneous tasks. We introduce DS-Lighting, a unified harness toolkit that makes harness design explicit for data-science automation. DS-Lighting decomposes the harness into four reusable layers: data, workflow, execution, and evaluation, and represents diverse agents as executable operator programs that support both predefined pipelines and adaptive search. We further integrate multiple open-source data-science benchmarks into an MLE-Bench-style task format, enabling controlled comparison under a shared task interface, sandboxed runtime, and metric protocol. Experiments across agents, harnesses, models, and ablations show that explicit harness design improves reproducibility, comparability, and reliability, while reducing avoidable system-level failures in end-to-end data-science workflows. Our code is available at https://github.com/usail-hkust/dslighting

Agentic AI uncovers conserved cross-tissue protein co-abundance programs inaccessible to single-dataset analysis

Tue, 01 Sep 2026 04:00:00 +0000

Protein co-abundance clusters preserved across tissues can reveal shared disease mechanisms and candidate therapeutic targets, particularly when proteins implicated in organ-confined diseases converge in peripheral or accessible tissues. However, previous cross-tissue studies have focused on biologically pre-selected tissue pairs, leaving most possible combinations and non-obvious relationships unexplored. We present an LLM-agent framework for large-scale, evidence-grounded comparison of tissue-specific protein co-abundance networks. The framework constructs tissue networks, derives pairwise consensus clusters, and integrates evidence from expression atlases, protein interaction and complex databases, pathway annotations, disease catalogues, and literature. Applied to all 820 pairwise combinations of 41 human tissues and fluids, it identified 1,833 conserved co-abundance clusters across 406 tissue pairs. Colon, synovial fluid, blood, cerebrospinal fluid, and bone marrow were the most broadly connected tissues, while the most cluster-rich pairs were dominated by bone marrow. The analysis also highlighted non-obvious relationships: skin-bone marrow exceeded the anatomically adjacent bone-bone marrow pair, while colon-breast contained cancer-relevant clusters involving extracellular-matrix remodeling, lipid metabolism, and immune modulation. Cluster-level analyses generated further mechanistic hypotheses, including a brain-gut extracellular-vesicle/redox/serotonin-cofactor axis and a liver-bone marrow stress-response axis involving genes linked to white matter disease. These results provide a global, comparable landscape of conserved protein co-abundance and a hypothesis-generating resource for mechanistic and therapeutic exploration. Code and data are available at https://github.com/Gry1005/AgenticAI-conserved-cross-tissue-protein-co-abundance.

A Framework Integrating the Dynamic Stiffness Matrix with Physics-Informed Neural Networks for Solving Eigenvalue Problems and Analysing Dynamic Response

Tue, 01 Sep 2026 04:00:00 +0000

This paper introduces a framework that integrates the dynamic stiffness matrix (DSM) with physics-informed neural networks (PINN). The DSM-PINN embeds physical constraints within the model and demonstrates robustness, particularly when addressing limited datasets across diverse investigations. In this approach, deep neural network outputs approximate the displacement fields of element nodes. Unlike the finite element method (FEM), the element shape functions are homogeneous solutions to the governing partial differential equation, forming the basis of the exact dynamic stiffness matrix, thereby avoiding high-order derivative terms. This matrix also serves as a frequency-domain spectral element, resulting in a strong-form PINN. The loss function is produced by connecting neural networks with dynamic stiffness matrices. We focus on utilising PINNs to resolve eigenvalue problems by employing the Wittrick-Williams algorithm, which overcomes the challenge of neural networks failing to converge to higher-order eigenvalues. Additionally, the frequency domain-PINN method is used to analyse structural dynamic responses under moving and impulsive loads, addressing the limitation of neural networks in handling complex numbers. Theoretical convergence stability of the suggested approach is also analysed even DSM is an indefinite matrix after implementing the boundary condition. The numerical results validate the practicality and efficacy of the recommended approach.

From the Loss Landscape to Diverse Feature Learning in Neural Networks

Tue, 01 Sep 2026 04:00:00 +0000

Over the course of the last decade, neural networks have grown from an academic curiosity to moving the markets of nations. Despite this explosion in both research and deployment, relatively little is understood about how they achieve the solutions they do. This is both scientifically relevant, and pressing for society. When neural networks make decisions across self-driving, construction, law, hiring and health, there have been and will continue to be unintended consequences. However, attempting to generalize the failures of the largest and most important production systems makes for a very difficult task. Yet signs of these failures exist at all scales of neural networks, so we should be able to study a much more tractable setting. All neural networks must undergo an optimization process, called training, to be useful. To a great degree, understanding neural networks is understanding their optimization: through what process and exposure to which data did they arrive at their results. Yet our knowledge on this topic as a field is quite imprecise. In particular, a curious phenomenon called mode connectivity, the ability to connect neural networks in the loss surface, defies explanation entirely. This dissertation elucidates, explains and exploits this special structure in the loss landscape...

Paper Pilot: A Human-in-the-Loop Expert System for Evidence-Traceable Scientific Manuscript Generation in Applied Sciences

Tue, 01 Sep 2026 04:00:00 +0000

Large language model (LLM) agents are increasingly embedded in scientific workflows for literature analysis, drafting, and review. Existing systems advance autonomous discovery and manuscript generation, but do not resolve the governance problem that arises when ideas, methods, results, and claims propagate through AI-assisted workflows without mandatory human approval or artifact-level traceability. This paper proposes Paper Pilot, a human-in-the-loop expert system for evidence-traceable scientific manuscript generation in applied sciences. It adapts the Collaborative Agent Reasoning Engineering (CARE) methodology to manuscript development through manuscript-owner approval gates, explicit no-pass criteria, claim classification, audit logging, advisory LLM review, and evidence-locked revision control. The framework defines eight approval gates across the idea-to-claim pipeline and distinguishes literature-grounded from artifact-grounded claims, requiring reported numbers and interpretations to remain traceable to approved evidence; its system prompt is openly released for deployment in ChatGPT, Gemini, Claude, or institutional LLM environments. As a first empirical validation, we evaluate the citation-grounding layer with a controlled, mechanically scored benchmark (two commercial LLMs, real arXiv papers, no LLM judge): under coverage pressure ungated drafters fabricated up to 25% of their citations and never flagged an evidence gap, whereas the same models under Paper Pilot's evidence-locked rules produced zero fabricated citations and surfaced the planted gaps as explicit placeholders. Preliminary results for result grounding, revision, and adversarial robustness point the same way; full evaluation is left to future work. Paper Pilot positions LLM-assisted writing as a controlled human-AI decision-support process rather than a fully autonomous authorship pipeline.

OmniClimate-TC: Physics-Aware Visual Abstractions for Tropical Cyclone Reasoning

Tue, 01 Sep 2026 04:00:00 +0000

Meteorological reanalysis encodes extreme weather through continuous, physically constrained fields, posing a fundamental challenge for vision-language models (VLMs) whose perceptual assumptions are shaped by natural images. Tropical cyclones exemplify this mismatch: critical properties such as intensity extrema, asymmetry, spatial extent, and physical impacts arise from field-level organization rather than object-centric visual cues. Existing approaches address this gap through text alignment or annotation, treating the problem as multimodal supervision rather than representation design. We introduce Physics-Aware Visual Abstraction (PAVA), a plug-and-play physics-aware representation and annotation interface that maps physical reanalysis fields to visually identifiable and semantically grounded perceptual abstractions for supervision and evaluation in vision-language reasoning. Building on PAVA, we construct OmniClimate-TC, a benchmark for tropical cyclone analysis spanning five classes of reasoning and nine tasks, with 243,890 physically grounded instruction-tuning pairs. Using PAVA-aligned supervision, we adapt VLMs and provide evidence that this representation design improves reasoning over tropical cyclone hazard fields. Our results position OmniClimate-TC as a benchmark for multimedia reasoning over structured geophysical fields, and highlight representation design as a key ingredient for physically grounded reasoning in scientific media.

Evaluating LLM-based AI agents integrated with materials synthesis tools: the case of atomic layer deposition

Tue, 01 Sep 2026 04:00:00 +0000

This work provides an overview of the different strategies that can be used to evaluate the performance of AI models and agents based on large language models (LLMs) for materials synthesis. After providing a brief overview of the key technologies behind the current generation of AI agents based on LLMs, we summarize the different approaches to evaluating these models in the context of materials science and in particular on materials synthesis, with a specific emphasis on scenarios in which the models are directly integrated with experimental tools. We discuss evaluation strategies spanning knowledge and reasoning benchmarks, tool-use benchmarks, and closed loop benchmarks involving the interaction with experimental systems or realistic virtual tools. We use atomic layer deposition (ALD) as a case study, emphasizing how existing approaches in the literature both build from general approaches used beyond materials science and can be generalized to other materials synthesis techniques. Finally, we provide a practical evaluation framework to evaluate LLMs in the context of materials synthesis

SHAPE of Chain-of-Thought in Math Reasoning

Tue, 01 Sep 2026 04:00:00 +0000

Large language models (LLMs) achieve strong performance on mathematical reasoning benchmarks, yet the mathematically meaningful skills underlying their reasoning remain underexplored. We introduce \texttt{SHAPE}, a framework that analyzes Chain-of-Thought (CoT) trajectories through two lenses developed in mathematics education: (1) semantic spaces: the model's evolving mathematical interpretations of a problem (e.g., algebraic, geometric), and (2) heuristics: the specific mathematical actions taken within those spaces (e.g., simplifying the problem, working backward). We first use \texttt{SHAPE} to analyze the reasoning patterns of various models. Our findings reveal that the mathematical heuristics employed by a model better explain final answer correctness than traditional CoT features. Furthermore, models are likely to reach correct solutions by concentrating their reasoning effort within a few semantic spaces rather than exploring many disparate ones -- a pattern consistent with human behavior. Next, we utilize the \texttt{SHAPE} lens to evaluate whether post-training truly enhances mathematical proficiency. We find that reinforcement learning induces mode-seeking in heuristic usage. Lastly, we post-train LLMs by promoting diverse heuristics and demonstrate its effectiveness in improving accuracy. Overall, \texttt{SHAPE} provides a theoretically-grounded diagnostic framework for decoding LLM reasoning and offers a new path toward post-training LLMs for math reasoning. The code for our model is available at https://github.com/holi-lab/SHAPE-of-CoT

Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs

Tue, 01 Sep 2026 04:00:00 +0000

Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how vector information can be transformed as it moves across a graph. We introduce \textsc{ESNN}, an Equivariant Sheaf Neural Network that enriches this interaction by learning directed, matrix-valued transport between neighboring vector features while preserving exact Euclidean equivariance. Rather than increasing the order of the representation, ESNN keeps scalar and vector features first-order and places the additional geometric flexibility in the edge transport itself. We characterize this transport theoretically, showing that when relative displacement is the only covariant geometric input, every linear $O(n)$-equivariant map decomposes into independent radial and tangential components, while learned covariant features enable richer feature-conditioned transformations. We also introduce controlled symmetry relaxation for systems with a preferred ambient direction, which may be prescribed or inferred from data while recovering full $E(n)$-equivariance when the directional pathway is inactive. Across particle dynamics, mesh-based simulation, point-cloud classification, and molecular property prediction, ESNN improves dynamics prediction, recovers the gravity axis when symmetry is broken, yields substantial gains on selected mesh tasks and long-horizon rollouts, and remains robust to unseen rotations. These results show that learning how geometric information is transported across edges offers a complementary route to expressive equivariant message passing without requiring higher-order representations.

AI-enhanced adaptive virtual screening of large libraries for ligand discovery

Tue, 01 Sep 2026 00:00:00 +0000

Ultralarge virtual screenings (ULVSs) evaluate billions of molecules for drug discovery but face cost, flexibility and scalability limits. We introduce AdaptiveFlow, an open-source platform that makes ULVSs more accessible, scalable and efficient and supports artificial intelligence (AI) and machine learning (ML) method development. AdaptiveFlow provides a screening-ready version of the Enamine REAL Space, to our knowledge the largest library of ready-to-dock, drug-like molecules, comprising 69 billion compounds, also available in SELFIES format. An 18-dimensional grid of molecular properties prioritizes promising chemical subspaces, with optional active learning, reducing computational costs by orders of magnitude. AdaptiveFlow integrates >1,500 docking protocols, including GPU-accelerated and ML-based methods, and achieves near-linear scaling on up to 5.6 million CPUs in the Amazon Web Services cloud. We identified nanomolar inhibitors of two disease-relevant targets, ferroptosis suppressor protein 1 (FSP1) and poly(ADP-ribose) polymerase 1. Co-crystal structures provided mechanistic insights into FSP1 inhibition. AdaptiveFlow enables drug discovery at unprecedented scale and supports the development of AI-driven methods.

Automatic bioinformatic software named entity recognition from literature

Tue, 01 Sep 2026 00:00:00 +0000

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

Ensuring multiomics data reproducibility for artificial intelligence with reference materials as a common calibrator

Tue, 01 Sep 2026 00:00:00 +0000

Nature Biotechnology, Published online: 01 September 2026; doi:10.1038/s41587-026-03267-1 Reference materials should be adopted as a common calibrator for multiomics measurement and co‑profiled with study samples. Multiomics results should be reported as sample‑to‑reference ratios so that they are reproducible and suitable for artificial intelligence tools.

From Prompt to Provenance: BloClaw, a Capability-Gated AI4S Workstation for Auditable Computational Biology

Tue, 01 Sep 2026 00:00:00 +0000

Scientific agents can produce plausible answers while remaining unable to establish whether the computation behind an answer is executable, recoverable, or reproducible. We present BloClaw, an AI4S workstation built around a simple principle: a scientific agent should know what it can do, show how it did it, and state what remains unvalidated. Each capability declares an execution state, input constraints, dependencies, expected outputs, and scientific limitations. Natural-language requests are translated into structured tasks, validated against this registry, executed through scientific tools, and recorded in a provenance-aware Living Lab Notebook. The system is designed to detect invalid inputs, failed tool calls, missing dependencies, and remote timeouts, and to route them to repair, retry, or escalation. The implemented and tested scope comprises RDKit-based molecular property and rule screening, protein structure analysis, docking-pose inspection, 3D visualization, and structured reporting. We demonstrate the workflow on a PubChem-retrieved osimertinib structure and a supplied 6LU7 docking artifact: the former yields deterministic descriptors (molecular weight 499.619 Da, cLogP 4.5098, TPSA 87.55 A^2), while the latter contains 2,387 protein ATOM records, 309 residues, and nine pose records. These examples are workflow demonstrations, not efficacy or affinity studies. Beyond retrospective prediction, the manuscript specifies a prior-minimized constructive mode in which a desired function is compiled into explicit physical, chemical, and systems constraints, candidate mechanisms are simulated, and observations are reintroduced for calibration and falsification; this is a proposed extension rather than a result of the present case studies. We describe an evaluation protocol that compares BloClaw with a standard single-agent workflow and fixed-script execution using task completion, scientific correctness, recovery success, provenance completeness, reproducibility, human review time, latency, and cost. This manuscript reports the system design, verified capability boundary, deterministic software artifacts, and a reproducible evaluation protocol; it does not claim benchmark improvements before those experiments are run. BloClaw is an execution and accountability layer for AI-assisted research, complementing expert review and experimental validation rather…

PhageTAILor leverages machine learning for phage tail-like elements detection and classification in plant-associated bacteria

Tue, 01 Sep 2026 00:00:00 +0000

Phage tail-like elements (PTEs) -- tailocins, bacterial type VI secretion systems (T6SS), and extracellular contractile injection systems (eCIS) -- are contractile nanomachines that bacteria use to kill their neighbors and compete within their micro-ecosystems. PTEs help shape microbial community composition. Most PTE detection tools only detect a single PTE class. Moreover, most tailocin detection methods are largely restricted to Pseudomonas, leaving a key part of tailocin diversity uncharacterized. In this work, we present PhageTAILor (https://github.com/hjcho-bio/PhageTAILor), an integrative and fully automated pipeline that detects and classifies prophages and 3 PTE classes from bacterial genomes. PhageTAILor combines a 6-detector homology-based candidate search (geNomad, tail-gene, PHROGs-tail, SecReT6, eCIStem, and a divergence-tolerant tail-HMM detector) with a LightGBM classifier comprising 1 multiclass and 3 binary heads, trained on 6,501 bacterial genomes carrying 13,082 prophages and PTEs. A phylogeny-free feature matrix used in our model keeps predictions reproducible between model construction and user inference. PhageTAILor performs strongly at the genome level and generalizes beyond its Pseudomonas-rich training set. On a 76-strain cross-clade benchmark, PhageTAILor detected tailocins at F1 = 0.955. Furthermore, it identified 12 of 13 experimentally validated tailocins spanning five genera versus 2 of 13 for a Pseudomonas-restricted tool TattleTail. PhageTAILor also demonstrated sensitivity equivalent to viral detection tool geNomad while avoiding its higher false-positive rate. Applied to 7,925 plant- and soil-associated bacterial isolates, PhageTAILor showed that prophages in the phyllosphere and tailocins in plant-associated bacteria, whereas eCIS are enriched in soil. PhageTAILor is distributed as an open-source, modular pipeline with a command-line interface.

Medea: AI Agent for Therapeutic Reasoning Across Biological Contexts

Tue, 01 Sep 2026 00:00:00 +0000

AI agents for therapeutic reasoning across biological contexts - Michelle M. Li, Carnegie Mellon University - Microsoft Research New England Generative Modeling & Sampling Seminar Therapeutic hypotheses can transfer across diseases but their relevance depends on biological context. The same target, perturbation, or treatment can produce different effects across cell types, disease states, genetic backgrounds, and patients. Therapeutic reasoning therefore requires methods that preserve context, test when evidence supports transfer, and identify where context-specific effects limit it. Although AI agents can perform therapeutic analyses, existing systems often fail to preserve biological context over long workflows, verify intermediate computational steps, or reconcile conflicting evidence across datasets and literature. We introduce Medea, an AI agent for therapeutic reasoning across biological contexts. Medea executes multi-step analyses using biological tools, machine learning models, and literature retrieval while enforcing verification during planning, execution, and evidence synthesis. We evaluate Medea across 5,673 open-ended analyses in three domains: cell type specific therapeutic target nomination in five diseases and 29 cell types, synthetic lethality prediction in 7 cancer cell lines, and immunotherapy response prediction from multimodal patient profiles. Using a previously unpublished epistatic miniarray profiling screen performed under two DNA-damaging treatments, we evaluate Medea on predicting synthetic lethality among 238,046 gene-gene pairs in yeast. Medea predicts these experimentally measured synthetic lethal interactions, indicating that its performance reflects biological relevance rather than information leakage from benchmark datasets. Across these evaluations, Medea improves performance over large language models, reasoning models, biomedical agents, and specialized machine learning models while maintaining low failure rates and calibrated abstention. These results show that verifiable AI agents can perform therapeutic analyses across biological contexts. Speaker bio Michelle M. Li is an Assistant Professor in Biomedical Engineering at Carnegie Mellon University. Her research focuses on developing novel AI algorithms that are grounded in biological and medical principles to minimize the risk of contextual error, where predictions appe…

GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models

Mon, 31 Aug 2026 16:00:00 +0000

At a glance - The Flash family extends GigaPath and GigaTIME with dramatically improved efficiency, making large-scale pathology research more accessible and practical. - A distilled pathology foundation model backbone reduces computational requirements without sacrificing performance, enabling repeated analyses across larger patient cohorts. - These open models support population-scale discovery, helping researchers investigate disease biology, biomarkers, and clinical outcomes across diverse cancer datasets. GigaPath (opens in new tab) and GigaTIME (opens in new tab) demonstrated how foundation models can support whole-slide analysis and tumor microenvironment modeling from routinely collected pathology data. GigaPath-Flash and GigaTIME-Flash make these capabilities substantially more efficient, enabling researchers to analyze larger cohorts, run more experiments, and move toward population-scale discovery. GigaPath-Flash and GigaTIME-Flash are research models. They are not intended or validated for clinical use, including diagnosis, prognosis, treatment selection, or other patient-care decisions. Performance may vary across datasets, scanners, institutions, populations, and use cases. The scale opportunity in computational pathology Histopathology is among the richest and most widely available sources of information in cancer research. Every tissue biopsy produces a whole-slide image that captures cellular morphology at subcellular resolution — and hospitals generate millions of these slides each year. This data contains information relevant to diagnosis, prognosis, treatment selection, and the biology of the tumor microenvironment. Foundation models have begun to unlock this information at scale. But whole-slide images are large — often exceeding a gigapixel — and applying a foundation model to even a single slide requires processing thousands of image tiles. When a research question involves tens of thousands of patients, the computational cost grows quickly. And population-scale discovery is not a single model run: it requires repeated cycles of feature extraction, statistical analysis, hypothesis testing, and validation across patient subgroups, biomarkers, and clinical endpoints. Computational cost limits the number of patients, datasets, tasks, and hypotheses that researchers can study. To realize the full potential of pathology foundation models,…

Machine-learning-assisted multiscale topology optimization of functionally graded superimposed lattice structures

Mon, 31 Aug 2026 04:00:00 +0000

Functionally graded lattice structures enable lightweight designs with spatially tunable stiffness and density, but their use in multiscale topology optimization is limited by the cost of repeated computational homogenization. This work presents a machine learning-assisted multiscale optimization framework for regular superimposed lattice structures. The unit cell is formed by combining body-centered cubic, face-centered cubic, and simple cubic lattice components, each controlled by an independent geometric parameter. Offline computational homogenization is used to generate effective stiffness data, which are then used to train a Cholesky-constrained neural network surrogate. This representation reconstructs the homogenized stiffness tensor in a physically admissible form. A separate neural network is trained to predict relative density from Monte Carlo-based density estimates. We incorporate our surrogates into a two-stage topology optimization strategy. First, a macroscale topology is obtained using the solid isotropic material with penalization (SIMP) method. The resulting solid region is then used for microscale lattice optimization, where the local lattice parameters are updated using the method of moving asymptotes (MMA). The trained stiffness and density surrogates replace repeated online homogenization during this stage. The method is demonstrated on a three-dimensional Messerschmitt-B\"olkow-Blohm (MBB) beam benchmark, producing spatially varying lattice parameters and relative density fields consistent with compliance minimization under a material constraint.

Class-Based Heuristic Selection for Solving the Flying Block Puzzle

Mon, 31 Aug 2026 04:00:00 +0000

Heuristic search underlies planning in autonomous systems ranging from warehouse logistics to robotic navigation, yet generic heuristics fail to exploit the structural constraints that govern constrained spatial domains, causing search performance to degrade catastrophically on harder instances. We study this problem through the two-column Flying Block Puzzle, a rigorously NP-complete spatial planning microworld whose bottleneck geometry mirrors clearance-to-size constraints encountered in multi-agent path finding, autonomous vehicle navigation, and block relocation systems. We introduce the Class-Based Heuristic A* (CBHA*) algorithm, which integrates a General Move Constraint to capture minimum displacement costs when vacant units are scarce, a formal kinematic taxonomy partitioning the state space into seven mutually exclusive classes with provably admissible heuristics based on vacancy ratio and goal-piece geometry, and a class-conditional tie-breaking mechanism that dynamically switches between depth-priority and vertical-distance ordering to overcome f-value plateaus. Over 146 benchmark instances, CBHA* achieves a 93.4% success rate against 64% for Depth-Prioritized A*, 39% for Standard A*, and 17% for BFS, while reducing node expansions by 87.98% relative to Standard A* and sustaining an average effective branching factor of approximately 3, demonstrating that class-triggered adaptive heuristics constitute a principled mechanism for efficient spatial planning that generalizes structurally to physical constraint systems.

Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution

Mon, 31 Aug 2026 04:00:00 +0000

Multi-label graph learning intends to capture the intrinsic complexity of real-world applications, where one sample is often related to multiple groups or consists of multiple objects. To date, a handful of multi-label graph learning methods exist, but none of them integrate training-time interpretation capability. While post-hoc graph explainers have been developed, they do not explicitly model label-dependent evidence sharing in multi-label graph learners, especially when label pairs are weakly or negatively associated. As a result, post-hoc approaches may miss how evidence should be shared or separated across different labels. This paper advances a new end-to-end self-explainable multi-label graph neural network (SEMGNN), which aims to simultaneously classify multi-labeled nodes and identify edges significantly contributing to each target node w.r.t. predicted labels. Different from post-hoc methods, SEMGNN jointly learns a predictor and a sparse edge-mask explainer within a unified framework and training objective. Label-label correlations are used to improve multi-label node classification and enhance individual label explanations, so that different labels of a node can be supported by distinct yet coherent structural and/or correlated evidence. Experiments and comparisons on synthetic and real-world multi-label networks, in social networking, entertainment, and life sciences, show that SEMGNN achieves competitive or improved predictive performance while providing more faithful and compact label-conditioned explanations.

Efficient Auto-Interpretability of AI Models in Biology

Mon, 31 Aug 2026 04:00:00 +0000

Sparse autoencoders (SAEs), and other interpretability methods could turn AI models in Biology and other fields into engines of scientific discovery by explaining the superhuman capabilities of those models. However, a latent is only useful if we know three things: whether it is coherent, whether it can be described, and whether that description has predictive power. These questions are routinely conflated. We assemble them into a single pipeline and report the practical innovations each stage required. First, cross-seed dictionary stability prioritises which latents are worth spending resources to investigate. Second, an intruder-detection task asks whether a latents activating examples share a recognizable pattern. Third, a separate pass proposes a candidate biological description which we convert into falsifiable predictions which can be tested in silico. Deployed on the Boltz-1 Pairformer trunk, stability prioritisation finds interpretable latents using about 4.4 times fewer latent evaluations each, and at 5.2 times lower measured cost, while recovering over half of them, and the external check shows the surfaced motifs are significantly enriched for their claimed annotations. The results also suggest a possible tension: the cross- seed stability might be selecting for some types of features, like structure-related ones, much more than others, such as function-related features.

Dimension Bridging for 3D RANS with Neural Network Accelerated Gaussian Functional Regression

Mon, 31 Aug 2026 04:00:00 +0000

In many computational science and engineering problems, repeatedly solving fully resolved physics-based models to design for a quantity of interest (QoI) can quickly become intractable, requiring the use of low-fidelity models to predict the same QoI but introduce errors where some features are neglected or are otherwise inaccurately resolved. We use Gaussian Functional Regression (GFR) to learn a correction to a 2D Reynolds-Averaged Navier-Stokes (RANS) model to predict the aerodynamic coefficients from a 3D RANS model. This model pair has a disparity in the governing physics from the reduced dimensionality, a previously unexplored application for GFR. Empirically, our results show that with a proper choice of low-dimensional (LD) model, the proposed kernel allows for the use of fewer high-dimensional (HD) evaluations to regress a response surface to the same level of accuracy as standard stationary kernels. Moreover, the new kernel provides more informative uncertainty quantification, which we show is advantageous when used to drive an adaptive sampling algorithm. Finally, we propose a novel neural network accelerated kernel, which we show offers predictions in good agreement while speeding up evaluations by millions of times in wall clock measurements, bringing the computational budget within the real-time regime.

Benchmarking General Mobile Assistants in Challenging Real-World Scenarios

Mon, 31 Aug 2026 04:00:00 +0000

Graphical user interfaces have emerged as an important environment for evaluating autonomous AI agents on multimodal interactive tasks. Existing benchmarks such as AndroidWorld and MobileWorld provide strong foundations for mobile agent evaluation, but their application coverage and task design do not yet fully capture the diversity and complexity of realistic mobile use. We present GMA, a benchmark for evaluating general mobile assistants in challenging real-world scenarios. GMA introduces seven applications based on open-source projects, spanning domains such as lifestyle sharing and travel planning, and 300 tasks across four difficulty tiers, from atomic actions to complex multi-step workflows. We evaluate eight frontier models and find that performance declines substantially as task complexity increases, with current agents remaining far from reliably handling realistic user requirements. We further conduct controlled ablation studies of agent harness choices, including context retention and explicit state tracking, under a shared environment, model setting, and task taxonomy. Results show that appropriate harness design can meaningfully improve performance, particularly on demanding workflows, while the effectiveness of specific designs can vary across foundation models. Overall, GMA complements existing benchmarks by expanding application coverage and task complexity, providing a challenging testbed for evaluating mobile agents and studying how harness design supports reliable execution in complex mobile workflows.

Compiling Chemical Knowledge into Executable Descriptors for Materials Prediction

Mon, 31 Aug 2026 04:00:00 +0000

Materials prediction depends critically on how scientific knowledge is represented, yet many governing considerations exist only as natural-language heuristics that conventional learners cannot use. We introduce CRISP, a large language model-assisted framework that treats representation construction as a rule-space exploration and compilation problem: it repeatedly samples target-relevant chemical rules without access to structures, labels or data splits, consolidates related concepts, and compiles each into an executable scalar descriptor supplied to a conventional learner. For positive-unlabeled inorganic-crystal synthesizability, CRISP outperformed expert-curated and generic structural representations under a shared learner and surpassed purpose-built synthesizability models, with its advantage most pronounced under structural-size and chemical-family shifts. Infrequently generated rules contributed complementary predictive information, showing that generation frequency does not determine utility. The same workflow yielded competitive representations for formation energy and ionic conductivity while revealing task-dependent limits for shear modulus, establishing a dataset-blind, auditable route from broad chemical knowledge to transferable computational representations.

OrbGNN: A Wave function-based Machine Learning Interelectronic Representation

Mon, 31 Aug 2026 04:00:00 +0000

Machine learning interatomic potentials (MLIPs) have become emerging tools in molecular modeling and computational chemistry. By learning high-dimensional potential energy surfaces from quantum chemical data, MLIPs enable accurate and efficient predictions of structural, thermodynamic, and dynamical properties. However, such models have limitations in predictions of electronic properties and the effects of static electron correlation due to their lack of electronic structure information. This work presents OrbGNN, an electronic structure graph architecture analogous to molecular graph and MLIP frameworks, where pair-orbital interactions constitute the graph representation, while orbital entanglement encodes the connectivity between them. By embedding information derived from orbital correlation metrics directly into the graph topology, OrbGNN provides a compact representation of a molecule s orbital landscape and electron correlation patterns. Analysis of the behavior of the feature space in an orbital graph are shown to demonstrate model robustness. The model is evaluated for the dissociation of nitrogen and for a larger dataset of diatomic molecules. Finally, the OrbGNN model is applied to a set of octahedral iron(II) complexes to predict spin-state energy gaps.

Optimal Transport for Network Comparison: A Review with Machine Learning Applications

Mon, 31 Aug 2026 04:00:00 +0000

Network comparison using optimal transport is a growing area of research in network science. Unlike standard graph metrics, optimal transport computes both network dissimilarity and a transport plan that explains how one graph morphs into another. In this paper, we review how optimal transport compares undirected, unweighted graphs using three primary distances: the Wasserstein, Gromov-Wasserstein, and Bures-Wasserstein distances. We examine the closed form of the Wasserstein distance in one dimension via node feature probability distributions, and show how the transport plans of the Wasserstein and Gromov-Wasserstein distances capture which specific nodes influence the distance after graph perturbation. For the Bures-Wasserstein distance, we derive bounds using Laplacian spectra to bypass full spectral decompositions. Finally, we evaluate these distances using a synthetic network dataset for clustering and a real-world time series network for anomaly detection.

Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap

Mon, 31 Aug 2026 04:00:00 +0000

Post-training quantization is often treated as a semantically neutral optimization for edge deployment of Large Language Models. When a full-precision source checkpoint is evaluated and quantization is applied downstream without equivalent re-evaluation, this workflow creates a structural validation--deployment gap: because quantization is a many-to-one mapping over parameter space, source-precision certification does not guarantee behavioral equivalence in the deployed configuration. We formalize this gap through Quantization Behavioral Equivalence Classes (QBECs) and prove that QBEC membership does not imply behavioral equivalence, providing a theoretical basis for quantization-triggered backdoor attacks. Building on a three-stage adversarial fine-tuning framework, we embed latent malicious payloads into models that satisfy the source-precision checks used in our evaluation, yet activate targeted adversarial behavior upon INT8 or 4-bit compression. We evaluate this threat in two operationally motivated scenarios, tactical machine translation and political content analysis, extending prior work from decoder-only causal LMs to multilingual encoder-decoder sequence-to-sequence models. Results show that backdoored translation models move from zero measured friend--foe corruption at repaired FP16 to up to 85.02% inversion after quantization, and that a paired stance classifier measures an ideological shift of up to $\Delta\mathrm{Bias}=0.33$ upon compression. A cross-quantizer transferability analysis further shows that attack persistence varies across quantization schemes and model architectures, rather than being determined by nominal bit-width alone. These findings demonstrate that source-precision auditing alone does not rule out quantization-triggered behavior and that the final deployed configuration must be included in behavioral certification for trustworthy edge AI.

Half of alcohol, drug, and self-harm presentations cannot be identified in coded emergency department data: a diagnostic accuracy study of a large language model

Mon, 31 Aug 2026 00:00:00 +0000

Objective To measure the accuracy of clinical coding, clinician review, and a locally deployed large language model (LLM) in identifying alcohol, drug, and self-harm involvement in emergency department (ED) attendances, and quantify prevalence. Design Two-phase diagnostic accuracy study. In a validation week, the identification strategies were assessed against a conflict-adjudicated reference standard (n=2,256); the LLM was then applied to n=105,096 annual attendances at the same site. Setting UK Type 1 Emergency Department treating patients [≥]16yrs. Main outcome measures Prevalence quantification compared with the reference standard; sensitivity, specificity, and balanced accuracy of each strategy; monthly identification rates and adjusted annual prevalence. Results The reference standard identified 12.1% of attendances as involving alcohol, drugs, or self-harm (coding 6.0%; clinician 10.0%, LLM 15.6%). LLM balanced accuracy matched or outperformed clinician review in all three domains (alcohol 0.942 v 0.930, p=0.635; drug 0.959 v 0.791, p < 0.001; self-harm 0.982 v 0.908, p=0.004). Coding recorded 1.07 domains per identified patient against 1.32 in the reference standard. Adjusted annual prevalence corresponded to 12,890 domain involvements per year not identifiable in coded data. Subdomain classification found at least 81.6% of self-harm attendances required medical assessment for injury or overdose before psychiatric review. Conclusions Clinical coding identified fewer than half of presentations involving alcohol, drugs, and self-harm and rarely captured co-occurring domains; under-recording was present across a full year. A locally deployed LLM generated more complete structured data from existing clinical text within NHS infrastructure, at a scale which is not feasible for manual review.

An Entropy- and ISOMAP-Based Framework for Dynamic Epistemic Uncertainty in Clinical LLMs

Mon, 31 Aug 2026 00:00:00 +0000

The widespread adoption of clinical large language models (LLMs) introduces significant risks of automation bias, premature closure, and clinician deskilling. Current interpretability paradigms, including latent space trajectories, Concept Activation Vectors, and Concept Bottleneck Models, suffer from topological stagnation, metric distortion, and epistemic occlusion, frequently masking intermediate diagnostic uncertainty behind falsely confident outputs. To address these structural vulnerabilities, this paper introduces a novel closed-loop, multi-agent framework designed to quantify and visualize dynamic epistemic uncertainty in clinical LLM reasoning. By coupling predictive Shannon entropy with non-linear Isometric Feature Mapping (ISOMAP), the architecture projects high-dimensional inference state vectors onto a calibrated two-dimensional latent space, thereby assigning a quantifiable thermodynamic energy state to the reasoning path to track diagnostic velocity, cognitive momentum, and trajectory efficiency across sequential diagnostic rounds. Pilot validation across representative emergency medicine scenarios demonstrated distinct topological and information-theoretic behaviors: unconfounded cases (cerebellar infarction) exhibited smooth geodesic progression toward the ground truth alongside monotonic Shannon entropy decay from 2.15 to 1.74; noisy environments with ambiguous findings (spontaneous pneumothorax) suffered from trajectory wandering, local minimum traps, and high sustained entropy (~2.41) due to insufficient repulsive weighting for negative evidence; and triage-conflicted cases (acute cholangitis) achieved precise geometric proximity to the true node but experienced top-1 rank stagnation because the model conflated acute severity triage (sepsis) with anatomical etiology. By rendering machine hesitation and cognitive divergence visually auditable before final diagnostic crystallization, this geometric-information framework enables dynamic trust calibration and human-AI co-regulation at the point of care while establishing a clear mathematical foundation for future architectural interventions, such as dual-channel safety decoupling and non-linear repulsive weighting. Moving forward, validating these architectural enhancements across large-scale electronic health record databases and prospective clinical trials will be essential to realize its…

Surprisal-based large language models reveal immunologic insights in lobular breast cancer

Mon, 31 Aug 2026 00:00:00 +0000

In large data sets discovery is often limited to pre-conceived hypotheses and data fishing. Here we tested whether systematic exploration of AI generated hypotheses could uncover clinically meaningful signals in extensively studied data. We deployed AutoDiscovery, a newly launched large language model (LLM) framework designed to search for hypotheses based on surprisal and systematically interrogate complex datasets, on The Cancer Genome Atlas breast cancer cohort. The system did not identify clinically meaningful novel findings without human input. However, a seeded warm-start run with minimal text input from an oncologist revealed multiple interesting and surprising hypotheses. Among these was that a robust immune signature was present across all subtypes of invasive lobular carcinoma (ILC) that exceeded invasive ductal carcinoma (IDC). This observation was independently validated in independent cohorts and confirmed by high-sensitivity multi-immunofluorescence tumor tissue analyses. These results suggest immunotherapy approaches should be tested in ILC including early stage ER+HER2- ILC; these patients are currently excluded from large neoadjuvant immunotherapy trials. They further demonstrate that surprisal-based hypothesis generation frameworks can extract previously unappreciated patterns from deeply interrogated cancer datasets and imply that disease domain experts working with LLMs can derive more meaningful insights from complex data than either could achieve alone.

Uncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributions

Mon, 31 Aug 2026 00:00:00 +0000

End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeline, including data assimilation, at a fraction of its cost. These systems are deterministic and issue no uncertainty. Here we render the Aardvark Weather model probabilistic by attaching one stochastic mechanism to each component: learned, input-dependent noise at the observation encoder, capturing aleatoric uncertainty inherited from the observing system, and Monte Carlo dropout in the processor, capturing epistemic uncertainty in the learned dynamics. The resulting nested ensemble attributes forecast spread to the two sources through a law-of-total-variance decomposition, cross-checked by withholding observation streams. Probabilistic finetuning significantly improves the mean forecast, by 4.2% on average across variables and lead times. The ensemble is calibrated against ERA5 through the medium range (spread-skill ratio 0.98), keeps station RMSE within 2.4% of the deterministic model while beating it in CRPS at every lead time, and trails the operational ECMWF ensemble. The encoder branch behaves as observation-driven uncertainty. Component-attributed uncertainty makes end-to-end forecasts more transparent, a step toward observation-driven digital twins of the atmosphere.

A genomic catalog of Earth’s bacterial and archaeal symbionts

Mon, 31 Aug 2026 00:00:00 +0000

Nature Biotechnology, Published online: 31 August 2026; doi:10.1038/s41587-026-03213-1 Symbiotic relationships of microorganisms are predicted and cataloged with a machine learning tool.

Building Custom Batched Ensemble Weather Forecasting with NVIDIA Earth2Studio

Sat, 29 Aug 2026 18:57:26 +0000

In this tutorial, we build an ensemble weather forecasting workflow with NVIDIA Earth2Studio. We install the required Earth2Studio components while preserving Colab’s existing CUDA-enabled PyTorch environment, load the FCN prognostic model, and retrieve atmospheric initial conditions from GFS. We then implement a custom wind-power diagnostic that converts 10-meter wind components into turbine capacity factors, along with a variable-scaled perturbation system that applies physically appropriate noise amplitudes to different atmospheric variables while retaining an unperturbed control member. Using Earth2Studio’s low-level iterator, coordinate-mapping, batching, and Zarr APIs, we construct our own ensemble execution pipeline, write forecast and diagnostic fields to a coordinate-aware data store, and verify the forecasts against GFS analyses using latitude-weighted RMSE, fair CRPS, ensemble spread, and spread-skill ratios. Finally, we visualize ensemble uncertainty through spatial maps, geopotential-height spaghetti contours, point-based fan charts, wind-capacity-factor forecasts, and lead-time skill curves. import importlib.util, os, subprocess, sys if importlib.util.find_spec("earth2studio") is None: import numpy as _np, torch as _torch cfile = os.path.join(os.getcwd(), "e2s_constraints.txt") with open(cfile, "w") as f: f.write(f"torch=={_torch.__version__.split('+')[0]}\n") f.write(f"numpy=={_np.__version__}\n") env = {**os.environ, "PIP_CONSTRAINT": cfile} subprocess.check_call( [sys.executable, "-m", "pip", "install", "-q", "earth2studio[fcn,data,perturbation,statistics]"], env=env) print("\n>>> Install done. If the imports below fail: Runtime > Restart session, re-run.\n") os.environ.setdefault("EARTH2STUDIO_CACHE", "/content/e2s_cache") os.makedirs("outputs", exist_ok=True) from collections import OrderedDict from datetime import datetime, timedelta, timezone from tqdm.auto import tqdm from earth2studio.data import GFS, fetch_data from earth2studio.io import ZarrBackend from earth2studio.models.batch import batch_coords, batch_func from earth2studio.models.px import FCN from earth2studio.statistics import rmse from earth2studio.utils import handshake_coords, handshake_dim from earth2studio.utils.coords import map_coords from earth2studio.utils.time import to_time_array from earth2studio.utils.type import CoordSystem if DEVICE.type == "cpu": print("!! No…

Automated researchers can reliably mitigate alignment failures

Fri, 28 Aug 2026 17:25:56 +0000

As AI begins to build itself, automating alignment research becomes increasingly important to let safety research keep pace. Although measuring the success of alignment research is enormously challenging, researchers (at Anthropic and elsewhere) have developed benchmarks and automated auditing tools, such as Petri, that quantify common alignment failures, like deception, sycophancy, and jailbreaks. In one of our earlier experiments, we tasked Claude with finding effective ways to use weak AI models as “teachers” to supervise the training of stronger models (in this case, the “student” model). Now, we’re releasing a new report that builds on this idea. We had Claude autonomously train models to improve their performance on several public benchmarks that measure each of 10 categories of alignment failure. For instance, Claude improved models’ performance on privacy violation, measured by ConfAIde, PrivaCI-Bench, and PrivacyLens. Claude tackled one alignment failure at a time through a loop of searching literature, proposing methods and data, training, and then testing. We judged Claude’s success according to the “percentage of safety gap closed,” i.e., how far its methods moved the student model towards the theoretical perfect score, as judged across the range of benchmarks (typically three to five) for each category of alignment failure. We excluded alignment methods that hurt the student models’ general capabilities, and forbade Claude from distilling its own alignment directly into the target model. We enforced these constraints with a monitoring agent, which read every method Claude had in mind before it ran. Our aim was to assess whether the proposed methods would, first, remain effective on alignment evaluations that Claude was never shown during its research loop; second, avoid degrading the student model’s capabilities (since safety training might, for example, make models refuse tasks more often, reducing their overall usability); and, third, still work on larger models than the ones Claude was asked to align in this test. On each of these counts, Claude’s methods worked. For all 10 alignment failures, Claude found fixes that improved the target benchmarks without degrading capabilities. The best methods also worked on withheld alignment benchmarks and on Petri, an open-source tool that simulates adversarial multi-turn scenarios for testing misalignm…

Physics-Informed Neural Networks for Biot's Model via Fixed-Stress Splitting and Energy Natural Gradient Descent

Fri, 28 Aug 2026 04:00:00 +0000

Physics-Informed Neural Networks (PINNs) have recently gained considerable attention as a mesh-free framework for solving partial differential equations. Nevertheless, their performance deteriorates when applied to strongly coupled multiphysics systems, such as Biot's consolidation model, due to severely ill-conditioned optimization landscapes. In this work, we propose a robust PINN-based solver, termed FS-ENGD-PINN, which synergistically integrates physics-based decoupling with geometry-aware optimization. Specifically, the Fixed-Stress (FS) splitting scheme is employed to decompose the coupled poroelastic system into contractive mechanics and flow subproblems, thereby significantly improving training stability and convergence. To further accelerate optimization, we adopt Energy Natural Gradient Descent (ENGD), which approximates the Newton direction in function space effectively mitigates stiffness-induced slow convergence. Moreover, to address volumetric locking arising in the nearly incompressible regime, we incorporate a three-field mixed formulation with an additional total pressure variable into the PINN framework. Extensive numerical experiments demonstrate that the proposed FS-ENGD-PINN consistently outperforms standard PINN formulations in terms of accuracy and robustness, providing a unified and reliable learning-based solver for poroelasticity across a wide range of material parameters.

Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

Fri, 28 Aug 2026 04:00:00 +0000

Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only computational tools but also the institutions, workforce models, and collaborative practices that support scientific discovery. This report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing, the second in a three-year series focused on strengthening scientific computing ecosystems through socio-technical co-design. Workshop discussions identified four interdependent strategic themes: software ecosystems for AI-enabled scientific discovery; trust, validation, and traceability; human-AI teaming and paradigm shifts; and workforce, pedagogy, and governance. The report translates these themes into eight priorities for community action spanning shared research infrastructure, trust and traceability, user experience, human-AI teaming, workforce development, cross-sector coordination, stewardship and sustainability, and evaluation of scientific value. Together, these priorities outline directions for building scientific computing ecosystems that remain trustworthy, sustainable, innovative, and resilient as AI assumes a growing role in scientific work.

Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap

Fri, 28 Aug 2026 04:00:00 +0000

Battery Prognostics and Health Management (BPHM) is critical for ensuring the safe, reliable, and cost-effective operation of batteries across electric vehicles, grid storage, and consumer electronics. Conventional BPHM approaches, including physics-based models and task-centric deep learning methods, face challenges in computational efficiency and parameterization, cross-domain generalization, dependence on extensive labeled run-to-failure data, and model interpretability. Recent Large Models (LMs), built upon Transformer architectures and self-supervised pre-training, offer a transformative new paradigm to overcome these long-standing bottlenecks. This review provides the first comprehensive survey of LM applications in BPHM, systematically examining how these models address challenges in the field. We begin by elucidating the foundational technologies enabling LMs, including Transformer architectures, self-supervised learning, large-scale multimodal datasets, and PEFT techniques. We then categorize recent progress along four critical dimensions: mitigating data scarcity, enhancing generalization and robustness, integrating domain knowledge for interpretability, and enabling system-level automation. Despite promising results, significant challenges remain across data accessibility, intelligence validation, trustworthiness, and deployment feasibility. To guide future research, we propose a roadmap focused on building collaborative data ecosystems, validating intelligence for industrial applications, enhancing trustworthiness with physics-informed designs, and enabling efficient on-device deployment. This review establishes a systematic approach to understand and advance LM-driven BPHM, providing researchers and practitioners with essential insights for developing next-generation battery management systems capable of safe, reliable, and autonomous operation throughout battery lifecycles.

Background Intensity Estimation for Cassini-ISS Image Using Deep Learning-Based Diffusion Model

Fri, 28 Aug 2026 04:00:00 +0000

Accurate background intensity estimation is crucial for precise astrometric measurements in astronomical imaging, particularly in complex scenarios such as those encountered in Cassini Imaging Science Subsystem (ISS) observations of Saturn's ring system. Traditional methods, like polynomial fitting and statistical method, often fail in non-uniform conditions, such as those caused by Saturn's rings or scattered light, due to mismatched assumptions and reliance on prior knowledge. This results in biased estimates and poor generalizability. We propose a deep learning framework based on Denoising Diffusion Probabilistic Model (DDPM) to address these challenges. By learning noise patterns and iteratively reconstructing backgrounds, DDPM improve background intensity estimation in ring-gap regions of ISS images by up to 62% relative to polynomial fitting. Additionally, When applied to centroiding of unresolved satellites in ring-gap, DDPM-based background estimation enhances positional precision by about 14% in the line direction and 15% in the sample direction. The framework autonomously captures spatial correlations, requires no manual parameter tuning, and generalizes across diverse backgrounds. These characteristics make it a promising, assumption-light solution for background estimation tasks in astrometry, photometry, and source detection, with potential applications to exoplanet transit imaging, deep-field surveys, and future missions.

Benchmarking of Fast and Interpretable UF Machine Learning Potentials

Fri, 28 Aug 2026 04:00:00 +0000

Machine learning interatomic potentials (MLIPs) have emerged as a powerful alternative to density functional theory (DFT) for molecular dynamics simulations, offering near-DFT accuracy at a fraction of the computational cost. However, many state-of-the-art MLIPs remain computationally demanding and act as black boxes, limiting physical interpretability. In this work, we evaluate the ultra-fast force field (UF$^3$) potential, which employs linear regression with cubic B-spline basis to represent effective two- and three-body interactions. We show that UF$^3$ displays accuracy comparable to established models such as GAP, MTP, NNP (Behler Parrinello), and qSNAP MLIPs. We further investigate the transferability of UF$^3$ by computing melting points for six elemental systems with potentials fitted without any solid-liquid interface configurations or explicit thermodynamic information about melting. The model reproduces experimental melting points within $\sim$6% for simple metals (Ni, Cu, Li), but substantially underestimates them for Mo and Si and fails to yield a stable potential for Ge, reflecting the limitations of a fixed expansion truncated at the three-body term for systems with strong angular or covalent bonding. We further illustrate how UF$^3$'s spline-based formulation allows direct visualization of the learned interactions, enabling identification of unphysical behavior that black-box approaches often obscure.

Interpreting Latent Protein Language Model Features with Geometric Annotations

Fri, 28 Aug 2026 04:00:00 +0000

Protein language models (pLMs) encode information about protein sequences which enable downstream tasks such as structure prediction, but their internal representations are not well understood. Sparse autoencoders (SAEs) provide a promising tool to disentangle latent pLM representations into interpretable features, but existing annotation pipelines largely rely on protein-level annotations derived from database labels and LLM annotations of top activating sequences. Such annotations can overlook the localized residue-level and geometric patterns encoded by sparse features. We introduce an automated and scalable method for interpreting SAE features in ESM-2 by using geometrically inspired features of the protein $\text{C}_\alpha$ backbone. Across ESM-2 8M layers, an FDR-controlled discovery analysis shows that local geometry is significantly associated with many SAE features, with varying levels of predictive strength, expanding coverage beyond database and sequence-based methods. In particular, geometry can distinguish SAE features sharing the same database annotation, revealing substructure within known biological labels. A significant portion of SAE features activate on unannotated metagenomic protein sequences enabling us to use our SAE annotations to better understand these sequences. In addition, ablation experiments at the level of contact prediction show that removing found geometric features shifts ESM-2's predicted contact maps in the direction of the descriptor. This provides a robust method of annotating proteins activated within SAE neurons at a residue level, providing a bridge between mechanistic interpretability and structural biology.

Stability Analysis and Data-driven Verification via Recurrent Lyapunov Functions

Fri, 28 Aug 2026 04:00:00 +0000

Lyapunov's direct method is a cornerstone of stability and control, but it hinges on finding a Lyapunov function, a task demanding ingenuity or computation. A key difficulty is that every sub-level set of the function must be forward invariant, coupling its geometry to the system's trajectories. We relax this by replacing invariance with recurrence: a set is recurrent if every trajectory starting in it returns within a finite time. This yields the notion of a Recurrent Lyapunov Function (RLF), whose sub-level sets need only be recurrent. We show that, under mild conditions, RLFs guarantee stability, and we introduce stronger notions yielding asymptotic and exponential stability. We also give norm-based converse theorems: under the corresponding stability conditions, any norm is an RLF for their practical versions. We then develop GPU-based algorithms that certify (practical) stability from trajectory data alone, without a Lyapunov function. Certifying stability up to an $\varepsilon$-neighborhood needs only $O(\log(1/\varepsilon))$ trajectory evaluations, with constants growing as the certified decay rate nears the true one, exposing an intrinsic performance-cost trade-off.

Physics-Informed Stochastic Configuration Machine: A Backpropagation-Free Neural Network with Fast Training for Nonlinear Differential Equations

Fri, 28 Aug 2026 04:00:00 +0000

While Physics-Informed Neural Networks (PINNs) have emerged as a transformative paradigm for solving complex differential equations, their reliance on backpropagation-based gradient descent and automatic differentiation (AD) imposes significant computational bottlenecks and severe non-convex optimization challenges. To overcome these fundamental limitations, we propose the Physics-Informed Stochastic Configuration Machine (PI-SCM), a novel backpropagation-free framework for both forward and inverse problems in differential equations. The core mathematical contribution lies in the analytical evaluation of local Jacobians for nonlinear differential operators, which facilitates a linearized representation of the physical loss and projects it into a unified, linearized algebraic subspace. This reformulation allows for the explicit determination of optimal network weights via a sequence of generalized linear least squares solvers, effectively bypassing the iterative traps of traditional nonlinear optimizers. We develop a progressive algorithmic suite comprising localized construction (PI-SC-I), sliding-window updating (PI-SC-II), and global updating (PI-SC-III), and rigorously establish their universal approximation properties. Extensive experiments demonstrate that PI-SCM achieves high-fidelity predictive accuracy and robust parameter identification while accelerating the training process by orders of magnitude compared to standard PINNs. Our work provides a highly efficient and scalable foundation for next-generation, real-time Scientific Machine Learning applications.

KAIST Unveils Korean AlphaFold-Style Bio-AI Model K-Fold

Fri, 28 Aug 2026 02:00:50 +0000

The analysis speed is up to 25 times faster Strategic technology to escape under Big Tech Bio-artificial intelligence (AI), such as Google DeepMind's "Alpha Fold 3," which won the 2024 Nobel Prize in Chemistry, has also emerged in Korea. The speed of predicting protein structure and designing new drug candidates is the fastest in the world. KAIST, the organizer of the 'AI-specific foundation model project' promoted by the government, announced on the 28th that it has unveiled the next-generation bio-AI model 'K-Fold'. The results came less than a year after the start of the project to develop Sovereign AI specialized in the field of scientific research. K-Fold is an AI system that predicts the binding of proteins and drugs needed to develop new drugs. In order to develop a new drug, it is necessary to identify the structure of the causative protein and create a new drug structure that can bind to the protein. The difficulty of grasping the protein structure was the biggest bottleneck in the development of new drugs. AlphaFold won the Nobel Prize in Chemistry earlier because AI solved these bottlenecks. AlphaFold 3, developed by Google DeepMind, accurately predicted the three-dimensional structure of the protein within hours with only the protein amino acid sequence. This is the result of changing the paradigm of new drug development and scientific research. K-Fold is the Korean version of Alpha Fold. BioAI, a strategic asset, is needed for new drug development competitiveness, and bio-specific Sovereign AI developed with its own technology has been secured. Using this, Korea can significantly shorten the time for new drug development and bio-research with its own technology. According to KAIST, the K-Fold has similar performance to the world's best Alpha Fold 3, but is 25 times faster. In our internal analysis, the molecular structure prediction accuracy of K-Fold was close to AlphaFold3 and showed higher performance in some evaluation items. When predicting the next-generation drug platform, PROTAC, K-Fold's prediction was about 33% more accurate than AlphaFold 3. In particular, the prediction success rate was significantly higher than that of Alpha Fold 3 in the work of analyzing how the structure changes when proteins and drugs combine. In the benchmark evaluation, the prediction of changes in activity of the drug was 11% more accurate, and the predictio…

When authors are the best judges of their work

Fri, 28 Aug 2026 00:00:00 +0000

Nature Computational Science, Published online: 28 August 2026; doi:10.1038/s43588-026-01046-1 A large-scale experiment at a major machine learning conference shows that when researchers privately rank their own submissions, those rankings forecast future citations more reliably than peer-review scores.

Large language models as uncertainty-calibrated optimizers for experimental discovery

Fri, 28 Aug 2026 00:00:00 +0000

Nature Machine Intelligence, Published online: 28 August 2026; doi:10.1038/s42256-026-01283-z Although language models can be helpful in molecular design, they are not typically calibrated for uncertainty. Rankovic and colleagues present a method to train language models while taking into account the uncertainty of the data.

Enhancing reproducibility in hybrid Earth system models

Fri, 28 Aug 2026 00:00:00 +0000

Nature Machine Intelligence, Published online: 28 August 2026; doi:10.1038/s42256-026-01299-5 AI integration in Earth system models enhances prediction and modelling capabilities but also amplifies challenges for reproducibility. This Perspective introduces a framework for assessing reproducibility and provides practical ways to strengthen reproducibility in hybrid Earth system models.

PhysicsNeMo General Release v2.2.0

Thu, 27 Aug 2026 21:35:43 +0000

PhysicsNeMo General Release v2.2.0 Added Adds ShardTensor support for GeoTransolver and FLARE models. Adds zarr save/load for Mesh and DomainMesh via tensordict's zarr storage backend: physicsnemo.mesh.io.to_zarr / from_zarr , with training-appropriate chunking and zstd compression. MeshReader and DomainMeshReader transparently read zarr stores alongside .pmsh / .pdmsh (opt in via pattern ). Requires optional zarr >= 3 and a tensordict release with the zarr backend. Promotes GeoTransolver out of experimental to physicsnemo.models.geotransolver.GeoTransolver , together with the FLARE model ( physicsnemo.models.flare.FLARE ) and the reusable GALE and FLARE attention layers ( physicsnemo.nn.GALE , physicsnemo.nn.GALEBlock , physicsnemo.nn.FLARE ). The embedded OOD guard is decoupled from the model. Wrap a constructed GeoTransolver with physicsnemo.experimental.guardrails.embedded.GuardedGeoTransolver (or attach_ood_guard ) to enable out-of-distribution guarding. PhysicsNeMo removes the guard_config model argument. Legacy import shims keep the pre-move physicsnemo.experimental import paths working and emit a LegacyFeatureWarning pointing to the new locations. Adds zenith_azimuth_angles and zenith_azimuth_angles_from_timestamp to physicsnemo.utils.zenith_angle , returning (sin_zenith, cos_zenith, sin_azimuth, cos_azimuth) alongside the existing cos_zenith_angle / cos_zenith_angle_from_timestamp helpers. The azimuth follows the north-clockwise convention. Adds physicsnemo.nn.shrink_and_perturb_ , an in-place shrink-and-perturb weight re-initialization for warm-starting from pretrained weights. Adds dimension-generic volume mesh generation for implicit domains to physicsnemo.mesh.generate . mesh_implicit_domain meshes {x : phi(x) < 0} , clipped to the bounding box (box faces are honored as a boundary, so that external-flow "box minus obstacle" domains work directly), for any implicit function (signed-distance functions, level sets, or neural fields). Adds compile-safe subclassing extension points to domain_parallel.ShardTensor ( _extra_inner_tensors , __subclass_flatten_context__ / __subclass_unflatten__ , _stable_inner_sentinel , and a DTensor-style __metadata_guard__ ), so a subclass can carry extra inner tensors and opaque metadata through torch.compile without re-implementing the flatten protocol. Base behavior unchanged. Adds ShardTensor._subclass_propagated_…

Decoding cosmic signals with deep learning and Keras

Thu, 27 Aug 2026 17:25:29 +0000

AUG. 27, 2026 Astroparticle physics sits at the exciting intersection of astrophysics and particle physics and studies cosmic messengers: particles such as atomic nuclei, electrons, high-energy photons (light), and neutrinos accelerated in distant, cosmic phenomena and objects such as black holes, neutron stars, active galaxies, and supernovas, as well as other events from the early formation of the universe. By studying these messenger particles, we probe the most energetic and extreme phenomena known, from neutrinos produced in the Sun to the mysterious origins of ultra-high-energy cosmic-rays, and the elusive nature of dark matter and dark energy. Modern astroparticle physics is inherently data-intensive. It relies on gigantic observatories, often spanning thousands of square kilometers, designed to detect rare and tiny signals from the cosmos. These instruments generate enormous volumes of complex data, far exceeding what traditional analysis techniques were designed to handle. This blog post focuses on the growing role of deep learning in astroparticle physics and the new possibilities it unlocks in this data-rich research field. These methods offer new prospects to improve instrument sensitivity, uncover patterns that would otherwise remain hidden, and search for signal anomalies. Ultimately, these advances help to address some of the most fundamental questions: “What is the structure of the Universe?”, and “What is the elementary nature of matter?” From stars and planets to galaxies and nebulae, astronomy is deeply rooted in the discovery and observation of fascinating objects in the Universe. In Astronomy, light (ranging from infrared to visible light, and all the way up to X-rays) is the messenger that is observed, providing the key to studying the cosmos. Astroparticle physics extends astronomy into the highest-energy regime, where the Universe reveals its most extreme phenomena. It looks at gamma rays (high-energetic photons, i.e., light), with charged nuclei, neutrinos, and most recently, gravitational waves. By observing not only a single messenger but combining multiple kinds, modern astroparticle physics opens a new window into the universe. However, for observations at these high energies, quantum and particle-interaction effects fundamentally change how we observe the cosmos. Individual high-energy particles can no longer be detected direct…

Ai2 and Providence Swedish Cancer Institute partner to advance AI-assisted scientific discovery

Thu, 27 Aug 2026 08:00:00 +0000

Ai2 and Providence Swedish Cancer Institute partner to advance AI-assisted scientific discovery The collaboration includes clinically meaningful research findings on lobular breast cancer, and a local deployment to enable next-gen discovery while keeping clinical data secure. This partnership represents an important milestone for AutoDiscovery as it moves beyond public research datasets into active cancer research programs at a leading oncology research center. It’s a particularly significant deployment given the high standard required for AI adoption in biomedical science, where every finding must be transparent, reproducible, and independently validated before informing future research. The decision to expand the collaboration builds on a new research paperauthored by a joint team led by Providence researchers, Dr. Kelly Paulson of the Paul G. Allen Research Center and Dr. Sasha Stanton of the Earle A. Chiles Research Institute, and Ai2’s Senior Research Scientist Bodhisattwa Majumder. Working together, the team used AutoDiscovery to analyze one of the world's most extensively studied breast cancer datasets, uncovering a stronger immune signature in invasive lobular breast cancer than previously recognized. Subsequently, this finding was validated across an independent patient dataset and through laboratory analysis. The findings suggest that it may be possible for an entire class of breast cancer patients to benefit from immunotherapy, representing roughly 15% of breast cancers diagnosed in the US each year. The results also illustrate how AI-assisted hypothesis generation can help researchers uncover questions worthy of further investigation. Helping researchers navigate an era of scientific abundance Biomedical research has entered an era of unprecedented data generation. Public repositories now contain decades of genomic, molecular, clinical, and imaging data representing millions of patients, and protected datasets within cancer centers and research institutes represent a potential trove of new insights. While these resources have transformed medicine, they have also created a new challenge: the scale and complexity of modern datasets make it increasingly difficult to comprehensively explore the scientific questions they contain. Scientific research has traditionally been driven by hypothesis testing. Researchers develop a question, design an experim…

AIFL: advancing global streamflow prediction with operational machine learning

Thu, 27 Aug 2026 08:00:00 +0000

Every year, floods affect more people worldwide than any other natural hazard. Accurate streamflow forecasts, predicting how much water will flow through a river in the coming days, are essential for issuing timely warnings, managing reservoirs, and protecting lives. For over 15 years, ECMWF has provided the computational backbone for the European and Global Flood Awareness Systems (EFAS and GloFAS), coupling weather forecasts with process-based hydrological models, which are physics-driven simulations that represent the movement of water through the landscape using mathematical equations. Now, we are exploring how machine learning can complement these capabilities. In a new study, we introduce Artificial Intelligence for Floods (AIFL), a global streamflow forecasting model that learns directly from data and, crucially, learns to cope with the realities of operational weather forecasting. This work contributed to the Destination Earth (DestinE) initiative of the European Commission. As part of DestinE, ECMWF together with many partners is building data-driven models across the Earth system. AIFL targets the hydrology component, complementing parallel efforts on the atmosphere, ocean, sea ice, waves, and land, and helping lay the groundwork for a fully coupled machine learning Earth system model. The challenge: training on the past, forecasting in real time Machine learning models for hydrology are typically trained and forced with atmospheric reanalysis data, which provide gridded reconstructions of past weather that are temporally consistent and grounded in observations via data assimilation. This is a problem as when these models are deployed operationally, they are driven not by reanalysis, but by weather forecasts from systems like ECMWF’s Integrated Forecasting System (IFS). These systems have a different climatology as well as forecast uncertainty. This mismatch, called the reanalysis-to-forecast domain shift, can significantly degrade a model's performance in the real world. A model that looks excellent in research papers may struggle when it matters most. A two-stage learning strategy AIFL addresses the gap between reanalysis data and operational forecasts with a straightforward but effective two-stage training approach. In the first stage, we pre-train a Long Short-Term Memory (LSTM) neural network on 40 years of ERA5-Land reanalysis data (1980–201…

Federation Is Nearly Free, Reasoning Is Not: Tradeoffs for AI Co-Scientists in Protein Characterization Workflows

Thu, 27 Aug 2026 04:00:00 +0000

Natural language driven autonomous co-scientist workflows involve a fundamental trade-off between flexibility and reasoning at the expense of determinism, reproducibility, and observability. Such agents increasingly must communicate across institutional boundaries, where federation topology can shape latency and cost. We systematically evaluated these tradeoffs using a controlled ablation on a production agentic platform for science. We use a verifiable task: given a protein sequence, we ask an agent to confidently characterize its function by routing across common tools. We compare federation topology, classic RL vs LLM-driven harnesses, language model, and prompt expertise. We also stratify results by protein novelty. We find that the choice of LLM dominated prediction quality far more than topology or prompting (Opus ~92%-94% vs o4-mini ~40%-50%). The PPO policy was nearly as accurate as the best LLM (88%) at zero token cost, fastest latency, and perfect consistency, but yields no reasoning trace. Expert prompted LLMs reached the highest accuracy but were high-cost and less consistent; prompt dependence was largest when the task was hardest. Federation imposed a negligible penalty on performance. These results offer actionable guidance for deploying agents for scientific workflows: for routine, verifiable tasks, a cheap deterministic policy delivers near-frontier accuracy with complete reproducibility, while flexible LLM reasoning is best reserved for open-ended discovery.

Physics-Informed Error Field Learning: A Post-Training Optimization Framework for Physics-Informed Neural Networks

Thu, 27 Aug 2026 04:00:00 +0000

Physics-Informed Neural Networks (PINNs) have emerged as an important class of numerical methods for solving partial differential equations (PDEs). However, during the late-stage optimization process, further parameter updates often yield diminishing accuracy improvements while increasing computational costs. To address this issue, this paper proposes a Physics-Informed Error Field Learning (PIEFL) framework for PINNs. Unlike conventional approaches that continuously approximate the solution field using a single network, PIEFL introduces an auxiliary error network after the primary network achieves satisfactory accuracy and shifts the learning objective from the solution field to the error field. By deriving error control equations under physical constraints, the error network learns the discrepancy between the current approximation and the exact solution, and the learned error correction is combined with the primary prediction to improve solution accuracy. The proposed framework avoids continuous optimization of the entire solution space and focuses computational resources on correcting existing prediction errors. Moreover, PIEFL requires no modification to the primary network architecture, making it compatible with existing PINN models and applicable as a general post-training optimization strategy. Numerical experiments on representative PDEs demonstrate that PIEFL achieves higher solution accuracy under the same computational budget, validating its effectiveness in improving the performance of PINNs.

Unlocking Multimodal Protein Language Models at Inference Time

Thu, 27 Aug 2026 04:00:00 +0000

Multimodal protein language models (pLMs) learn joint protein sequence-structure distributions, and their generation performance should also depend critically on inference-time sampling strategies. Yet prior work has focused more on model training than on how inference-time strategies behave. In this paper, we establish a three-stage investigation framework to empirically study the inference design space of multimodal pLMs across three representative pLMs and four fundamental tasks. We evaluate vanilla sampling, task-specific classifier-free guidance, and reward-guided beam search on multimodal pLMs, corresponding to controls over sampling distributions, per-step logits, and parallel trajectories. Throughout the complementary advancements centered on exploration-exploitation trade-off, we (1) reveal the suboptimality of default inference protocols and identify task-oriented sampling preferences; (2) observe substantial quantitative gains across tasks, consistently boosting the upper bound performance of multimodal pLMs without updating model parameters; (3) derive conclusions about base models that differ from prior consensus.

Interpreting Protein Language Model Embeddings via Orthogonal Projection for Protein Fitness Prediction

Thu, 27 Aug 2026 04:00:00 +0000

Recently, there has been a growing adoption of protein language models (PLMs) in biomedical science. Their embeddings provide a rich numerical representation of protein sequences which achieve state-of-the-art performance on several downstream tasks including protein fitness prediction. However, PLM embeddings are not directly interpretable and, thereby, it remains unclear what features they encode. To gain insight into which biochemical properties of the protein are driving the prediction, we leverage an orthogonal projection technique that removes linear effects of known tabular features from embeddings and extend it to high-order and interaction effects. In this way, we remove the effects of interpretable biochemical features from PLM embeddings. In an ablation study, we show that this leads to a decrease in performance for a downstream classifier trained only on the embeddings to predict protein fitness. In an additional evaluation, we find that these biochemical features explain a substantial part of the variance in the predictions of this classifier. Hence, we can show that PLM embeddings encode patterns correlated with biochemical properties and quantify their contribution to predicting protein fitness. This computationally efficient approach is not limited to the features or embeddings considered here and is readily transferable to problem settings beyond protein fitness prediction.

Demystifying Reinforcement Learning Post-Training of Language Models

Thu, 27 Aug 2026 04:00:00 +0000

Reinforcement learning (RL) post-training has emerged as a powerful framework for enhancing the capabilities of large language models (LLMs), enabling impressive reasoning, math, and coding capabilities. Yet for many researchers and practitioners, the principles behind classical RL remain a "black box". In this work, we deconstruct the RL post-training algorithm, investigating each step to clarify what is actually happening beneath the surface. By isolating the mechanics of RL with Verifiable Rewards in a controlled and simplified environment, we examine how RL outcomes are shaped by the base model's prior distribution, the granularity of the reward signal, the diversity of the prompt distribution, and model scale. We use the entropy of the policy's output distribution as a lens to compare the distributions learned through pretraining, SFT, and RL post-training, revealing how each stage shapes model certainty. Our investigation sheds light on how these choices interact to affect post-training success. For example, we show that the effect of so-called 'spurious rewards' depends on the prompt distribution used for post-training. We also provide insight into why the success of RL post-training depends on whether the base model already places sufficient probability mass on the desired behavior, linking it to the classical concept of exploration in RL. Ultimately, we provide this primer as a resource to those in the NLP community wishing to incorporate RL as a tool in their toolbox.

Energy Yield and Lifetime Climate Classification via Machine Learning for Optimizing Photovoltaic Module Design and Materials

Thu, 27 Aug 2026 04:00:00 +0000

To resiliently and sustainably meet our future energy demand, photovoltaic (PV) modules must be deployed across a broad and diverse range of geographical regions with varying operating conditions. As these conditions strongly affect both performance and optimal system design, a dedicated PV-specific climate classification can be of great use. In this work, we develop a climate classification framework tailored to PV applications using a variety of machine learning (ML) techniques. Building on previous studies, our approach incorporates both energy yield, and for the first time, also the module lifetime with climate dependent degradation. We generate an interpolated dataset containing twelve input features and two target variables (i.e. energy yield and module lifetime). Feature importance analysis shows that annual global horizontal irradiation and ambient temperature are the most influential predictors. The most accurate regression model achieves root mean square errors (RMSE) of 0.007 MWh for energy yield and 1.5 years for lifetime prediction. The calculated feature importance scores are then integrated into a hierarchical clustering framework, resulting in 6 primary climate clusters (Tropical, Desert, Continental, Temperate, Boreal, and Polar) and 15 corresponding subclusters. Our analysis shows that the low temperature continental climate offers the highest discounted lifetime energy yield. These results can support a wide range of applications, including PV module optimization, system siting decisions, and comparative performance studies.

Interpretable physics-informed retrieval-augmented generation language model for end-to-end inorganic crystal synthesis planning

Thu, 27 Aug 2026 04:00:00 +0000

Synthesis planning for inorganic materials requires predicting both synthesizability and viable routes by linking microscopic thermodynamic stability with macroscopic synthesis methods, precursors, and processing conditions. Here, we develop an interpretable Physics-Informed Retrieval-Augmented Generation Language Model (PIRAG-LM) for end-to-end inorganic crystal synthesis planning. We construct a material-centered Structured Synthesis Knowledge Base (SSKB) containing route-level records for 13,820 experimentally synthesized inorganic crystals. PIRAG-LM retrieves historical precedents using chemical, structural, and thermodynamic similarity, then employs a structured LLM reasoning module to propose routes, precursors, and processing conditions and assess thermodynamic feasibility, kinetics, and accessibility. It achieves 91.4% accuracy in synthesis-method prediction, compared with 72.1% for the LLM alone, and generalizes to materials reported after the knowledge cutoff. Because the framework relies on retrieval rather than parametric memorization, its performance can be improved by expanding the SSKB without retraining the language model. Guided by PIRAG-LM, we experimentally synthesize five new compounds: BaMo0.3In0.7O2.95, BaNb0.4In0.6O2.9, Hg[B(CN)4]2, CoCo(CN)6, and SrNb2Fe2(PO4)6, via solid-state and solution routes. These results demonstrate an interpretable machine-learning approach that helps bridge computational materials discovery and experimental realization.

BixBench3: Benchmarking AI agents on research-study-scale computational biology tasks

Thu, 27 Aug 2026 04:00:00 +0000

Artificial intelligence (AI) promises to accelerate biological research by automating computational analyses. Yet the ability of AI agents to carry out computational biology at the scale of complete research studies has not been systematically evaluated. Here we introduce BixBench3, a benchmark that measures the capacity of AI agents to process raw biological data through to scientific results. We designed BixBench3 tasks to mirror the delegation of work from a scientist to an agent: the scientist chooses the research question and high-level methods, then delegates implementation of all analyses to the agent. In each task, an agent receives a research objective, methodological guidance, and raw data derived from a published scientific study, and must execute a sequence of analyses to achieve the research objective. The data artifacts resulting from these analyses - such as peak call matrices or differential expression tables - are programmatically graded against the corresponding artifacts generated and reported in the original study. Across 20 BixBench3 tasks encompassing the generation of 138 unique artifacts, we find that 13 frontier models achieve scores ranging from 0.00 for Gemini 3.1 Flash Lite to 0.48 for GPT 5.6 Sol. Agents perform worse on tasks with larger raw datasets (0.36 on tasks with < 100 GB versus 0.10 on tasks with >100 GB) and on analyses requiring more sequential steps (0.36 at 1-2 steps vs 0.24 at 3+). On average, agents use 6.8 hours, 102 million tokens, and $43 to complete each task, with the longest attempts consuming 24 hours, 1.07 billion tokens, and $525. Notably, the highest-scoring agents used fewer tokens and were cheaper than less performant options. These results reveal that LLMs vary substantially in their ability to (1) execute multiple sequential analysis steps coherently, (2) manage large quantities of raw data, and (3) work across scientific domains.

AFDBench: A Reasoning-First AI Scientist for National Weather Service Forecast Discussions

Thu, 27 Aug 2026 04:00:00 +0000

Large language models (LLMs) hallucinate numerical values when generating high-stakes meteorological text, posing risks for weather communication. We present AFDBench, an AI meteorologist that generates professional Area Forecast Discussions (AFDs) by reasoning through structured AI weather forecast data from Google's WeatherNext 2. We introduce AFDBench, the first benchmark for evaluating generative meteorological reasoning, comprising 7,732 expert written discussions from 13 National Weather Service (NWS) offices paired with real AI weather forecast inputs, and three complementary metrics: Met-Align (numerical accuracy), Style-Align (professional dialect adherence), and Input-Grounding (fidelity to source weather data). Zero-shot evaluations reveal that open-source LLMs achieve low Style-Align (~0.33) and moderate Input-Grounding (~0.88), failing to write in the professional NWS register or faithfully use their input data. We apply Group Relative Policy Optimization (GRPO) with domain-specific rewards targeting temperature accuracy, synoptic correctness, and format compliance. On 1,033 held-out samples from two unseen NWS offices, GRPO nearly doubles Style-Align from 0.318 to 0.619 and improves Input-Grounding from 0.881 to 0.940, demonstrating that reinforcement learning teaches a 7B-parameter model to write like a professional meteorologist and faithfully interpret AI weather data.

PMPNN-DDG: an accurate machine learning-based ΔΔG prediction pipeline trained on a novel interpretable feature set extracted from ProteinMPNN

Thu, 27 Aug 2026 00:00:00 +0000

An accurate and tractable approximation of the single-point mutation-induced change in protein thermodynamic stability, denoted by DDG, is critical for understanding the genotype-phenotype relationship. Several computational methods have been proposed for this problem; however, limited and error-prone training data and the difficult-to-predict magnitude of structural perturbations make this a challenging task. Consequently, the computational predictors proposed throughout the past decade incrementally improved prediction performance by proposing novel features, combining existing features, task-adapted neural network architectures, loss functions, data augmentation techniques, and pre-training procedures. In this work, we propose PMPNN-DDG, a Random Forest-based DDG prediction model, trained on a novel set of interpretable features extracted from the recently proposed message-passing neural network-based fixed backbone protein design model, ProteinMPNN. On the S669 independent test set, PMPNN-DDG achieves rF +R = 0.64 and RMSE = 1.45, outperforming all compared baseline methods across the reported evaluation measures. On the Ssym independent test set, it achieves rF +R = 0.81, rF -R = -0.99, and RMSE = 1.10, showing competitive performance relative to the compared baselines. PMPNN-DDG is publicly available at https://github.com/dRanger666/PMPNN-DDG.

Intent Drift in LLM-Assisted Brain Computer Interface Communication: An In-Silico Benchmark Under Simulated Decoder Corruption

Thu, 27 Aug 2026 00:00:00 +0000

Background Large language models are increasingly proposed to post-edit decoded text in communication brain-computer interfaces and augmentative communication. A fluent model can substitute a different intent than attempted (intent drift). Whether meaning survives or confidence flags failure is unmeasured. Methods In-silico benchmark of 20 open-weight models post-editing text (4,252,326 labeled generations) corrupted with an empirical P300 confusion matrix at five levels (0-40% character error rate, CER) across the ALS message-banking vocabulary (AUTH), a message-critical probe set, and matched controls. Outputs were scored faithful, degraded, or drift by an ensemble benchmarked against physicians. A substudy re-ran 562 messages under six interface policies (seven-model panel). Findings Detected drift rose steeply with corruption in all three corpora, from 2.2% to 60.3% at 0-40% target CER in AUTH, a stress-test upper bound, not an expected clinical rate (odds ratio 2.30 per 10-percentage-point rise in target CER). Stated confidence discriminated faithful outputs reasonably well (AUROC 0.83, 0.80-0.85) but was poorly calibrated (expected calibration error 0.32, 0.27-0.37): 28.4% of outputs at confidence 90 or higher were not faithful. Message-critical content carried a small excess after matching, surviving detector removal (rule-free OR 1.10). The ratio of faithful rescues to fluent errors exceeded 1 at low corruption but fell below 1 at 20-30% target CER. No interface policy removed drift: conservative editing and abstention lowered it, alternatives and expansion raised it; the best drifted on 18.0 per 100. A 2,281-item panel (16 of 20 models) gave moderate ensemble-versus-consensus agreement (kappa 0.41); correction lowered pooled drift 31.4% to 28.3%, and a CER-stratified physician-corrected re-analysis confirmed the dose-response at each level. Interpretation Language-model post-editing produced fluent semantic substitutions that rose with corruption, confidence did not reliably flag, and no interface policy removed. This does not demonstrate clinical harm; prospective human-in-the-loop evaluation is needed. Funding: A.G. and E.K. were supported in part by the Clinical and Translational Science Awards (CTSA) grant UL1TR002541 from the National Center for Advancing Translational Sciences, through the Harvard Catalyst | The Harvard Clinical and Translati…

GlucoFM: Foundation model for continuous glucose monitoring

Wed, 26 Aug 2026 20:10:19 +0000

August 26, 2026 Ahmed A. Metwally, Staff Research Scientist, and Zechen Li, Student Researcher, Google Research GlucoFM is a lightweight, self-supervised CGM foundation model that models slower glucose trends and short-term deviations in separate streams, producing transferable representations and setting new performance standards across diverse metabolic prediction tasks, including diabetes risk assessment, insulin resistance, beta-cell dysfunction, and post-prandial glycemic response. Consumer wearables use motion and physiological sensors to estimate activity and sleep, but these signals provide only an indirect view of glucose regulation. Continuous glucose monitors (CGM) complement these measurements by tracking interstitial glucose every few minutes through a small sensor inserted under the skin, capturing fasting, overnight, and post-meal patterns. Yet making sense of these traces remains challenging, especially when high-quality clinical labels that help interpret them are sparse and costly to obtain. Many existing CGM foundation models — including CGMformer, GluFormer, and CGM-JEPA — process glucose through a single representation stream rather than explicitly separating slow baseline and transient event dynamics. But CGM is not an undifferentiated data stream: it contains relatively slow baseline patterns punctuated by short-term deviations that may reflect meals, activity, or sensor artifacts. What if we could leverage daily CGM data to estimate things like diabetes risk, insulin resistance, and beta-cell dysfunction using limited labeled data? That's why we built GlucoFM, a self-supervised foundation model with a dual-stream design that separates slower glycemic trends from short-term deviations while preserving time-of-day and missingness. Latent-prediction objectives then learn their daily context and temporal evolution. We evaluated GlucoFM across four diverse cohorts on seven clinical prediction tasks — diabetes risk, insulin resistance, beta-cell dysfunction, hyperlipidemia, hypoglycemia, obesity, and glucotype — comprising 14 cohort–task evaluations. Across these evaluations, GlucoFM’s PR-AUC was 5.8 percentage points higher on average than that of the best-performing GluFormer variant evaluated, with both pre-trained on the same corpus. On PR-AUC, GlucoFM led all diabetes-risk and beta-cell-dysfunction evaluations and three of four insuli…

A Hybrid Two-Stage Machine Learning Pipeline for Fault Detection and Classification in Power Transmission Systems

Wed, 26 Aug 2026 04:00:00 +0000

Rapid and accurate fault detection in high-voltage transmission networks is essential for grid reliability and equipment protection. Transmission fault datasets are frequently imbalanced, and certain fault types produce electrical signatures that fall within the normal operating envelope, causing single-model classifiers to fail on safety-critical cases. This paper proposes a hybrid two-stage machine learning pipeline that decouples detection from classification. Stage 1 combines an Isolation Forest anomaly detector with an optional supervised binary detector through an OR-fusion rule; the supervised branch is allocated automatically during training for any fault class the anomaly detector cannot resolve, and is omitted when no such class exists. Stage 2 applies a Random Forest multiclass classifier only to samples flagged by Stage 1. Feature engineering is expressed as a per-measurement-point operator mapping six raw channels to eighteen features, including zero-sequence symmetrical components derived from Fortescue's theorem, yielding 18L features for L measurement points. On the TLFaultDataset, the pipeline raises Line-fault end-to-end accuracy from 31.3% to 95.8%. On an independent single-point dataset, the same framework attains 97.25% end-to-end accuracy across all classes including normal operation, exceeding the TLFed federated benchmark of 94.84% without GPU or federated infrastructure, at 0.05 ms per sample on CPU. Ablation on both datasets shows zero-sequence features resolving the three-phase versus three-phase-to-ground ambiguity, raising the F1-score of that class pair from 0.39 to 0.997. The direction of the zero-sequence signature is found to be system-dependent, motivating a learned decision boundary in place of a fixed relay threshold.

Auditing the Synthetic Memoir: Measuring Scene-Level Confabulation in LLM-Generated Autobiography Against the Documented Record

Wed, 26 Aug 2026 04:00:00 +0000

When a large language model (LLM) is asked to write a person's life, how much of what it writes actually happened? We present a scene-level case-study audit - the first quantified audit of LLM-generated autobiography against a subject-specific ground-truth corpus that we are aware of, based on an unsystematic literature search. The subject and the author of this paper are the same person: a 366-day "page-a-day" book of first-person anecdotal entries was drafted with a conversational LLM whose documented inputs were a template, two exemplar days, and each day's quote - not her corpus - and every day was subsequently audited at the anecdote-scene level against an independent verification corpus using a four-level rubric fixed before analysis. We define the verification-failure rate as the share of days not rated VERIFIED (scene positively corroborated): 354 of 366 days fail, 96.7% (Wilson 95% CI 94.4-98.1%). Only 12 days contain a corroborated scene; 19 days (5.2%) assert claims actively contradicted by the record; the dominant failure mode is grounded drift - real people, employers, and settings inside invented scenes - though its measured share varies across raters. Independent re-rating replicates the headline (no evidence the original rate was inflated) while showing that the four-way taxonomy has only fair-to-moderate reliability. Regenerating the same days with current named models reproduces 100% verification failure under the same inputs; grounding generation in the subject's corpus significantly improves the verification rate while leaving substantial residual failure (83.3%). We contribute the measurement, a reusable audit instrument whose WEAK/UNVERIFIED boundary we show to be unreliable, and a grounding remedy with quantified effect.

Discovering Cross-Language Reasoning Invariance in LLMs with Geometry-Invariant Sparse Autoencoders

Wed, 26 Aug 2026 04:00:00 +0000

Multilingual language models can solve the same mathematical problem in different languages, but it remains unclear whether they rely on shared features or on language-specific computations that only produce similar outputs. We study this question in five models from four families using the Multilingual Grade School Math (MGSM) dataset, with problems solved in English, German, French, Spanish, Russian, and Chinese, retaining problems with valid reasoning traces in all six languages and replaying those traces through the model to record representations at multiple layers. For each model, we first use Centered Kernel Alignment (CKA) to identify layers with cross-language alignment. At each selected layer, we train two sparse autoencoders (SAE): a baseline reconstruction-only model and a contrastive variant introduced in this work, the Geometry-Invariant SAE (GI-SAE). GI-SAE supplements the reconstruction loss with an Information Noise-Contrastive Estimation (InfoNCE) loss that trains the encoder to produce similar activations for traces of the same problem, regardless of language or token position. We then test whether the resulting shared features are functionally interchangeable by swapping their values between languages during the model's forward pass and measuring the resulting change in output, quantified by Kullback-Leibler (KL) divergence per feature. Although GI-SAE yields higher CKA and Jaccard similarity at nearly every layer, higher geometric similarity does not consistently imply greater functional interchangeability. We find that cross-language feature sharing is model- and architecture-dependent in this sample and appears at different depths in different models. GI-SAE primarily amplifies cross-language structure already present: the pattern is model-specific, with strengthening in Qwen, no functional benefit in Gemma, and mixed layer-dependent effects in Llama and Phi.

Finch: Toxicity Dose Response Curve Prediction of Chemical Compounds and Mixtures

Wed, 26 Aug 2026 04:00:00 +0000

A holistic approach to chemical mixtures is reshaping risk assessment emphasizing mixture testing over single compounds eliminating animal testing and advancing modeling methods. Most computational models still focus on individual chemicals and conventional mixture models like concentration addition and independent action are limited they struggle with multiple Modes of Action and often miss synergistic or antagonistic effects. Regulatory agencies need faster more efficient models that go beyond these constraints. Finch addresses these challenges with a novel workflow. It integrates molecular descriptor based frameworks and deep learning embeddings in multi task quantitative structure activity relationship models for improved chemical exposure prediction. DL embeddings preserve information from diverse inputs molecular descriptors physicochemical properties and large language model embeddings from SMILES by distilling key features into a latent space enhancing machine learning predictions. Finch multi task learning optimizes multiple loss functions simultaneously leveraging all available data to learn generalized representations. This enables effective modeling of complex ingredient interactions within mixtures offering a significant advancement for regulatory safety assessment.

Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value

Wed, 26 Aug 2026 04:00:00 +0000

Large language models (LLMs) are becoming routine instruments of scientific research, assisting with literature synthesis, hypothesis development, coding, and formal reasoning. Their use raises a central epistemic question: when parts of scientific reasoning are delegated to an artificial system, what conditions must remain under human control for the resulting knowledge claims to retain epistemic legitimacy and accountable authorship? This paper develops a normative and conceptual framework for analyzing such delegation. Scientific reasoning is treated as a distributed process in which the origin of a contribution may vary between human and machine, while responsibility for its acceptance into the scientific record remains human. The framework distinguishes content origin $O(g)$, completion of human verification $V(g)$, responsibility assignment $R(g)$, accountable human ownership $M(g)$, and epistemic outcome $E(g)$. These constructs separate the provenance of a claim from the process by which it is checked, the epistemic outcome of that checking, and the human responsibility attached to its disposition. The central proposition is that the ethical boundary of LLM-assisted research is determined primarily by adequate verification and accountable human ownership rather than by the degree of machine involvement itself. On this basis, the paper develops the notion of an \emph{epistemic audit}: a structured record of delegation, verification, provenance, and responsibility intended to make AI-assisted reasoning transparent and reviewable. The resulting framework provides a formal vocabulary for distinguishing responsible cognitive delegation from the transfer or neglect of epistemic responsibility in scientific research.

LLM Agents Perform Controlled Experiments Using Simulation Models

Wed, 26 Aug 2026 04:00:00 +0000

Large language models (LLMs) have shown strong capabilities in reasoning, planning, and tool use, but many scientific and engineering tasks require more than plausible text and code generation. They require understanding how a system responds to intervention, which in practice depends on controlled experimentation. In this work, we propose a multi-agent framework that enables LLM agents to conduct controlled experiments with scientific simulation models for pharmaceutical process design. Given a user query and a baseline configuration, the system constructs a structured task representation, designs experiments, executes comparative simulation, interprets the resulting outcomes, and synthesizes evidence-based recommendations for process parameter optimization. By coupling language models with high-fidelity simulation models in an interactive agent framework, the proposed system supports reasoning through intervention, comparison, and observation. As a result, it produces more specific and actionable outputs than language-only reasoning. In an industrial application setting, this advantage is reflected in higher output specificity as well as improved user-rated correctness and helpfulness. Ablation studies and visualized case analyses further demonstrate the effectiveness and practical utility of simulation-integrated experimental reasoning.

Optimizing RNA yield using deep neural networks coupled to massively parallel screening

Wed, 26 Aug 2026 04:00:00 +0000

Messenger RNA (mRNA)-based therapeutics have emerged as a powerful platform for vaccines, protein replacement therapies, and cancer immunotherapy. A critical bottleneck in mRNA development is manufacturing large quantities of RNA economically, as measured by RNA yield emerging from an in vitro transcription (IVT) reaction. However, how promoter-adjacent DNA sequences influence RNA yield remains poorly characterized. Here, we present an integrated deep learning framework that leverages massively parallel next-generation sequencing (NGS) assays to measure RNA yield across large sequence spaces. A library of 10^5 randomized oligonucleotide sequences was designed to systematically explore sequence diversity within a defined structural context. DNA and RNA abundances were quantified in parallel using Illumina sequencing, enabling high-resolution measurement of sequence-to-yield relationships at scale. Sequences were one-hot encoded and used to train deep learning models, using a convolutional neural network architecture. The model achieved a Pearson correlation of 0.94 between predicted and experimentally measured RNA yield on a held-out test set, demonstrating strong generalization across diverse sequence contexts. Importantly, the trained model can be deployed in a production environment to score and rank novel RNA sequence designs by predicted IVT yield, enabling cost-effective, pre-experimental prioritization of the most manufacturable candidates. This framework establishes a scalable, data-driven approach to DNA and RNA sequence optimization, with broad applicability to vaccine antigen design, therapeutic protein delivery, and synthetic biology. By integrating high-throughput experimentation with advanced deep learning modeling, it significantly reduces screening costs and accelerates RNA engineering cycle times.

Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theory

Wed, 26 Aug 2026 04:00:00 +0000

Kohn--Sham density functional theory (DFT) underpins electronic-structure simulations, but repeated orbital diagonalizations lead to cubic scaling, restricting quantum calculations to modest scales only. Eliminating these auxiliary orbitals while retaining Kohn--Sham accuracy is the central goal of orbital-free DFT, but both analytical and machine-learning methods have so far fallen short. Prior learning approaches either try to learn the variational kinetic-energy functionals, which are ill-conditioned, or directly predict the ground state, which extrapolate poorly to larger systems. Instead, we identify the Kohn--Sham map as the right learning target for orbital-free DFT. It maps a Kohn--Sham potential directly to the corresponding density and noninteracting kinetic energy, quantities otherwise obtained through an orbital diagonalization. Focusing on the density component in this work, a domain-invariant $\mathrm{SE}(3)$-equivariant Fourier neural operator learns to predict it from the potential as input on real-space grids, enabling stable quasi-linear scaling SCFs. Trained jointly on 8,504 molecules and solids, a single model generalizes to out-of-distribution organic molecules, insulators, and metals. For the first time, the same method converges SCFs across these systems without explicitly constructing Kohn--Sham orbitals, while reproducing densities, electronic spectra, and structural observables at Kohn--Sham DFT accuracy. Linear-scaling SCFs additionally allow converging magnesium dislocation densities containing up to 82,500 valence electrons on a single GPU.

MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models

Wed, 26 Aug 2026 04:00:00 +0000

Molecular embedding models can serve as foundational infrastructure for computational chemistry and drug discovery, where reusable vector representations support property prediction, virtual screening, and retrieval. Most molecular encoders are specialist models built around a single molecular view, producing unconditional vectors with no language interface for varying the representation. We ask whether multimodal large language models (MLLMs), which natively process images, text, and symbolic inputs, can instead serve as \emph{general molecular embedding models} that produce embeddings conditioned on both a molecular profile and a natural-language semantic context. We introduce \textbf{MolEmb}, a lightweight framework that adapts MLLMs by aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective. The resulting embedding model is competitive on molecular property prediction and supports cross-modal molecule--text retrieval in the same space. We further introduce \textbf{MolCAR}, a diagnostic benchmark for context-aware retrieval, and find that context-aware molecular embedding is primarily a data property of the supervision. These results suggest that MLLMs are not merely chemistry assistants or generators, but a viable and extensible route to general molecular embedding models.

A Survey Detection Channel Overrides Pixels in an Astronomical Foundation Model and Biases Tomographic Mean Redshifts

Wed, 26 Aug 2026 04:00:00 +0000

Foundation models for astronomy are trained on survey pixels together with the catalogue products derived from those pixels. Those catalogues are incomplete at a measurable rate, and a model trained on both inherits that incompleteness as a systematic. We audit AION-1, a 39-modality transformer trained on more than 200 million objects, using causal interventions on its inputs. Holding the image tokens byte-identical and editing only the survey segmentation map changes every quantity the model reports -- flux, size, ellipticity, redshift -- by 110-4400 times a matched placebo. The mechanism is detection gating, presence at the field centre (r = 0.47), not the light the mask encloses (r = 0.30); across 322 real blends the model ignores how the pipeline partitioned the light (R = -0.006). Nor is the preference specific to that channel: contradicted catalogue photometry leaves the model nine times worse than supplying no metadata at all. The Legacy Survey pipeline leaves 3.68% of targets with no segment covering their position. Propagating that rate, with a miss represented by the fields the pipeline actually returns, shifts tomographic mean redshifts by a median 0.71 times the LSST DESC requirement over 40 assignments and exceeds it in 12; observed positional errors take the worst bin to 8.3 times. Drawing the misses by their measured magnitude dependence rather than uniformly does not change it. Spectroscopy removes the effect, withholding the detection channel removes it at no measurable cost, and the effect grows with model scale. Two further limits lie in the tokeniser: its image codec resolves 28 effective states on source patches against 934 for the spectrum codec, and the redshift readout is quantisation-limited. Sparse dictionaries are unreliable causal handles: across 15, recovery spans 26-75% and moves up to 18 points on the seed alone.

A knowledge-driven framework for predicting single-cell responses for unprofiled drugs

Wed, 26 Aug 2026 00:00:00 +0000

Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01286-w Feng et al. introduce MAP, an artificial intelligence framework that integrates biological mechanism knowledge to predict how cells respond to chemical perturbation, improving generalization to untested drugs and prioritizing cancer drug candidates in virtual screening.

Enhancing materials discovery with valence-constrained design in generative modeling

Wed, 26 Aug 2026 00:00:00 +0000

Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet many existing approaches overlook essential chemical constraints, such as oxidation-state balance, which can lead to chemically invalid structures. Here we introduce 'crystal generator with valence-constrained design' (CrysVCD), a modular framework that integrates chemical rules directly into the generative process. CrysVCD first uses a transformer-based elemental language model to generate valence-balanced compositions, followed by a diffusion model to generate crystal structures. The valence constraint enables orders-of-magnitude more efficient chemical valence checking compared with pure data-driven approaches with post-screening. When fine-tuned on stability metrics, CrysVCD achieves 85% metastability (Ehull < 0.1 eV per atom) and 68% phonon stability. Moreover, CrysVCD supports conditional generation of functional materials, enabling discovery of candidates such as high thermal conductivity semiconductors and high dielectric constant (high-κ) materials. Designed as a general-purpose plugin, CrysVCD can be integrated into diverse generative pipelines to promote chemical validity, offering a reliable, scientifically grounded path for materials discovery.

Biomolecular Multiscale Simulation (BMS25) Dataset to Train Neural Network Potentials for QM/MM Settings with Electrostatic Embedding

Wed, 26 Aug 2026 00:00:00 +0000

Abstract Neural network potentials (NNPs) can provide insight into biological processes at atomic resolution. Training these NNPs requires large and diverse datasets of molecules, conformations, and configurations. However, so far little attention has been paid to the description of solvation, despite its importance for biomolecular systems. This work lays the foundation for NNPs where solvation is an integral part of the model. Following a quantum-mechanics/molecular-mechanics (QM/MM) formalism with an electrostatic embedding scheme, systems are decomposed into a QM zone with the solute(s), which is electrostatically coupled to the point charges from surrounding solvent molecules (MM zone). Using an accelerated sampling approach, we generate the biomolecular multiscale simulation (BMS25) dataset with over 50,000 topologies and more than 1.5 million unique conformations of peptides and miniproteins as well as small molecules and transition states from chemical reactions. The dataset includes energies, gradients, and multipoles of solute molecules as well as gradients on solvent molecules at the ω B97M-D4/ma-def2-TZVPP level of theory, enabling the development of multiscale NNPs for simulating large biomolecular systems.

Ultrafast and reference-free sequence discovery in single-cell data

Wed, 26 Aug 2026 00:00:00 +0000

Abstract Knowledge of RNA sequences, expression, splicing, isoforms, structure and modifications is central for understanding and targeting cellular processes. Revolutionary single-cell and spatial transcriptomics technologies—for example, as deployed by consortia such as the Human Cell Atlas—partially capture this diversity and generate cellular profiles that expand at petabyte scale each year 1–5 . Yet researchers cannot search sequences across these datasets: standard pipelines do not scale or rely on references, retaining only gene or isoform counts, whereas accessing raw sequences requires collecting, downloading and processing millions of large files. Here we present Malva, a computational platform that enables ultrafast, species-agnostic and reference-free interrogation of the raw sequence space, enabling searching for any sequence, mutation, splice junction or pathogen, or spatial location of arbitrary transcripts. The continuously expanding Malva Index currently comprises around 74 million cells from thousands of experiments in health and disease. Malva enables reference-free discovery—researchers can, for example, identify cell types and predict cell–cell similarity directly from sequence composition. Building on Malva’s speed and accuracy, we demonstrate how Malva can be flexibly connected to state-of-the-art neural networks and how to execute complex searches and enable automated analyses. Malva transforms single-cell atlases from static gene count tables into dynamic, sequence-resolved resources that may help to bridge human–machine reasoning about biology.

A Critical Review of Three Levers Governing Collapse in On-Policy Self-Distillation

Wed, 26 Aug 2026 00:00:00 +0000

On-policy distillation trains a language model on its own generations while a teacher scores them token by token. It combines the dense supervision of imitation learning with the on-policy sampling of reinforcement learning. But it requires a second, larger model to act as teacher. On-Policy Self-Distillation (OPSD) removes that cost. The teacher is the model itself, conditioned on privileged information the student will not have at test time, such as a reference solution, a plan, or environment feedback. The teacher is no stronger than the student, only better informed. Early results were promising, with accuracy comparable to reinforcement learning at a fraction of the generated tokens. But the same asymmetry that produces the signal also biases it. One failure mode now dominates the field: collapse, the progressive narrowing of the set of reasoning paths the model can produce. Collapse is not specific to OPSD, though privileged information aggravates it. This review treats collapse as a symptom governed by three levers: (i) where the signal is applied, that is, how tokens are weighted; (ii) what the teacher is shown, that is, the nature of the privileged information; and (iii) when the signal changes, that is, the teacher's dynamics and the decay of guidance. We restrict our scope to mathematical reasoning, where the method originated and where its failure modes are best documented. We report no new experiments. The contribution is structural: a shared vocabulary for phenomena named differently across papers, and a clear line between what is settled and what is still disputed.

Longitudinal alignments and syntheses of multimodal clinical data for personalized medicine with the PULSE framework

Wed, 26 Aug 2026 00:00:00 +0000

Nature Computational Science, Published online: 26 August 2026; doi:10.1038/s43588-026-01026-5 PULSE is an AI framework that uses patients’ longitudinal electronic health records to accurately generate within- and cross-modal data from routine clinical laboratory tests, enabling cost-effective precision medicine across diverse biomedical settings.

A patient’s longitudinal history reconstructs their unmeasured molecular profile

Wed, 26 Aug 2026 00:00:00 +0000

Nature Computational Science, Published online: 26 August 2026; doi:10.1038/s43588-026-01024-7 Most patients leave the clinic with only a few of the clinical measurements needed to tell the full story of their health. PULSE is a new artificial intelligence (AI) framework that fills in the blanks, using a patient’s own history to predict what was never directly measured.

AGENTEX automates prototyping of genetic codes

Wed, 26 Aug 2026 00:00:00 +0000

The standard genetic code uses 64 codons to encode 20 canonical amino acids across domains of life. New-to-nature genetic codes enable new chemistries, therapeutics and ecosystem engineering, but recoding the genome of an organism is exceptionally challenging 1-6 . Here we describe automated genetic tRNA expansion (AGENTEX) for multiplexed robotic prototyping of genetic codes in cell-free translation systems. Two Watson-Crick interactions in the ribosomal large subunit (LSU) mediate recognition of the 3' CCA end of tRNAs, preventing tRNAs with alternative 3' sequences from being accommodated during translation 7,8 . Building on these interactions, we investigated the extent to which non-CCA-3' tRNAs (otRNAs) would be aminoacylated by natural aminoacyl tRNA synthetases (aaRSs), allowing pools of otRNAs to specify unique genetic codes using ribosomes with altered LSU. We developed multiplexed and automated methods to read aminoacylation in libraries of synthetic tRNAs. We discovered that the tRNA 3' end shows remarkable flexibility to mutation, allowing aminoacylation of most otRNAs by all Escherichia coli aaRSs. Building on our discovery, we developed cell-free translation systems enabling compressed genetic codes of 34 aaRSs for 34 codons. Using AGENTEX, we evaluated two genetic codes alongside the standard genetic code, with non-standard amino acid incorporation and reassignment of up to three codons. Our findings have implications for the design of radically new translation systems, the synthesis of biopolymers with several instances of non-standard monomers, and understanding of possible past and future genetic codes.

Next AI Breakthrough Could Come From Physics, Says Max Welling

Tue, 25 Aug 2026 21:09:02 +0000

Next AI Breakthrough Could Come From Physics, Says Max Welling Max Welling, co-founder of CuspAI, discusses how physics principles could unlock the next AI breakthrough, accelerating material discovery and informing AI architectures. 5 min read Visual TL;DR Max Welling proposes physics principles as the next significant leap for AI From the article 8 mentionsThe conventional wisdom in artificial intelligence research suggests that future breakthroughs will stem from increased computational power, larger datasets, and more extensive models. CuspAI uses generative AI to design novel materials for critical sectors From the articleWelling begins by highlighting the work of CuspAI, a company that utilizes generative AI to design novel materials. physical laws like symmetry and conservation can inform AI architectures From the article 7 mentionsHe argues that the next significant leap in AI may be found within the principles of physics. physics-informed AI will accelerate scientific discovery and innovation significantly Max Welling proposes physics principles as the next significant leap for AI From the article 8 mentionsThe conventional wisdom in artificial intelligence research suggests that future breakthroughs will stem from increased computational power, larger datasets, and more extensive models. CuspAI uses generative AI to design novel materials for critical sectors From the articleWelling begins by highlighting the work of CuspAI, a company that utilizes generative AI to design novel materials. physical laws like symmetry and conservation can inform AI architectures From the article 7 mentionsHe argues that the next significant leap in AI may be found within the principles of physics. foundation models for chemistry, agentic workflows accelerate material discovery From the article 4 mentionsThis approach is fundamentally reshaping the scientific discovery process. AI helps physics, physics helps AI, creating a synergistic relationship understanding AI through physics concepts like energy landscapes and phase transitions From the article 4 mentionsWelling touches upon the internal dynamics of neural networks, drawing parallels to physical phenomena. physics-informed AI will accelerate scientific discovery and innovation significantly Contents(5) © 2026 StartupHub.ai. All rights reserved. Do not enter, scrape, copy, reproduce, or republish this article in…

The geometry of AI validation: Exact certification limits for iid best-of-N search

Tue, 25 Aug 2026 04:00:00 +0000

AI systems increasingly generate alternatives, inspect evidence, and deploy a selected output. Validation is therefore target-relative: evidence certifies deployment only in directions resolved by the interventions that produced it. We represent validation and deployment rules as kernels over a reliability surface. Their span geometry separates replication, which reduces sampling noise, from new intervention directions, which reduce structural blindness. We make this principle exact for iid best-of-$N$ search. Under scalar ranking, randomized ties, maximum selection, bounded binary truth, and a stable rank-truth relation, knowing best-of-$n$ reliability through $n=m$ leaves exact ambiguity width $B_{m,N}=1+2\sum_{r=1}^{m}(-1)^r\cos^{2N}{r\pi/[2(m+1)]}$. Explicit bounded worlds attain the entire interval, and the complete prefix is information-maximal among reliability-mean audits confined to $n\le m$. The governing scale is $m^2/N$: when $m$ is proportional to $\sqrt{N}$, ambiguity remains about 0.83, while width $\varepsilon$ requires $m$ of order $\sqrt{N\log(1/\varepsilon)}$. Monotonicity gives an exact uniform-approximation frontier; a Lipschitz bound gives an exact capped-tail dual and order-sharp $L/m^2$ ambiguity. These results yield a two-gate audit rule: establish structural coverage, then add independent tasks for precision. Retrospective studies of mathematical reasoning and code selection construct compatible deployment values with wide separation and show that a score-tail audit rule frozen on 82 discovery tasks substantially reduces held-out error. Beyond iid search, the geometry applies only to known or independently estimated kernels; the empirical analyses are illustrative rather than prospective interventions.

First-Principles Atomistic Structure and Dynamics of Polyethylene During High-Pressure Radical Polymerization via Machine Learning Force Fields

Tue, 25 Aug 2026 04:00:00 +0000

Polyethylene (PE) is one of the most commonly used synthetic polymers. While the synthesis and processing protocols for PE are well established, precise experimental assignment of microscopic structures at atomistic resolution (i.e., the position of each atom) remains largely limited to highly crystalline systems. This gap is often addressed via computer simulations using empirical interatomic potentials, which use approximate but efficient descriptions of interatomic interactions to reach the length and time scales needed to describe macromolecules. These empirical potentials typically perform well for bulk and/or collective properties but face challenges with chemical realism for complex systems, e.g., during reactive processes. In this work, we address this challenge by combining the computational efficiency of a deep potential (DP) machine-learning force field and the chemical realism of first-principles van der Waals (vdW) corrected hybrid density functional theory (DFT) enabled by a SeA high-throughput framework. Using this approach, we study the structure and dynamics of PE oligomers and polymers in an ethylene solvent under common high-pressure (supercritical) radical polymerization conditions. We found that the local solvation environment of radical-containing PE oligomers converges for chain lengths greater than (n~6), suggesting extensibility of our oligomer-trained MLFF to significantly longer polymers. We then confirmed the extensibility of these models to long PE chains by characterizing the molecular weight scaling of single-chain structure and dynamics, which showed classic good solvent behavior. Our PE MLFF retained a consistent level of fidelity and stability across a wide range of thermodynamic state points and chain lengths, at full atomistic resolution, therefore paving the way towards first-principles-based polymer structure and property prediction.

Radio Galaxies detection and characterization using deep learning techniques

Tue, 25 Aug 2026 04:00:00 +0000

Future radio telescopes will generate data volumes that are increasingly difficult to analyse using traditional statistical methods, motivating the adoption of machine-learning techniques. In this work, we present YOLO-Chars (YOLO-based Detection and Characterisation of Radio Sources), a two-stage deep-learning framework for the automated detection and characterisation of radio galaxies in survey images. The framework is developed and evaluated using the Square Kilometre Array Science Data Challenge 1 (SKA SDC1) dataset. In the first stage, customised YOLO-based multi-scale detection models are used to localise compact and extended sources across large sky maps. In the second stage, a dedicated source-characterisation network estimates the physical properties of the detected sources. We focus on three key parameters: flux density, angular size, and position angle. Our results show that YOLO-Chars achieves competitive detection and characterisation performance on the SKA SDC1 benchmark, demonstrating its potential as a scalable framework for next-generation radio continuum surveys.

Automated Analysis to Reveal Grain Boundary Phase Microstructures

Tue, 25 Aug 2026 04:00:00 +0000

We develop a method for analyzing grain boundary (GB) microstructures that identifies distinct interfacial phases and the dislocation line defects separating them. Similar to bulk materials, GBs can adopt multiple distinct interfacial phases and undergo first-order phase transitions. When these phases coexist, their spatial arrangement and phase junctions constitute a GB microstructure, characterized by variations in excess properties and line defects with associated dislocation content. Despite this intrinsic heterogeneity, our ability to quantitatively characterize GB microstructures remains limited, as it requires identification of individual GB phases, phase-resolved excess properties, and the Burgers content of phase junctions, capabilities not available in existing automated methods. Here, we present an automated tool that performs interfacial microstructure mapping for planar coincidence site lattice GBs. The method identifies the spatial distribution of GB phases, quantifies phase-specific excess properties, and estimates the Burgers content of GB phase junctions. We demonstrate the approach using three representative cases: (i) quantification of mass transport during diffusion-limited GB phase transformations; (ii) identification of structurally indistinguishable phases formed by vacancy and interstitial loops; and (iii) characterization of GB microstructures containing phase nuclei. More broadly, this framework enables quantitative studies of GB evolution processes, including spinodal decomposition and coarsening with direct implications for GB deformation, creep, and migration.

Runtime Action Interference for AI Control of AlphaStar in StarCraft II

Tue, 25 Aug 2026 04:00:00 +0000

A trained reinforcement learning policy does not determine the complete behavior that users encounter: deployment code still schedules, admits, suppresses, or replaces its proposed actions. We contribute \emph{runtime action interference} (RAI), an AI control mechanism that preserves policy parameters while regulating action pacing and filtering configured action patterns after inference. RAI releases a proposed action only when its cooldown condition is satisfied and its content detector does not flag the action; otherwise, it dispatches a no-op. The detector covers specified toxic behaviors, including worker-unit harassment, while the cooldown controls action rate. We implement RAI in a replication of AlphaStar actor.py and make the implementation and reproducibility materials available through an open source code repository. We deployed RAI in a \textit{StarCraft~II} human participant study that compared two presentations of the same opponent with high capability and rate limited actions; we withheld its capability claim in one presentation and disclosed it in the other. On response scales from 1 to 5, we observed pooled fairness, trust, and toxicity means of 3.90, 3.50, and 2.00 under claim withholding, compared with 2.62, 4.31, and 2.85 under disclosure. Disclosure corresponded with lower perceived fairness and higher perceived toxicity across every expertise group, whereas trust increased among novices and experts but decreased among intermediate participants. Our human evaluation therefore shows that perceptions of an opponent controlled through RAI can vary substantially with the capability information presented to users, even when the configured control remains constant. We conclude that human-computer evaluations must separate control within the execution stack from capability disclosure and assess fairness, trust, and toxicity as distinct dimensions of human experience.

Polymer-Linked Nanoparticle Networks Running on Heat Can Act as Computing Devices

Tue, 25 Aug 2026 04:00:00 +0000

Developing physical neural network (PNN) hardwares is important to next generation artificial intelligence systems. Phononic devices-using heat current to encode and process information-is one of the solutions to neuromorphic computing. In this work, we back map an artificial neural network (ANN) into a PNN simulation model using polymer networked nanoparticles (PNNPs). Our atomistic simulation results demonstrate that the polymer linked nanoparticle networks can potentially realize information processing using heat current. Using high-throughput molecular dynamics (MD) simulations and the trust region Bayesian optimization (TuRBO) methods, we tune the plasticity of polymer linkers and the temperatures of nanoparticles to optimize the performance of the PNNP machines, which is similar to tune the weights and bias in ANNs. After 5 rounds of high-throughput MD simulations, we show that the PNNP machines have improved in performance. We also use a testing data set to verify the heat flow outputs from the top 5 PNNP machines in each round.

ChemDIRT: A Diversified Instruction, Representation, and Task Benchmark for Robust Chemistry-LLM Evaluation

Tue, 25 Aug 2026 04:00:00 +0000

The rapid advancement of large language models (LLMs) has led to increasing interest in their application to scientific domains such as chemistry. However, existing chemistry benchmarks often provide only a narrow view of model capability, focusing on limited task sets while overlooking robustness to variations in problem formulation and chemical representation. As a result, reported performance may overestimate a model's true ability to reason consistently across realistic settings. To address this challenge, we introduce ChemDIRT (Diversified Instruction, Representation, and Task Benchmark), a comprehensive evaluation framework designed to assess the robustness of chemical reasoning in LLMs. ChemDIRT systematically measures model performance across variations in instructions and molecular representations while spanning eight categories of chemistry tasks. By evaluating both accuracy and consistency under these controlled perturbations, ChemDIRT provides a more reliable assessment of model reasoning capabilities than conventional single-format benchmarks. We benchmark a diverse set of open- and closed-source LLMs, revealing substantial prompt sensitivity, representation dependence, and uneven performance across task families.

Precise Modeling of a Complex Solenoidal Magnetic Field Using a Combination of Analytic Functions and a PINN

Tue, 25 Aug 2026 04:00:00 +0000

We demonstrate an iterative approach to modeling a sparsely measured magnetic field in a large-bore solenoid. This approach uses a hybrid of traditional and machine learning techniques. The traditional technique is a linear least-squares fit using a series solution to Laplace's equation, while the machine learning technique involves the training of a physics-informed neural network (PINN) on the least-squares fit residuals. We use a newly defined activation function "DELTAsnake," a modification to the snake activation function proposed by Ziyin et al. that allows for stronger curvature and non-monotonicity. The combined model approximately obeys Maxwell's equations to a level sufficient for producing high quality physics simulations and analysis. Our approach is applied to a highly realistic calculation of the expected magnetic field in the Mu2e experiment's Detector Solenoid which includes a simple model for the expected statistical measurement uncertainties. Using ten toy measurement simulations, we demonstrate the capabilities of our model in comparison to the least-squares method alone; the least-squares method alone results in a reduced chi-squared statistic of ${2.15 \pm 0.01}$, while our approach improves the reduced chi-square to ${1.034 \pm 0.005}$. Furthermore, for an average toy simulation, we show that the range of the RMS of the three field component residuals reduces from ${0.07-0.37}$ Gauss to ${0.05-0.07}$ Gauss. We find that this novel method is robust against a realistic systematic uncertainty deriving from Hall probe calibration bias and can be used to significantly reduce the number of measurements required to achieve an accurate model.

AI-Assisted Extraction of Follow-up Observations from GCN Circulars in Astro-COLIBRI

Tue, 25 Aug 2026 04:00:00 +0000

We present a new Astro-COLIBRI component that converts free-text GCN Circulars into structured, event-linked follow-up records and combines them with structured reports submitted directly by the community. A continuously running Circular listener associates new reports with transient events, applies deterministic pre-analysis, and invokes a schema-constrained large language model extraction step for photometry, contacts, redshifts, and other reported results and metadata. The resulting records are submitted to the Astro-COLIBRI API, normalized into a common event-level follow-up database, and exposed through the web and mobile interfaces as report summaries, contact tools, optical-afterglow context figures, and downloadable CSV or VOTable products. After tuning, all 1,775 Circulars of an operational evaluation corpus covering the first half of 2026 completed the workflow without failures. An internal human audit of 210 Circulars confirmed 25,827 of 25,880 definite field-level decisions (99.80%), with the remaining errors confined to observation timing and facility attribution in eight of 231 assessed reports. Extracted redshifts agree with the independent GRBweb compilation for 228 of the 249 events the two share, and every disagreement traces to a limit, a candidate-host estimate, or a value later refined rather than to a misread Circular. The final pipeline was applied to the full GCN archive since 2016, yielding 68,393 individual observations from 26,811 reports across 5,787 transient events, and is now running in real time on new Circulars. The reusable parsing and normalization pipeline is released as the open-source Python package astro-colibri-circular-parser. This paper describes the scientific motivation, architecture, extraction schema, quality-control safeguards, user-facing products, and current use-cases of the system.

LitReview Arena: Evaluating Literature Review Agents with Battle-Style Peer Review Platform

Tue, 25 Aug 2026 04:00:00 +0000

Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspects of research utility depend on expert judgment rather than reference-overlap metrics. We introduce LitReview Arena, a battle-style evaluation platform with a structured protocol tailored to literature review quality: domain experts with AI paper-writing experience compare anonymized drafts, are matched to topics within their expertise, and provide dimension-wise outcomes over five literature-review-specific criteria. From this protocol, we collect approximately 3k expert judgments, each containing five dimension-wise outcomes, and show that even the strongest current systems win only 23.0% of decisive matches against human drafts on overall utility, while agentic LLMs such as Sonar Deep Research substantially outperform base language models by over 60%. We further find that existing LLM-as-a-judge methods are substantially misaligned with human experts (Spearman's rho=0.467), especially on synthesis-heavy criteria such as paper structure and research suggestions. Using the collected preference data, we provide an expert-calibrated evaluator, LitJudge, which improves alignment to Spearman's rho=0.78, comparable to inter-expert consistency; code and data are publicly available at https://github.com/VanellopeAsher/LitReview-Arena.

LLMs are Few-Shot Decision-Makers: Generalized Context-Aware Microgrid Frequency Control through Prompt Decision Transformer

Tue, 25 Aug 2026 04:00:00 +0000

The rapid evolution of energy structures has positioned microgrids as pivotal components of next-generation power systems, offering enhanced resilience and renewable energy integration. However, the inherent low inertia, complex dynamics, and poor model conditions of microgrids necessitate advanced data-driven frequency control strategies. Although reinforcement learning (RL) has demonstrated certain potential and advantages, existing RL methods often struggle with generalization across diverse microgrid configurations and lack adaptability to unseen environments, particularly when explicit system parameters are unavailable. To address these challenges, in this paper, we introduce a novel prompt decision transformer (Prompt-DT) architecture for microgrid frequency control. Unlike traditional approaches that rely on hard-to-obtain environmental characteristic parameters, the proposed method leverages few-shot expert historical trajectories as prompts to guide autonomous perception and adaptive decision-making. In addition, we propose a context-aware training and execution mechanism utilizing self-supervised contrastive learning to enhance environment recognition and prompt utilization efficiency. In addition, a physics-informed prompt design technique that filters prompts based on cumulative reward and frequency volatility is proposed, ensuring high-quality physical guidance during online execution. Finally, to ensure generalization in unseen environments with limited data, we develop a lightweight finetuning approach that achieves performance comparable to full-parameter finetuning with minimal adjustments.

GenomeHarness: Harnessing AI Agents for Reliable Adaptation of Genome Language Models

Tue, 25 Aug 2026 04:00:00 +0000

Pretrained genome language models provide reusable representations for DNA sequence analysis, but turning them into reliable downstream predictors remains non-trivial. Their practical performance depends strongly on fine-tuning recipes, and default recipes reported in prior studies may be suboptimal for new tasks or model backbones, making weak downstream results difficult to interpret. These requirements place a substantial operational burden on many intended users, whose expertise is often centered on biological questions and interpretation rather than machine-learning engineering. Reliable use of genome language models therefore requires more than conventional AutoML-style tuning: it requires a systematic, budget-aware, and auditable procedure that lowers the barrier to downstream adaptation. We present GenomeHarness, an agentic harness for adapting genome language models through controlled search over fine-tuning recipes. GenomeHarness combines an AI agent for proposing and repairing recipe edits, a harness for protocol-constrained execution, resource management, and test isolation, and a Monte Carlo tree search controller for allocating search effort across recipe lineages. We evaluate GenomeHarness on DNABERT2 and NTv2-100M-Multi across the NT Benchmark and Genomic Benchmarks. Final evaluation is performed using three random seeds after recipe freezing. Across 52 model-task settings, GenomeHarness improves mean test MCC in 47 settings, including 24 of 26 DNABERT2 settings and 23 of 26 NTv2-100M-Multi settings. The gains are especially pronounced on Genomic Benchmarks and on tasks where the root recipe is unstable or poorly matched, such as human ocr ensembl task. Search traces further show that GenomeHarness progressively identifies stronger recipes, turning downstream adaptation into a controlled and auditable workflow rather than a manual tuning process.

PepLLM: ESM-Guided Llama for Structured Protein-Peptide Binding Interface Analysis

Tue, 25 Aug 2026 04:00:00 +0000

Protein-peptide interactions are central to cellular regulation and peptide-based drug discovery, yet existing computational methods mainly focus on interaction classification, binding-site prediction, or peptide binder generation. These formulations provide limited insight into the physicochemical mechanisms that determine how a peptide binds to a protein. In this work, we introduce \textbf{PepLLM}, an instruction-tuned framework for structured protein-peptide interface understanding. Given protein-peptide sequences, PepLLM generates a machine-readable JSON annotation describing multiple interface properties, including peptide burial state, hydrogen-bond density, salt-bridge presence, hotspot residues, hydrophobicity, and electrostatic complementarity. To support this task, we construct a new protein-peptide interface dataset by integrating structural interface analysis, solvent-accessible surface area computation, hydrophobic burial estimation, electrostatic potential calculation, and redundancy-aware data splitting. PepLLM connects a pretrained ESM encoder with a LLaMA decoder through a nonlinear modality adapter. The adapted ESM residue embeddings are injected into the LLaMA prompt as continuous soft tokens via placeholder-token replacement, enabling the decoder to generate structured interface annotations under instruction tuning. By moving beyond single-label prediction toward multi-property and mechanism-aware generation, PepLLM establishes a new task and modeling paradigm for interpretable protein-peptide interface analysis.

Generative Neural Networks for Sinkhorn Distributionally Robust Hypothesis Testing

Tue, 25 Aug 2026 04:00:00 +0000

This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust detector against least-favorable distributions in Sinkhorn discrepancy-based ambiguity sets centered at the empirical distributions. Existing approaches solve this problem by solving large-scale conic programs, which are not scalable. To overcome this, we propose a generative framework that learns least-favorable distributions and supports efficient training and end-to-end sampling. For the Sinkhorn discrepancy-based ambiguity sets, we first derive an equivalent conditional-KL-divergence representation with respect to kernel-smoothed reference distributions. This property allows us to prove strong duality for both constrained and unconstrained minimax SDRHT formulations. Based on the closed-form optimal detector and Brenier's theorem, we reformulate the max-min dual formulation as a maximization problem over convex potentials whose gradients characterize invertible transport maps between kernel-smoothed distributions and their least-favorable counterparts. We efficiently approximate these potentials using Hyper Input Convex Neural Networks (HyCNNs) equipped with stochastic gradient estimators and prove the representation power of HyCNNs and the distributional universality of their induced transport maps. Numerical results show that the proposed method achieves superior accuracy and robustness across different sample sizes and dimensions, while avoiding the scalability limitations of classical SDRHT methods.

MolJam: A Multidimensional Framework for Assessing Molecular Dataset Quality and Its Impact on Machine Learning

Tue, 25 Aug 2026 00:00:00 +0000

High-quality molecular datasets are essential for reliable machine learning in cheminformatics and bioinformatics, yet dataset quality is rarely assessed systematically and its relationship with downstream model performance remains poorly understood. Here, we present MolJam, an open-source framework for quantitative assessment of molecular dataset quality across five dimensions-structural integrity, data quality, experimental information quality, chemical space coverage, and data distribution-using 12 standardized metrics. Application of MolJam to 11 MoleculeNet and eight ChEMBL-derived datasets revealed widespread and heterogeneous quality issues, including undefined stereochemistry in up to 70.72% of molecules, inconsistent molecular representations, and contradictory labels. We next asked whether improving these quality metrics necessarily improves machine learning performance. Refinement of the ESOL and Lipophilicity datasets increased their MolJam quality scores but produced mixed effects on predictive performance, suggesting a competing influence of reduced dataset size. Controlled ablation experiments further demonstrated that both dataset quality and data quantity contribute to model performance and, notably, that retaining molecules with incomplete stereochemical information can outperform their removal when the resulting gain in data quantity offsets the quality penalty. Thus, molecular dataset curation cannot be reduced to maximizing data cleanliness alone but requires balancing multiple dimensions of data quality against information loss. MolJam provides a standardized framework for diagnosing molecular dataset limitations, comparing benchmark quality, and quantitatively evaluating how data curation decisions influence downstream machine learning.

PhageLysData: an evidence-aware and AI-ready dataset of phage lytic enzymes and depolymerases

Tue, 25 Aug 2026 00:00:00 +0000

Bacteriophage lytic enzymes and depolymerases are relevant to phage biology, antimicrobial development, and protein engineering, but their sequence and annotation data remain dispersed across general databases, specialized resources, genome-centred collections, and prediction-oriented datasets. We present PhageLysData, an evidence-aware and AI-ready resource constructed through reproducible multisource integration, provenance tracking, and exact-sequence consolidation. The release integrates 807,366 source observations from seven primary resources into 759,105 unique exact-sequence entities, comprising an evidence-supported Core of 11,867 entities, a Prediction Extension of 745,092 prediction-only candidates, and 2,146 Context entities retained for provenance and reference. This architecture preserves broad sequence-space coverage while maintaining a clear distinction between non-predictive and prediction-derived support. Core entities are enriched with harmonized biological annotations, physicochemical properties, independent InterProScan-derived functional annotations, mapped PDB and AlphaFold DB structural assets, and reusable numerical representations. For 11,259 eligible Core sequences, PhageLysData provides embeddings from 11 protein language models together with one-hot encoding under a common representation contract. Release-facing examples demonstrate latent-space exploration, unsupervised clustering, supervised classification, and evidence-aware candidate retrieval without defining a universal predictive benchmark. PhageLysData provides a traceable, versioned, and computationally accessible foundation for protein retrieval, comparative analysis, task-specific dataset construction, and machine-learning applications involving phage lytic enzymes and depolymerases.

Prot-LAMBDA: Explicit Distance Learning Enhances Structural Reasoning in Protein Language Models

Tue, 25 Aug 2026 00:00:00 +0000

Protein language models (PLMs) learn evolutionary information from large-scale sequence data, but three-dimensional relationships are encoded only implicitly. Here, we introduce Prot-LAMBDA (Protein LAnguage Model Boosted with Distance Awareness), a PLM that explicitly incorporates spatial relationships by coupling residue embeddings with inter-residue contacts. Prot-LAMBDA improves performance across diverse structure-related tasks, including contact, secondary structure, backbone geometry, solvent accessibility, and protein fold prediction. Notably, it achieves a twofold improvement in long-range contact recall and an 11.7% reduction in {psi}-angle prediction error relative to ESM2-3B. Despite having approximately fivefold fewer parameters, Prot-LAMBDA also improves 3D structure prediction over ESM2-3B by 5-7% in TM-score when coupled to the same structure-prediction module. Building on these representations, we developed LambdaFold, a lightweight distance-guided structure prediction framework that achieves performance comparable to ESMFold on proteins strictly non-redundant to the training data. Finally, retrieval-augmented integration of structural templates increases mean TM-score substantially for targets with high template coverage and rescues several incorrect folds. Together, these results demonstrate that explicit spatial constraints enable efficient and generalizable structural representation learning and protein structure prediction.

An Imaging-Informed Reaction-Diffusion Model of Infarct Growth

Mon, 24 Aug 2026 04:00:00 +0000

Predicting final ischemic infarct volumes from acute imaging is a cornerstone of personalized stroke management, yet current strategies remain polarized between uninterpretable machine learning architectures and overly detailed electrophysiological models that are intractable in clinical imaging settings. We bridge this clinical gap by introducing the first imaging-driven framework that parameterizes a Fisher-KPP reaction-diffusion partial differential equation (PDE) directly from clinical acute MRI. The continuous state variable $u(\mathbf{x},t)\in[0,1]$ models tissue damage, capturing the forward expansion of ionic stress through the extracellular space alongside a localized metabolic commitment to cell death gated within the baseline perfusion deficit ($T_{\max}>6$\,s). Evaluating this paradigm on a subset of the ISLES 2017 dataset ($N=29$) via an oracle framework reveals that incorporating biophysical propagation constraints yields a mean Dice score of $0.46 \pm 0.24$, compared to standard rCBF thresholding ($0.25 \pm 0.21$). Extensive ablations demonstrate that modeling spatially heterogeneous diffusion fields derived from clinical perfusion maps accurately captures penumbral expansion, while providing full physiological interpretability. This proof-of-concept establishes that first-principles physics could capture complex ischemic progression directly on clinical scan grids, shifting the paradigm from purely data-driven models toward patient-specific biophysical forecasting.

A Survey on Foundations and Frontiers of Multimodal Agentic Frameworks: Techniques and Applications

Mon, 24 Aug 2026 04:00:00 +0000

Advances in large language models (LLMs) have fueled a wave of research into agency: the ability to reason, plan, and act. This effort has produced agentic frameworks that orchestrate perception, memory, and decision-making around powerful LLM backbones. With the advent of large multimodal models (LMMs), these systems can process and integrate diverse modalities, including images, audio, and video, thereby improving their real-world applicability. Yet, while surveys of LLM-based agents exist, the role of multimodality in shaping agency has not been systematically examined in recent years. This survey fills the gap by analyzing the impact of multimodality across the core functional modules of the agentic framework: perception, reasoning, planning, memory, and action. Using this lens, we trace the evolution from text-centric agents to multimodal frameworks, examine how modalities are integrated through delegated, late-fusion, and early-fusion architectures, and assess the emergence of agentic behaviors enabled by grounded perception and multimodal reasoning. We organize existing work through a modality-centric taxonomy that links architectural design choices to agent capabilities. Moreover, we review multimodal agentic systems across various application domains, including Robotics, GUI & Web Navigation, Multimedia Content Generation & Editing, and Long-form Video Understanding & Retrieval. Beyond capabilities, we analyze performance across these settings and discuss efficiency-scalability trade-offs, including training and inference costs, latency, and deployment constraints. By focusing on the impact of multimodality in agentic design, we aim to identify key gaps and chart a roadmap toward robust and general-purpose intelligent systems.

Machine learning predictions of the Hessian matrix for peptides chains and small proteins

Mon, 24 Aug 2026 04:00:00 +0000

Molecular Hessians have a key role in describing molecular vibrations, trajectories and optimization paths. An explicit calculation of them through standard quantum mechanical methods can be computationally expensive for medium-large systems, and in many applications not even needed. Machine learning methods can be a shortcut to tackle efficiently the computational difficulties. This paper will present a ML model able to predict the Hessian matrix of biological system made of thousands of atoms. The method, based on learning the Hessian in internal coordinates is intrinsically invariant to molecular rotations and translations, and has a very good scaling with the systems' size. The training was performed on a dataset of simple aminoacids, as they constitute the building blocks of larger proteins. From the predicted Hessian matrix it is possible to calculate thermochemical properties within the harmonic approximations, among them enthalpies, entropies, Gibbs' free energies, and zero point vibrational energies.

ResiliFlow: An Open Transport World Model for Infrastructure Perception and Disaster Resilience

Mon, 24 Aug 2026 04:00:00 +0000

Transport resilience work is often split across separate data preparation scripts, network models, simulation tools, image inspection systems and reports. This fragmentation makes it difficult to move from an observation to a tested and reviewable decision. We introduce ResiliFlow, an open transport world model concept and an implemented platform for infrastructure resilience, response and recovery. The platform connects two workspaces. Disaster Transport Resilience Analysis provides six map-centred functions for critical-road and critical-area identification, recovery prioritisation, disruption routing, resilience testing and scenario simulation. AI-based Transport Infrastructure Perception and Decision Support organises street-level and satellite evidence, detects visible road, footpath and kerb conditions, and prepares these observations for human-reviewed intervention planning. Both workspaces share an eight-step cycle of perception, prediction, model development, verification, execution, decision, feedback and memory. Research Validation records assumptions and checks, while a local Assistant and an optional multi-provider large language model Copilot translate user questions into bounded calls to executable tools. We document the platform architecture, representative mathematical models, interface evidence and computer-vision learning results. Examples show accurate recognition across eight visible-condition classes, while compact error analysis demonstrates how difficult cases guide continued learning. ResiliFlow shows how transport models can become an inspectable, reusable and question-led system rather than a collection of disconnected analyses. The accompanying release is intended to support research collaboration, public scrutiny and extension under institutional review.

Techno-Economic Analysis of Repurposing Abandoned Oil Wells for Geothermal Energy Extraction Using Physics-Informed Neural Networks

Mon, 24 Aug 2026 04:00:00 +0000

To achieve net-zero targets by 2050, it is critical to diversify renewable energy. Hydropower, wind, and solar energy dominate; geothermal energy remains underutilized. Conventional Enhanced Geothermal Systems (EGS) rely on hydraulic stimulation, which poses risks such as induced seismicity. To address this, Closed-Loop Geothermal Systems (CLGS) circulate working fluids in sealed tubing to avoid direct reservoir contact, making them a potential solution for repurposing idle oil wells without environmental hazards. This study developed a Physics-Informed Neural Network (PINN) to model the CLGS performance. Unlike traditional neural networks, PINN explicitly embeds governing physical equations into their learning processes, such as heat conduction and convection. This integration enabled the model to accurately predict the wellbore temperature and flow characteristics over a 25-year lifespan, even with sparse training data. The simulation results confirmed stable long-term predictions. When coupled with an Organic Rankine Cycle (ORC) model, the system yielded a thermodynamic efficiency of 9.5%. Crucially, several economic indicators (e.g., DPP, NPV, and LCOE) are conducted to evaluate the investment feasibility and economic potential of the proposed CLGS-based power generation system. This proposed framework provides a scalable, physics-consistent tool for evaluating both technical performance and economic returns, offering a robust pathway to accelerate geothermal adoption.

Wrong-Physics Backdoors in Neural PDE Operators

Mon, 24 Aug 2026 04:00:00 +0000

Neural PDE operators are increasingly trained on reusable solver archives, yet validation often relies on clean prediction error and parameter-agnostic plausibility checks. We introduce cross-parameter relinking, a data-poisoning primitive that makes a triggered input select a valid solution from the same PDE family under an incorrect physical parameter. We term this a wrong-physics backdoor: the output remains physically plausible but is wrong for the intended parameter. The attack exploits tensor-to-parameter provenance failures in multi-parameter archives by stamping the surrogate input and relinking its supervision to a cached alternate-parameter solution for the same latent sample. Across 476 attack campaigns, we evaluate Burgers, advection-diffusion, two-dimensional Navier-Stokes, and an elliptic Poisson case. Fourier Neural Operators and DeepONet provide the primary evidence, with Transformer, GRU, and LSTM models as support. FNO reaches a backdoor success rate of 1.0000 on both advection-diffusion and two-dimensional Navier-Stokes while retaining low clean relative L2 error. Clean-label, label-only, and shuffled controls show that high attack success alone is insufficient: successful attacks must move predictions toward the intended alternate-physics target while preserving bounded clean error. These results expose a structural validation gap: smoothness or generic solver-like behavior is insufficient unless the provenance of the intended physical parameter is also verified.

Decision Tree and K-Means Analysis of Raman Spectra for Edible Oils: A Physics-Informed AI Approach

Mon, 24 Aug 2026 04:00:00 +0000

Authentication of edible oils in processed foods is important for food quality, fraud prevention, and regulatory compliance. This study establishes an integrated Raman spectroscopy and machine-learning framework that links intrinsic spectral organization, interpretable classification, and Physics-Informed Artificial Intelligence (PI-AI). Five edible oils were investigated in pure form and within a fried-potato-chip matrix using t-SNE, K-means clustering, Decision Trees, and Non-Negative Least Squares (NNLS)-based spectral decomposition. Unsupervised analyses revealed substantially stronger class organization and separability in pure oils, whereas food-matrix effects introduced pronounced spectral overlap. Decision Trees achieved 100% classification accuracy for pure oils using only four Raman variables from the original 1866-feature spectral space. These four variables, consistently identified by both pre-pruned and post-pruned models, represented only approximately 0.21% of the available spectral information while retaining perfect test-set performance. For matrix-containing samples, NNLS-based PI-AI spectral decomposition substantially improved classification by separating oil-related signatures from paper and potato contributions. Optimized post-pruned models achieved accuracies of 86.4% and 85.4% for paper-subtracted and paper-plus-potato-subtracted datasets, respectively, while reducing the number of important Raman variables to only five and four. The compact four-feature representation further reduced the data footprint by 99.44% without loss of classification accuracy. Collectively, these findings demonstrate that accurate Raman-based oil identification can be achieved through physically meaningful, highly compact, and interpretable spectral representations, providing a promising foundation for Frugal AI, Edge AI, portable sensing, and embedded food-quality monitoring.

An LLM agent for end-to-end computational materials discovery

Mon, 24 Aug 2026 04:00:00 +0000

The coordination of multi-scale tasks is an effective strategy for computational materials discovery, yet the repeated application of diverse algorithms and tools renders it challenging. We report MAESTRO, a large language model (LLM) agent system capable of executing the entire screening pipeline for metal-organic frameworks (MOFs). It processes a large body of MOF literature, links relevant publications to their crystal structures, and curates the results into a computation-ready database, which is then screened through a strategy of progressively increasing computational cost. The promising candidates identified for separation under wet flue gas conditions all originate from unrelated studies. By connecting the heterogeneous stages of computational materials discovery, the LLM-based agents of MAESTRO can operate across application domains and uncover high-performance materials that conventional screening approaches would be unlikely to consider.

Machine Learning Guided Discovery of Corundum High Entropy Oxides

Mon, 24 Aug 2026 04:00:00 +0000

Early thinking in the field of high entropy oxides (HEOs) emphasized their likely abundance, with combinatorial arguments hinting at a myriad of new materials. The experimental reality has proven more challenging: the stability of HEOs cannot be straightforwardly predicted based on ionic radii, lattice geometry, and charge-balancing considerations alone. In this work, we employ machine learning interatomic potentials (MLIPs) to predict the synthesizability of HEOs of the form $A_2$O$_3$ derived from a selection of trivalent cations. From nearly 500 possible compositions, we identify 16 promising candidates for experimental validation with solid-state and combustion synthesis. We discover three new HEOs in the corundum structure, including (Al,Cr,Fe,Rh,Sc)$_2$O$_3$, and one novel cation-ordered phase, (Al,Fe,Ga,Sc)$_2$O$_3$. By far the most common synthesis outcome was a mixture of competing phases, sometimes involving redox reactions. Our results also reveal profound synthesis method dependence for the final product, where qualitatively equivalent outcomes between the two synthesis methods were only observed for 3 of the 16 tested compositions. We conclude that the occurrence rate of HEOs is far rarer than initially believed and that machine learning approaches can effectively guide us to the "needle in the haystack".

Resolving Positive Semi-Definiteness in Physics-Informed Kernels for Scientific Machine Learning

Mon, 24 Aug 2026 04:00:00 +0000

Many modern machine learning models can be understood as kernel-based function-space models, including Gaussian processes and neural tangent kernels. In scientific machine learning, differential operators are increasingly used to encode physical structure directly into such models. However, it has remained unclear under which conditions these constructions are valid machine learning models, i.e. preserve positive semi-definiteness, and whether observed instabilities arise from ill-posed modeling or numerical effects. Here, we establish a simple and sufficient condition for positive semi-definiteness: for linear differential operators of order m, the base kernel must be m-times continuously differentiable. Crucially, this guarantee holds for operators with non-constant and even discontinuous coefficients. Examples are ubiquitous in physical systems, including diffusion, material elasticity, wave propagation in inhomogeneous media, and quantum systems. We conclude that remaining instabilities are attributable to numerical issues, providing a unifying validity foundation for operator-informed kernel methods.

BioFirewall: A genome-writing-native governance layer for design-stage biosecurity screening of agentic AI

Mon, 24 Aug 2026 04:00:00 +0000

Background. Artificial-intelligence design tools now plan genome-scale edits, and agentic systems execute those plans with progressively less human oversight. Biosecurity controls are limited to two points: refusal guardrails at the foundation model and sequence-identity screening at the synthesiser. The design stage between them, where the plan is specified, remains governed by recommendations rather than any deployed system. Results. We present BioFirewall, a rule-governed middleware that intercepts a genome-writing plan and returns allow, flag-for-review, or refuse across five hazard axes native to genome writing: cargo, locus, edit type, germline and scale, with cited evidence, a signed design passport, a tamper-evident audit log, and tiered access. On a de-circularised benchmark of safe proxies scored against independent oracles, a function-aware cargo classifier reached a true-positive rate of 0.72 (95% CI 0.43 to 0.89) at a 1% false-positive rate, whereas frontier and open language-model judges did not screen the same sequences reliably. Under prompt injection, the open-weight judges flipped their blocking verdict to allow in 3 and 5 of 6 trials per channel, while the deterministic screen remained invariant. None of 288 legitimate plans from three templates was refused, yielding a certified 95% upper bound of 0.0103 on the false-refuse rate, and a session monitor intercepted cross-call decomposition attacks. On a held-out gene set, the locus axis was enriched for drivers of in vivo insertional oncogenesis (AUROC 0.605; odds ratio 3.34). Conclusions. Design-stage governance is achievable in practice. BioFirewall is released as open source with a pre-registered, open-data-reproducible benchmark.

A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Baselines

Mon, 24 Aug 2026 04:00:00 +0000

Graph neural networks (GNNs) are routinely employed for short-range forecasting on multivariate time series with a spatial graph structure. Despite the availability of many alternative datasets, method innovations within this domain are predominantly assessed against a rather limited set of benchmark datasets, most notably Chickenpox, PedalMe, WikiMaths, METR-LA, and PEMS-BAY. The evaluation protocols contain baselines spanning from historical averages to classical machine learning approaches. These baselines often show competitive performance compared to GNNs. In the present work, we take a step back and analyse the benchmark datasets via classical time series methods to uncover why spatially-unaware linear models pose a stronger competitor than previously reported, casting further doubt on the discriminative reliability of the aforementioned widely adopted datasets. Our statistical analysis provides a toolset for identifying significant spatial and temporal correlations, while revealing a structural bias introduced by first-order differenced datasets. We therefore recommend reducing the over-reliance on such datasets for method comparison, and instead advocate for more rigorous statistical evaluation. By applying the results of our analysis to a simple hybrid model, we show how our methodology can lead to novel ways of developing GNN models

TracingFlow: A Simulation-Free Trajectory Inference Framework Based on Second-Order Dynamics

Mon, 24 Aug 2026 04:00:00 +0000

Inferring continuous system evolution from sparse temporal snapshots is a key challenge in generative modeling and single-cell omics. While Optimal Transport (OT) is popular, existing frameworks are largely restricted to first-order dynamics, assuming memoryless velocity fields. This limits expressiveness, as first-order systems fail to account for regulatory momentum and time-delayed responses inherent in processes like cell differentiation. Here, we introduce TracingFlow, a simulation-free Flow Matching framework generalizing to second-order dynamics. By using neural networks to regress the acceleration field, TracingFlow provides an exact, efficient solution to the Dynamical Optimal Acceleration Transport (DOAT) problem. Unlike first-order methods yielding over-smoothed trajectories, our second-order formulation captures high-curvature transitions and nonlinear evolutions by learning the underlying force fields. Evaluated on complex synthetic and large-scale scRNA-seq datasets, TracingFlow achieves superior accuracy in distributional reconstruction and trajectory faithfulness. Moreover, by integrating lineage tracing priors, it recovers dynamical structures that are both mathematically optimal and biologically plausible.

Advanced Linear Algebra with Applications - Part I (Numerical linear algebra for PDEs, machine learning, and data assimilation)

Mon, 24 Aug 2026 04:00:00 +0000

These lecture notes form the first part of a master's-level course on advanced numerical linear algebra. Their aim is not only to present the classical algorithms, but to show why the subject has become considerably more central than it was a generation ago. Numerical linear algebra grew up alongside the numerical solution of partial differential equations, and for a long time that is where its large sparse systems came from. Ranking the nodes of a network, assimilating observations into a weather forecast, and fitting a model to a large noisy data set now lead to problems of the same kind: too large to factorise, structured, and accessible only through matrix-vector products. Strikingly few ideas are needed for all of them. Each chapter therefore develops a standard topic and then puts it to work outside its original setting. We treat norms, factorisations, conditioning and floating-point arithmetic; sparse matrices arising from finite differences, from graphs and from machine learning; stationary iterations and the smoothing property; the conjugate gradient and Lanczos methods, with spectral clustering and regularisation by early stopping; Arnoldi and GMRES, with PageRank and large least squares; and finally preconditioning, Schwarz domain decomposition and multigrid. We assume a first course in linear algebra. Every section closes with a summary of what should be retained and every chapter with exercises, several drawn from past examinations. Accompanying Python code reproduces the numerical illustrations.

Self-rankings as a predictor of scientific impact beyond peer review

Mon, 24 Aug 2026 00:00:00 +0000

Abstract Peer review in academic research aims to ensure factual correctness and identify work of high scientific potential, but rapid submission growth has made this increasingly difficult. Here we investigate an additional measure for identifying high-impact research: authors’ rankings of their own submissions to the same artificial intelligence (AI) conference. Grounded in game-theoretic reasoning, we hypothesize that self-rankings are informative because authors understand their work’s conceptual depth and long-term promise. We tested this hypothesis in a large-scale experiment at a leading AI conference. Over more than a year, papers ranked highest by their authors received twice as many citations as their lowest-ranked counterparts, and self-rankings were especially effective at identifying highly cited papers. Self-rankings also outperformed peer-review scores in predicting future citation counts. These findings show that authors’ self-rankings can complement peer review when identifying high-impact AI research.

Harnessing implicit neural representations for scientific data compression

Mon, 24 Aug 2026 00:00:00 +0000

Nature Machine Intelligence, Published online: 24 August 2026; doi:10.1038/s42256-026-01290-0 Implicit neural representations can turn massive scientific measurements into compact functions. A hierarchical approach now helps these representations preserve richer fine-scale detail.

Nature Biotechnology

Mon, 24 Aug 2026 00:00:00 +0000

Nature Biotechnology, Published online: 24 August 2026; doi:10.1038/s41587-026-03272-4 Computational protein evolution with sparse data generates efficient small RNA-guided nucleases.

Multi-resolution enhancement for full-spectrum neural representations

Mon, 24 Aug 2026 00:00:00 +0000

Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-based representations increasingly intractable. Implicit neural representations (INRs) offer a promising solution by encoding signals through coordinate-based neural networks, serving as surrogates of data, with computational and storage requirements scaling with network complexity rather than data dimensionality. However, smaller INRs struggle to faithfully represent multiscale structures, high-frequency information and fine textures that constitute a large proportion of scientific measurements. We propose WIEN-INR, a theoretically guided hierarchical INR framework that distributes modelling across resolution scales and enables improved representation capacity through a novel enhancement network to recover subtle details. This multiscale architecture allows smaller networks to retain the full spatial-frequency content of the signal as well as preserve training efficiency and lower storage cost. Evaluated on distinct raw experimental measurements across scales and complexities, WIEN-INR represents a practical step towards a broader adoption of neural representations in scientific workflows, delivering compact, robust and high-fidelity representations. Ni et al. present WIEN-INR, an implicit neural representation for scientific data compression. It operates in the multiscale wavelet domain to improve compression as well as preserve fine details and signal fidelity.

Fields Medalist to Join OpenAI as Roughly 40 Mathematicians Gather for Closed-Door Summit on AI and Mathematics

Sun, 23 Aug 2026 04:58:45 +0000

Fields Medalist Jacob Tsimerman announced at the award ceremony that he will soon join OpenAI to conduct AI safety research. Author and source: The Washington Post Jacob Tsimerman, who just won the Fields Medal last month, announced at the award ceremony that he will soon join OpenAI to work on AI safety research. In early August, he convened around 40 mathematicians at OpenAI’s headquarters in San Francisco with research scientist Sébastien Bubeck for a closed-door summit, one of whose talks was titled “The End of Mathematics.” Sébastien Bubeck, research scientist at OpenAI Speaker and University of Toronto professor Daniel Litt said: It is possible that we eventually end up in a world without high-quality mathematical research and the complete disappearance of human mathematical expertise. He believes extreme outcomes are unlikely, but mathematicians must take action. MIT mathematician Drew Sutherland put it more directly and coldly: Perhaps we are just the canaries in the coal mine, and mathematicians are the first to suffer. Mathematics has been chosen as a testing ground for AI because it serves as a proxy indicator for high-level cognitive ability—what can be achieved in mathematics will eventually be replicated in other domains. AI is currently performing calculations in bulk. The dense release of results served as the immediate backdrop for this summit. In May, an unreleased OpenAI model disproved the unit distance conjecture. This long-standing open problem regarding the arrangement of points on an infinite plane has been solved by AI using a known but difficult-to-operate technique from algebraic number theory—essentially applying tools across subfields. At the time, Tsimerman said he would "unhesitatingly accept" this result in any journal. This is regarded by many mathematicians as AI's first major mathematical breakthrough. Subsequent achievements emerged in quick succession. In August, OpenAI announced 10 new discoveries in mathematics and computer science generated by AI, spanning multiple subfields that typically take years to master. Anthropic hasn't been sitting idle either. Anthropic employee Levent Alpöge used Claude to construct pairs of orthogonal vectors on a 668-dimensional hypercube. Mathematicians have long believed that such a construction exists, but no one has ever actually built it. Alpöge announced the results by posting a 24,…

Can GPT Be Used as an Alternative Prediction Model to Traditional Machine Learning and Neural Networks on Low-Volume Clinical Data?

Sun, 23 Aug 2026 00:00:00 +0000

Background and Objective: Early and reliable disease prediction from structured clinical data remains challenging when datasets are small, highly imbalanced, and contain limited positive disease cases. Conventional machine learning (ML) and deep learning approaches often struggle to capture clinically meaningful relationships under such low-data representation conditions due to weak statistical associations between features and prediction targets. This study proposes a clinically grounded GPT2-based table-to-text framework for disease prediction using structured healthcare datasets, motivated by the contextual reasoning capability of GPT models to better capture clinically meaningful relationships when statistical learning alone becomes insufficient due to limited data availability. Methods & Materials: Structured clinical records were transformed into physician-style textual descriptions and enriched through GPT4-generated medical paraphrasing to improve minority-class representation while preserving clinical meaning. Both the original and generated clinical texts were used to fine-tune a GPT2 model across four public healthcare datasets, including heart disease, heart failure, chronic kidney disease, and thyroid cancer recurrence. Gradient-based explainable AI analysis was additionally incorporated to identify clinically important features influencing prediction outcomes. Results: The proposed framework demonstrated consistently strong predictive performance with average precision, specificity, sensitivity, and F1-score of 0.96, 0.97, 0.96, and 0.96, respectively. The model achieved improved sensitivity, stronger generalization, and more stable predictive behavior compared with traditional ML, deep learning, transformer-based, and GAN-augmented approaches. Importantly, the framework consistently emphasized clinically meaningful variables even under severe class imbalance where conventional ML and neural network models often struggled. Conclusions: The proposed GPT2-based table-to-text framework provides a practical and clinically interpretable approach for disease prediction from limited structured healthcare data. By integrating contextual clinical reasoning with explainable prediction mechanisms, the framework demonstrates strong potential for early risk detection, transparent clinical decision support, and reliable deployment in real-world low-resource…

"Classifying CRISPR-Cas9 Off-Target Cleavage Sites from GUIDE-seq Data: A Class-Imbalanced Machine Learning Benchmark"

Sun, 23 Aug 2026 00:00:00 +0000

Off-target cleavage is a central safety concern for CRISPR-Cas9 genome editing, particularly in therapeutic applications where unintended double-strand breaks carry clinical risk. We benchmarked five machine learning classifiers: logistic regression on mismatch-count summary features, a random forest and a gradient boosting model on one-hot-encoded sgRNA/candidate-site sequence pairs, a one-dimensional convolutional neural network (CNN) over the positional mismatch map, and a gradient-boosting/CNN ensemble: on a real, published GUIDE-seq off-target dataset (Kleinstiver et al., 2016, Nature) comprising 95,829 candidate off-target sites for five sgRNAs, of which only 54 (0.06%) were experimentally validated as true cleavage sites. On a held-out, stratified test split (n = 19,166; 11 true positives), gradient boosting on combined mismatch and sequence features performed best (ROC-AUC = 0.997, PR-AUC = 0.355, best F1 = 0.50), outperforming a random forest on raw sequence encoding alone (PR-AUC = 0.083) and a sequence CNN (PR-AUC = 0.129). Because the positive class is extremely rare, we report precision-recall AUC as the primary metric rather than ROC-AUC, which is inflated by the large negative class. A positional mismatch analysis showed that experimentally validated off-target sites carried substantially fewer mismatches overall than non-cleaved candidate sites (mean 3.6 vs. 5.9 mismatches across the 23-nucleotide target), and were markedly more mismatch-intolerant in the 10-nucleotide PAM-proximal seed region (11.3% vs. 27.4% per-position mismatch rate) and at the PAM itself (6.8% vs. 16.0%), consistent with established seed-region and PAM-sensitivity models of Cas9 target recognition. We report these findings, including the low absolute precision achievable in this severely imbalanced, small-positive-class setting, as a realistic picture of what off-target classifiers can and cannot yet deliver from sequence alone.

TrialCode Agent: LLM-Assisted Clinical Code-Set Construction for Trial Emulation

Sun, 23 Aug 2026 00:00:00 +0000

Objective Translating free-text clinical trial criteria into computable code sets is a valuable standardization practice that is necessary for producing reproducible real-world evidence studies but requires standardized interpretation across multiple clinical vocabularies. Methods We developed TrialCode Agent, a hybrid-large language model (LLM)-terminology verification agent that generates, formats, verifies, and expands candidate codes from free-text clinical criteria. The system supports ICD-9-CM diagnoses and procedures, ICD-10-CM, ICD-10-PCS, LOINC, and RxNorm medication concepts. We compared Baseline, Hybrid biomedical retrieval-augmented generation (RAG), and terminology-guided Family expansion pipelines using Claude, GPT Qwen, and MedGemma on 40 criteria from 11 trial groups. Performance was evaluated against expert-built reference code sets using exact-code precision, recall, and F1. Results The optimal pipeline varied by model. Claude with Baseline achieved the highest performance (precision 0.755, recall 0.619, F1 0.680), followed by GPT-5.5 with Baseline (precision 0.569, recall 0.658, F1 0.610), Qwen with Hybrid biomedical RAG (precision 0.656, recall 0.470, F1 0.548), and MedGemma with Family expansion (precision 0.487, recall 0.316, F1 0.383). Hybrid biomedical RAG improved aggregate F1 only for Qwen but increased GPT-5.5 RxNorm F1 from 0.320 to 0.909. Macro-averaged results showed criterion-level gains despite lower micro-averaged aggregate performance. Family expansion increased recall across models but generally reduced precision. In staged verifier ablation, micro-F1 increased from 0.254 before verification to 0.505 after final verification and expansion. Existence/vocabulary checking removed 2,594 false-positive codes, and acceptance filtering removed 952 additional false-positive codes before controlled expansion. Conclusions Combining LLM-based clinical interpretation with deterministic terminology verification produces auditable, database-ready code sets, but retrieval and broad family expansion do not consistently improve exact-code performance. Retrieval was particularly useful for RxNorm mapping, whereas overly broad or incomplete candidate generation remained the main source of error. Deterministic verification improves code validity and query readiness but cannot replace accurate clinical interpretation.

What AstroPT Knows About Galaxies, and What That Can Teach Us About LLMs

Sun, 23 Aug 2026 00:00:00 +0000

Interpretability research increasingly asks when concepts emerge during training and whether linear probes recover real structure, but in language models these claims are hard to validate because language offers little ground-truth ordering of concepts or relationships among them. We propose the use of astronomical ground truth through AstroPT, a transformer trained on millions of galaxy images, as a calibration testbed. AstroPT is an LLM-like model trained within a domain where the difficulty ordering of concepts and the relations among them are known in advance. Probing frozen representations across checkpoints, layers, model sizes, and objective choices, we find that galaxy properties emerge in a fixed order that tracks their known difficulty---quantities written almost directly into the pixels (band magnitude) become decodable early in training and shallow in the network, while multiband/spectra based and inferred quantities (such as redshift and specific star formation rate) emerge later and deeper. This order is invariant to our tested training objectives, and scales in magnitude but not in sequence with capacity. Our linear probe directions further recover the known physical structure among galaxy properties. Our findings suggest that astronomy offers a controlled sandbox for calibrating mechanistic interpretability methods we otherwise apply to LLMs blind.

World Models That Ignore Human Beliefs Predict Wrong Actions

Sat, 22 Aug 2026 09:00:20 +0000

New research reveals that current world models like Sora or Genie only simulate physics, ignoring what people think, want, or feel. The new "Mental World Modeling" framework adds mental variables such as beliefs and intentions. Even weaker language models using this approach outperform stronger models without mental modeling. The biggest bottleneck remains predicting how physical and mental states change together.

AINews: 10% worse, 100x cheaper, 10000x faster: Why Simulation is taking over

Sat, 22 Aug 2026 07:36:00 +0000

According to Latent Space, since 2022, components of the AI pipeline have been shifting from human-made to model-made, each flip starting with a 'patient zero' at a frontier lab. This trend is characterized as simulation: slightly worse (10%), but 100x cheaper and 10,000x faster. Stage 1 (2022): Reward signal went synthetic with InstructGPT's reward model, followed by Constitutional AI and RLAIF, where AI critiques itself. Stage 2 (2023): Training data became synthetic; Microsoft's Phi series showed 'Textbooks Are All You Need', Apple's WRAP rephrased the web with an LLM, making pretraining about 3x more efficient, and NVIDIA's Nemotron-4 340B shipped with a permissively licensed synthetic data generation pipeline.

Psychological methods reveal major weaknesses in AI security testing

Sat, 22 Aug 2026 07:00:05 +0000

Researchers at the UK AI Security Institute used psychometric methods to show that popular safety benchmarks for language models don't measure one consistent trait. Blanket blocking of requests can artificially inflate a safety score even as the model gets less useful day to day. The study also offers a method for catching models that act more cautious during tests than they do in normal use.

A Novel Benchmark Dataset for Enzyme Function Prediction Reveals Limitations of State-of-the-Art Models

Sat, 22 Aug 2026 00:00:00 +0000

Accurate computational prediction of enzyme function, standardized by Enzyme Commission (EC) numbers, is essential for large-scale genome annotation and generative enzyme design. However, it remains unclear whether state-of-the-art predictors learn the intrinsic structural determinants of catalytic activity or merely rely on global sequence similarity to annotated homologues. To address this gap, we introduce EnzymARC, a novel benchmark dataset of putative non-functional decoy sequences generated via structure-guided, systematic disruption of active sites (targeting catalytic residues and surrounding 5 A, 10 A, and 15 A radii) from experimentally annotated enzymes. We evaluated three distinct prediction paradigms against this dataset: homology-based annotation (DIAMOND), contrastive learning with protein language models (CLEAN), and a deep learning model incorporating non-enzyme discrimination (DeepEC). Our findings reveal that current models are highly vulnerable to phylogenetic shortcuts. Both DIAMOND and CLEAN exhibited false positive rates exceeding 90% for low-perturbation decoys, confidently assigning the original EC numbers despite the destruction of the catalytic machinery. While DeepEC demonstrated improved sensitivity at higher perturbation levels, highlighting the benefit of negative training examples, all models struggled to identify targeted active-site disruptions. We demonstrate that modern EC predictors largely fail to distinguish catalytically incompetent variants from functional enzymes, and we propose that integrating structure-aware negative examples into both training and benchmarking is critical for developing functionally robust models in computational enzymology.

Characterizing interference weights in a tiny language model

Fri, 21 Aug 2026 22:06:47 +0000

Mechanistic interpretability aims to read neural networks like programs, but superposition forces features into low-dimensional projections. This study trains a one-layer transformer and decomposes its virtual weights between tokens, positions, features, and logits, scoring weights by effectiveness and helpfulness. They find an interference weight: the path from 'IN' to logits via residual stream votes for 'utions', a token that never follows 'IN' in training, so every time it affects output, it worsens loss. This demonstrates, for the first time, an interference weight inside a trained transformer by measuring its effect on training loss.

Building Agentic Document Intelligence Pipelines: Creating Scientific Figures with AutoFigure

Fri, 21 Aug 2026 22:00:25 +0000

This tutorial introduces AutoFigure, a practical toolkit for generating scientific figures directly from text descriptions, paper-like content, and structured methodological explanations. Steps include setting up the AutoFigure environment, fixing dependency issues such as Pillow compatibility, preparing SVG and PNG rendering tools, building a custom reference figure, configuring an API-backed generation workflow, and converting a detailed agentic document intelligence pipeline into a publication-style scientific diagram. The tutorial also covers offline SVG rendering, inspecting generated files, creating a sample paper and PDF, and exporting final outputs to a reusable gallery and zip archive. Code snippets show configuration with default models (e.g., google/gemini-3.1-pro-preview), maximum iterations, and quality threshold settings.

Nvidia Research: The Harness, Not the Model, Is Key for AI Agents

Fri, 21 Aug 2026 19:43:39 +0000

New Nvidia research highlights that for long-horizon tasks, the software harness—the tools, memory management, and rules around a model—matters more than the model itself. Using a custom harness with improved memory handling and a supervisor component, researchers achieved a 100% score on the interactive reasoning benchmark ARC-AGI-3 with Claude Opus 5, compared to 30% without the harness, which was still the best among models tested. Nvidia product VP Adel El Hallak notes that an agent encompasses the model, scaffolding, runtime, and skills. Long-horizon tasks require many sequential decisions and are prone to errors; Microsoft's April research found all tested LLMs filled documents with errors on such tasks.

How mobility gives language models a deeper understanding of place

Fri, 21 Aug 2026 19:38:27 +0000

Google Research researchers introduce Mobility-Embedded POIs (ME-POIs), a dynamic, mobility-informed framework that enhances language models' understanding of places by integrating aggregate, anonymized mobility patterns such as arrival times, stay durations, and surrounding movement. This self-supervised approach blends text descriptions with large-scale mobility data to create embeddings that capture both a place's identity and its dynamic functionality, improving predictions of attributes like opening hours, price levels, and busyness. The framework demonstrates that complementing semantic labels with mobility data significantly enriches geospatial representations, offering a deeper understanding of the built environment.

An AI tool for prioritizing candidate biomarkers from wearable sensor data

Fri, 21 Aug 2026 18:08:26 +0000

Researchers at Google Research introduce the Biomarker Discovery Framework, a multi-agent system that prioritizes candidate biomarkers from wearable sensor data through iterative hypothesis generation, statistical analysis, adversarial validation, and literature-grounded reasoning, under human supervision. The system combines deterministic computation for numerical analysis with generative reasoning for hypothesis formation and interpretation, using an Orchestrator agent to guide specialized agents through a six-phase process. This approach addresses the limitations of existing language model-based systems that often overlook statistical validity on physiological time-series data. In three cohorts (N = 9,279 participant-observations), the framework recovered known clinical signals, identified convergent biomarkers across independent datasets, and improved downstream prediction when combined with demographic features. The framework maintains strict statistical rigor and preserves traceability through shared memory and structured fact sheets.

NVIDIA's Coding Agent AVO Scores 100% on ARC-AGI-3

Fri, 21 Aug 2026 14:54:53 +0000

NVIDIA's coding agent AVO achieved a perfect score on ARC-AGI-3's 25 public games, solving all 183 levels. Given no rules or stated goals, the agent learns by trial and error, observing outcomes and correcting mistakes. AVO's success hinges on retaining what it has learned.

🧮 Introducing MathForm: An Open-Source Framework, Dataset, and Model for Mathematical Autoformalization

Fri, 21 Aug 2026 13:01:13 +0000

Introducing MathForm, an open-source framework, dataset, and model for mathematical autoformalization with Lean 4. Formalizing mathematics makes mathematical knowledge machine-checkable, but it is more than translating statements into code. A model must map each concept onto… (source truncated).

AI-Associated Writing Spreads Very Unevenly Across the Web

Fri, 21 Aug 2026 12:43:43 +0000

By 2026, AI-associated writing has spread very unevenly across the web. 9.4% of .com webpages show significant signs of AI editing or authorship, compared with only 1% of .edu and 0.8% of .gov pages, indicating a significant disparity in AI influence across different top-level domains.

#IJCAI-ECAI 2026 Distinguished Paper Award Winners Announced

Fri, 21 Aug 2026 09:34:40 +0000

The 35th International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJCAI-ECAI 2026) named three distinguished papers. One paper, titled "Approximate Strategyproofness in Approval-Based Budget Division" by Haris Aziz et al., proves that the Nash product rule (NASH) has an incentive ratio of 2, thereby circumventing the impossibility theorem by Brandl et al. by relaxing strategyproofness. Another paper, "Tuple Inconsistency Measures: Toward Explaining Query Answers" by Yurun Gu et al., introduces novel tuple inconsistency measures that quantify the extent to which individual tuples contribute to violations of denial constraints in inconsistent databases, and applies them to explain inconsistency in query answers.

Microsoft Research's Skala Model Joins CP2K to Rework DFT's Exchange-Correlation Functional

Fri, 21 Aug 2026 07:00:00 +0000

A new artificial intelligence model designed to improve one of quantum chemistry’s most difficult calculations is now available through the CP2K software ecosystem, potentially allowing researchers to simulate larger molecular systems without sacrificing as much accuracy. Developed by Microsoft Research AI for Science, the model, known as Skala, replaces part of the conventional mathematical machinery used in density functional theory with a neural-network-based exchange-correlation functional. Its integration into CP2K gives scientists an open-source environment in which to test the approach across problems in chemistry, physics, materials science and engineering. Density functional theory, or DFT, has become one of the central tools of modern computational science. The method makes it possible to investigate the electronic structure of molecules and materials by calculating how electrons are distributed through space. Rather than tracking every electron individually, DFT works primarily with the electron density, a quantity that describes the probability of finding electrons in different regions. This reduction makes quantum mechanical simulations feasible for systems containing many atoms, but it also introduces a major approximation: the exchange-correlation functional, which describes complex interactions among electrons that are difficult to calculate directly. “The Achilles’ heel of DFT is the so-called exchange-correlation functional,” says Prof. Thomas D. Kühne, director of the Center for Advanced Systems Understanding, or CASUS. These functionals determine how the electron density is translated into energy and forces, meaning that their quality strongly influences the reliability of a simulation. More advanced functionals can deliver more accurate results, but they often require substantially greater computational resources. As the number of particles increases, the computational cost can become prohibitive, restricting the most precise calculations to relatively small molecular systems. Skala takes a different route from traditional approaches, which generally improve DFT by adding increasingly elaborate analytical expressions. Its neural network has been trained to recognize how electron densities in separate regions influence one another and to use those relationships when estimating exchange-correlation energy. In practical terms, the model at…

PEtab SciML: an exchange format for specifying and training dynamic scientific machine learning models

Fri, 21 Aug 2026 04:00:00 +0000

Summary: Dynamic scientific machine learning (SciML) models that combine mechanistic ordinary differential equations (ODEs) with machine learning (ML) components have applications ranging from learning unknown biological processes to integrating auxiliary data modalities into dynamic modelling. To enable reproducible and efficient SciML training, we introduce PEtab SciML, an interoperable data format for specifying parameter estimation problems in which mechanistic and ML model parameters are jointly estimated from time series data. PEtab SciML supports several ML ODE hybridization patterns in realistic problem setups. It is accompanied by a reference Python library and downstream modelling support in Python/JAX and Julia, provided by AMICI and PEtab.jl, respectively, and a collection of real data benchmarks. Availability and implementation: PEtab SciML is available on GitHub (https://github.com/PEtab-dev/petab_sciml). The reference Python package is installable from PyPI and is continuously tested and supported on Linux, macOS, and Windows.

Multi-Tool Robotics Enables In-Situ Sample Manipulation for Time-Resolved Synchrotron Measurements

Fri, 21 Aug 2026 04:00:00 +0000

The high photon flux at synchrotron beamlines allows for the measurement of fast dynamical processes. However, beamline radiation-safety protocols prohibit human intervention during X-ray experiments, limiting the ability to perform versatile real-time sample manipulations during continuous data acquisition. Here we present a robotic platform at an X-ray scattering beamline to enable real-time sample handling and processing in the experimental hutch, revealing previously inaccessible transient in-situ dynamics in perovskite thin films. This modular multi-tool robotic architecture enables in-hutch sample manipulation beyond human-access constraints, establishing a foundation for automated and autonomous synchrotron experimentation.

Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective

Fri, 21 Aug 2026 04:00:00 +0000

This survey proposes viewing agent evolution as dynamic graph transformation, modeling agent state as a dynamic graph where memories, tools, skills, workflows, and inter-agent relations are represented as typed nodes, edges, and subgraphs updated through schema-constrained rewrites. The authors organize existing dynamic-graph-based methods for self-evolving agents into four taxonomies: node/feature evolution, edge/topology evolution, subgraph activation, and cross-component co-evolution. They propose dynamic graph learning as reusable infrastructure for self-evolving agents, mapping nine dynamic-graph-learning subfields to agent-evolution capabilities and discussing adaptations and possible failure modes. Finally, they discuss five types of graph-aware evaluation and governance protocols from a dynamic-graph perspective. The goal is to provide a compact structural lens for designing and governing self-evolving agents.

Automatic bioinformatic software named entity recognition from literature

Fri, 21 Aug 2026 04:00:00 +0000

Uncovering the Limits of Proof Sharing for Neural Networks

Fri, 21 Aug 2026 04:00:00 +0000

Robustness verification of neural networks is increasingly important, due to their use in many critical domains. In certain scenarios, proof sharing has been shown to accelerate incomplete verification techniques by reusing intermediate-layer abstract states, or templates, across queries. However, questions remain as to the robustness of template-based acceleration across varying network architectures, properties, datasets, and training methods. In this work, we perform a systematic study of the effectiveness of template-based acceleration and its limits. Our study shows that template subsumption rates can vary widely across scenarios. We present a novel metric of jointly stable neurons to explain this variation, showing that in some cases template-based techniques are very unlikely to provide any speedup. Then, we present FastCert, a novel technique for automatically distributing templates across neural network layers to increase performance impact, eschewing templates entirely if they are unlikely to produce a speedup. Across a large set of covering-design based $L_0$-verification tasks, FastCert achieved an average speedup of 1.13x over an extant template-based reuse technique.

Hepatitis C Virus Genotyping with a Transformer Neural Network

Fri, 21 Aug 2026 04:00:00 +0000

This study evaluated the performance of Transformer-based models in predicting hepatitis C virus (HCV) genotypes and subtypes after fine-tuning. A total of 2,881 HCV whole-genome sequences from the Los Alamos HCV Sequence Database were used, covering genotypes 1 to 6 and all confirmed subtypes; genotypes 7 and 8 were excluded due to insufficient samples. Fine-tuning with homogeneous fragmentation and balanced sample distribution achieved precision ranging from 98.48% to 100% for genotype classification. In contrast, using a fragmentation strategy that caused data imbalance and arbitrary sample distribution across training files resulted in a precision of 48.12%, which is considered low, with a high error rate for genotype 5 due to its low frequency in the datasets. For subtype classification, the best-performing fine-tuning approach achieved 99.89% accuracy and 99.87% precision. Models that included additional genotypes showed a slight performance decrease due to increased task complexity. The study demonstrates that, with properly fragmented, distributed, and labeled data, Transformer-based neural networks are a promising approach for HCV genotype and subtype classification.

BirdCODE: Scaling Zero-Shot Sound Event Detection for Bioacoustics

Thu, 20 Aug 2026 17:02:01 +0000

Image credit: Bejan Adrian / Pexels Key Takeaways BirdCODE is a supervised deep learning model that offers zero-shot sound event detection , enabling researchers to detect and classify bird vocalizations with temporal precision and without dataset-specific fine-tuning. The model was developed using a combination of training strategies using weakly labelled recordings, synthetic soundscapes, and pseudo-labelled data to perform sound event detection and classification across more than 9,000 bird species . Applying BirdCODE to more than one million recordings demonstrates how large-scale temporal annotation can enable new analyses of vocal behaviour across species, seasons, geography, and mixed-species interactions . BirdCODE, the model weights, and large-scale detections are openly available on GitHub to support future research in Animal Language Processing. This paper is a preprint and currently under review. Sound Event Detection: A Bottleneck in Bioacoustics Sound event detection remains one of the central challenges in bioacoustics. While recent machine learning models have dramatically improved species classification, detecting the precise temporal boundaries of individual vocalizations remains difficult, particularly at large taxonomic scales where strongly labelled training data are scarce. For researchers interested in animal communication, this limitation is significant. Studying communication at scale depends on accurate temporal annotation. Understanding interactions between individuals often requires knowing precisely when each vocalization begins and ends relative to every other vocalization. Solving this challenge is therefore a prerequisite for studying animal communication at scale. Introducing BirdCODE To address this challenge, we developed BirdCODE (Bird Communication Detector), a deep learning model for large-scale sound event detection and classification across more than 9,000 bird species. Unlike conventional sound event detection systems, BirdCODE does not rely on extensive strongly labelled training datasets. Instead, it learns from the vast collection of weakly labelled recordings contributed through citizen-science initiatives, combining them with synthetic soundscapes and pseudo-labelled predictions in a multi-stage training pipeline. Figure 1: An overview of BirdCODE model training. Strongly labeled data are converted to per-frame…

Humyn Labs Releases Egocentric Video Library for Physical AI

Thu, 20 Aug 2026 16:39:57 +0000

Humyn Labs has unveiled an egocentric video library for physical AI, comprising 5 first-person datasets categorized by environment and capture type. The datasets feature 6-DoF head poses, 21-point hand keypoints, absolute metric depth, and per-frame action labels. Data is delivered in MCAP, RLDS, and LeRobot v3 formats.

Broadening access to Skala creates a faster path to predictive DFT

Thu, 20 Aug 2026 16:00:00 +0000

Microsoft Research's Skala 1.1 demonstrates the continuous improvement of their deep-learning DFT approach. Trained on 2.5× more data than its predecessor, it delivers substantially higher accuracy across key molecular simulation challenges, including thermochemistry, reaction kinetics, and molecular structure prediction. Skala is now available in CP2K and is being integrated into Psi4, FHI-aims, ORCA, and VASP, bringing next-generation DFT accuracy closer to the communities that rely on these codes. Microsoft Research also introduces a living benchmark to track the computational performance of successive Skala releases, helping the community measure and accelerate progress toward greater accuracy and efficiency.

Welcome to the AI crisis in math

Thu, 20 Aug 2026 14:00:00 +0000

On this episode of Decoder, The Verge's London-based AI reporter Robert Hart discusses the impact of AI on the field of mathematics and the existential crisis many leading mathematicians are experiencing. OpenAI's recent release of solutions to longstanding math problems has caused a stir in the math community, sparking intense debate. While AI systems still struggle with elementary arithmetic, they are becoming increasingly adept at high-level abstract math, raising big questions: If AI can do math at this caliber, can those skills transfer to other domains? What is the value of academic grants and university programs training new mathematicians if frontier models can answer all outstanding questions? Or is this all just a marketing exercise for AI labs? Hart has spoken with numerous accomplished mathematicians to explore these issues.

Ultra-FineWeb-L1 Released: Over 1 Trillion Tokens of High-Quality Web Data

Thu, 20 Aug 2026 13:01:14 +0000

Ultra-FineWeb-L1, part of the UltraData ecosystem, is now open-sourced, offering over 1 trillion tokens of high-quality English web data processed from Common Crawl. The release underscores the importance of high-quality data for training powerful large language models (LLMs).

HourGlass: Filling in the Gaps Between Forecasts

Thu, 20 Aug 2026 09:00:00 +0000

A forecast valid at noon and another at 18:00 can tell us where the weather is going, but not everything that happens in between. Storms develop, move and weaken continuously, yet many state-of-the-art artificial intelligence (AI) weather forecasting systems provide forecasts only every six hours. While this resolution is sufficient for many applications, finer temporal detail is essential for applications such as flood forecasting and renewable energy, as well as for rapidly evolving weather events. Producing hourly forecasts directly is possible but can lead to error accumulation and temporal inconsistencies. HourGlass is our new data-driven probabilistic temporal downscaling method developed through a collaboration between MET Norway and ECMWF to address this challenge. Rather than replacing the underlying forecast, HourGlass reconstructs the likely evolution of the atmosphere between forecast times, generating coherent hourly forecasts that remain consistent with the original prediction. The collaboration combines MET Norway's expertise in regional AI weather forecasting with ECMWF’s expertise in global AI weather forecasting, demonstrating that the same approach can be successfully applied across forecasting scales. Following successful evaluation in both global and regional forecasting systems, HourGlass will become part of ECMWF's operational forecasting system later this year. As part of the Artificial Intelligence Forecasting System (AIFS) v3, it will provide hourly forecasts of key surface variables for both AIFS Single and AIFS Ensemble (AIFS ENS), bringing high-temporal-resolution AI forecasts to operational users. At MET Norway, HourGlass is already running in near-real time and is available to forecasters for evaluation, with plans to implement it operationally to produce hourly forecasts for the Bris ensemble forecasting system. How HourGlass generates hourly forecasts from six-hourly inputs. M₁ and M₂ represent different ensemble members. Preserving variability and consistency This operational capability is made possible by two key features of HourGlass. Existing temporal downscaling methods have typically been trained deterministically, which can lead to overly smooth forecasts, particularly near the centre of the temporal window. HourGlass instead uses variants of the continuous ranked probability score (CRPS) to help preserve small-scale…

Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization

Thu, 20 Aug 2026 08:51:38 +0000

This tutorial presents an end-to-end preference-learning workflow using the Anthropic HH-RLHF dataset and Direct Preference Optimization (DPO). It covers preparing a Colab environment, loading and parsing chosen-rejected response pairs, auditing the dataset for structural and length-based biases, and performing lexical shortcut diagnostics. The process includes tokenizer-aware length filtering and a version-robust DPO training pipeline with TRL and optional LoRA. It fine-tunes a Qwen2.5-0.5B-Instruct model, evaluates reward accuracy and training behavior, analyzes performance across HH-RLHF subsets, inspects potential length bias, generates sample responses, and saves the policy. The workflow emphasizes robustness and compatibility, including handling dependency issues in Colab.

Flux-form spatiotemporal neural operators for coarse-grained dynamics of multiscale PDEs

Thu, 20 Aug 2026 04:00:00 +0000

This study addresses data-driven prediction of coarse-grained dynamics in multiscale PDE systems. Adopting a closure-free operator-learning viewpoint, the authors apply a linear coarse-graining map and learn a surrogate evolution operator for the resolved field directly from filtered high-fidelity trajectories. Motivated by the Mori-Zwanzig formalism, they propose a spatiotemporal neural operator mapping a resolved history slab to a resolved future slab. Spatial mixing uses Fourier convolution, while temporal mixing uses a causal kernel operator with position-attention weights on time lags. A flux-form inductive bias embeds explicit divergence form to improve rollout robustness and suppress nonconservative artifacts. Validation on viscous Burgers' equation, Kuramoto-Sivashinsky equation, and two-dimensional turbulent flows yields stable autoregressive rollouts with improved long-horizon accuracy and statistical fidelity.

Debiased Inference for AI-Generated Data without Gold-Standard Labels: Identification via Multiple Imperfect Measurements

Thu, 20 Aug 2026 04:00:00 +0000

Researchers increasingly use AI to measure variables for downstream analyses, but ignoring prediction errors—even with accuracy above 90%—can cause substantial bias and invalid confidence intervals. Existing remedies like design-based supervised learning and prediction-powered inference rely on costly gold-standard labels. This paper introduces debiased inference with multiple imperfect measurements (DMM), a framework that leverages several error-prone AI measurements to achieve valid inference without gold-standard labels. Building on CP decomposition, DMM assumes measurements are independent conditional on the latent true label and unit-level features, allowing misclassification rates to vary across annotators and units. Semiparametric theory proves the DMM estimator is consistent and asymptotically normal. Simulations show valid inference and efficiency gains from adding imperfect measurements. Diagnostics for the conditional independence assumption are also developed.

Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference

Thu, 20 Aug 2026 04:00:00 +0000

Monroe is a new molecular foundation model (MFM) pretrained on over 81 million molecules from the PM6 quantum chemistry dataset. It incorporates innovations including an improved graph representation of stereochemistry, improved training losses (conformer denoising and embedding decorrelation), improved multi-task learning, and uses a prior-data-fitted network (TabPFN) for downstream in-context prediction. Across established Polaris benchmarks, Monroe matches or exceeds existing MFMs, and on activity cliff benchmarks it achieves significant improvements over prior methods. Ablation and transfer experiments show that the PFN-based downstream predictors also substantially improve two leading existing models, MiniMol and CheMeleon, yielding new state-of-the-art variants called MiniMol_PFN and CheMeleon_PFN, indicating the strategy generalizes beyond Monroe. Source code is available at github.com/blazejba/monroe.

A single design choice determines whether machine learning models of materials make physically impossible predictions

Thu, 20 Aug 2026 04:00:00 +0000

Machine-learned models are replacing first-principles calculations across materials discovery, and physical symmetry is the central guarantee built into them. The debate over how much symmetry to hard-wire rather than learn has run on rotations, where a symmetry error is an approximation error. Some constraints are exact: symmetry forces certain property tensors to exactly zero, so a nonzero prediction is physically impossible rather than inaccurate. Here we show that whether a model can make such predictions is decided before training by one rarely reported design bit, whether its features carry parity labels, and derive a criterion, the parity gap, that computes from group theory alone which properties and crystals are exposed. Across matched architecture pairs differing only in that bit, evaluated on two thousand centrosymmetric crystals whose piezoelectric tensor must vanish, parity-labelled arms sit at the floating-point floor while rotation-only arms predict forbidden responses on 90-96% of crystals, six orders of magnitude apart, at no accuracy cost. Training on explicit zeros does not recover exactness, and a head on a frozen universal potential inherits its backbone's symmetry group. One reflection at random initialization verifies the label in seconds.

EXIMO: VLM-Guided Exploration of VLA Policies

Thu, 20 Aug 2026 00:00:00 +0000

EXIMO is an efficient algorithm for fine-tuning vision-language-action (VLA) policies to learn new tasks quickly. It operates in three stages: explore, imitate, and optimize. During exploration, a vision-language model (VLM) acts as a planner, breaking down long-horizon tasks into shorter subtasks for the VLA, and together they collect an orchestrated dataset. During imitation, the VLA is fine-tuned on this data. Finally, residual off-policy reinforcement learning further optimizes the policy. Experiments show EXIMO significantly outperforms existing approaches in sample efficiency and final performance.

Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization

Thu, 20 Aug 2026 00:00:00 +0000

Large language models often fail to answer questions about a bounded document collection without retrieval at inference. This work frames the problem as document knowledge internalization, converting a fixed corpus into usable parametric knowledge. The proposed IAR (Inject, Align, Recover) framework separates structured knowledge injection, QA alignment, and general ability recovery. Inject uses continuation, rewrite, and instruction-conditioned reconstruction objectives; Align adapts with answer-only QA supervision; Recover merges the domain-adapted model with the base instruction model. Across Common Corpus and CCI, and Llama, Phi, Qwen, and SmolLM families, IAR improves the domain-general frontier. In the main comparison, IAR outperforms Vanilla SFT in 7 of 8 dataset-model settings on all four metrics, with average gains of 3.6 percentage points in domain QA accuracy and 12.1 points in mean general performance (IFEval, MMLU, MSBench). While LoRA and FAPM can win individual general metrics, IAR retains one of the strongest general profiles among methods with leading domain internalization.

Provenance Grounds Trust in Autonomous Science

Thu, 20 Aug 2026 00:00:00 +0000

Nature Computational Science, Published online: 20 August 2026; doi:10.1038/s43588-026-01035-4 As large language models begin to plan and run experiments on their own, the reflex is to demand that they be interpretable before they are trusted. We argue that what grounds trust in autonomous science is not a view inside the model; it is provenance: a complete, re-openable record of what was reasoned, done and measured that can be audited and set right when it is wrong, making autonomy a corrective for the literature’s biases rather than a threat to rigor. Interpretability remains valuable, but the record is the gateway to trust.

PolicyGuide: From Guarding One Action to Guiding the Whole Workflow for Policy-Compliant LLM Agents

Thu, 20 Aug 2026 00:00:00 +0000

Customer-service LLM agents must follow organizational policy when acting on a user's behalf. Compliance failures arise from either forbidden actions, such as granting an ineligible change, or omitted procedural requirements, such as identification or confirmation. Runtime safeguards can intervene on risky actions, but action-local checks do not guide an agent through a multi-step procedure. PolicyGuide compiles each domain policy into a workflow graph and invokes a proactive verifier at user-turn boundaries. From persisted graph state, the verifier reconciles open requests and returns step-specific remediation along a policy-compliant path. Across the τ²-bench airline, retail, and telecom domains with a GPT-5.4 agent and verifier, PolicyGuide raises mean Pass⁴ from 0.42 to 0.62, with the largest gain on telecom (0.19 to 0.61), the most workflow-structured domain. The same workflows transfer to Claude Sonnet 4.6 and Gemini 2.5 Pro agents. Complementary evaluations find the lowest observed attack-success rate under adversarial users and the strongest procedural compliance in an author-designed workflow-level validation.

Quantifying Benchmark Optimization in ASR Models

Thu, 20 Aug 2026 00:00:00 +0000

Public benchmarks are important for measuring Automatic Speech Recognition (ASR) capabilities, but models may be optimized for these benchmarks in ways that fail to generalize. This paper presents a methodology to quantify such benchmark optimization, focusing on cases where audio underdetermines the reference transcript. The authors identify three families of behavioral probes—reference disagreement, masked-number recovery, and orthographic switching—that reveal models' abilities to reproduce benchmark reference spans despite ambiguous audio. They find that top-scoring open-source models output verbatim reference transcripts even when audio is contradictory, masked, or ambiguous. Mechanistic probes show models use narrow acoustic cues to override faithful audio representation, favoring a benchmark-optimized policy. This behavior can be causally manipulated via low-rank linear steering or appending audio. Overall, high-performing models exhibit benchmark-conditioned behaviors that can inflate benchmark scores without reflecting improved general-purpose transcription ability.

NAPE: Next Patch Embedding Prediction Enables Scalable Audio Learners

Thu, 20 Aug 2026 00:00:00 +0000

Self-supervised learning has driven audio representation learning, but existing methods often rely on complex pre-training recipes. The authors introduce NAPE, a framework where a causal Transformer predicts the next patch embedding of a log-mel spectrogram from previous ones, using only causal masking and stop-gradient as training signals, avoiding reconstruction decoders, acoustic tokenizers, and other components. Across six audio and speech benchmarks, NAPE achieves state-of-the-art fine-tuning performance on several tasks, scales consistently with encoder size, and yields strong linear-probing results, while producing structured attention patterns without explicit supervision.

Repo0: Design-Driven Zero-to-All Code Generation

Thu, 20 Aug 2026 00:00:00 +0000

Existing code generation systems often assume a predefined repository architecture, which fails in zero-to-all code generation where an agent must build a complete software project from natural-language requirements while maintaining modular architecture. We present Repo0, a continuous structural evolution framework that maintains an explicit architectural state as a Dual-Directed-Acyclic-Graph (Dual-DAG), combining requirement-level and component-level DAGs with alignment. Starting from requirements, it iteratively evolves component boundaries via structural actions guided by modularity metrics until convergence, then uses the converged architecture for test-driven code generation. Evaluated on six real-world repositories from RepoCraft using GPT-5 mini and DeepSeek V3.2, Repo0 achieves the highest Functionality Coverage and Pass Rate, improving over the strongest baseline RPG by up to 20.08 and 29.74 percentage points, respectively. Ablations and structural-evolution analyses confirm the importance of the Dual-DAG state, modularity-guided evolution, and explicit convergence.

EnvHarness: Awakening Static Worlds for Agent Learning

Thu, 20 Aug 2026 00:00:00 +0000

LLM agents learn by interacting with environments, yet existing environments are hand-built and static: blind to an agent's weaknesses, and quickly left behind as it improves. While recent environment generation methods attempt to address this, they require domain-specific pipelines, rely on expensive or unreliable verifiers, and still produce static environments. To alleviate the engineering burden, we propose Environment Harness (EnvHarness), a programmable layer of plug-in components that wraps a static environment to reshape its behavior without modifying the underlying logic. Operating through standard interfaces, EnvHarness applies across diverse domains while ensuring every reshaped environment retains its original verifier. To automate this process, we introduce EnvRigger, which treats the target policy as a black box, observing its execution trajectories to synthesize components targeting diagnosed flaws, and validating them via fresh rollouts. Across five benchmarks in four domains, EnvHarness outperforms both original environments and domain-specific pipelines, achieving up to a 9.0-point improvement on held-out instances with 9.8% fewer execution steps. Furthermore, EnvHarness provides a superior optimization signal for reinforcement learning, enabling continuous, targeted co-evolution of the policy and its environment.

MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use

Thu, 20 Aug 2026 00:00:00 +0000

MemTrapBench is a new benchmark for evaluating cognitive traps in large language model (LLM) memory use. Existing memory benchmarks mainly assess whether information is correctly extracted, stored, and retrieved, but overlook how retrieved memories reshape model reasoning and affect current task performance. MemTrapBench covers two forms of cognitive traps: Reasoning Fixation and Belief Distortion. Experiments across two model families and five memory frameworks show that all evaluated memory strategies underperform the no-memory setting, with even the strongest methods suffering drops of more than 10%. To mitigate these traps, the authors propose AdaptiveMem, a simple inference-time method that instructs LLMs to avoid memory traps. AdaptiveMem mitigates cognitive traps on MemTrapBench while preserving or improving performance on standard memory benchmarks across diverse memory frameworks.

Multilingual Knowledge Transfer under Data Constraints via Lexical Interventions

Thu, 20 Aug 2026 00:00:00 +0000

Cross-lingual knowledge transfer is critical for multilingual models with scarce target-language data. Existing methods require parallel data, translation systems, or extra training stages. The paper proposes LINK, a data-level intervention that substitutes words in high-resource (English) pretraining data with translations using bilingual vocabularies, requiring no extra training. Evaluations on eight languages and five model sizes show notable downstream improvements and up to a 2x training speedup to reach equivalent performance.

Dynamics-aware geometric learning predicts disease-associated molecular perturbations

Thu, 20 Aug 2026 00:00:00 +0000

Missense mutations and post-translational modifications (PTMs) are major molecular perturbations that reshape protein function but are traditionally studied independently. Current computational approaches largely rely on sequence conservation or static structural features, limiting our understanding of how perturbations alter intrinsic protein dynamics. We present DynGeo-Pheno, a unified geometric deep learning framework that integrates protein language model representations with anisotropic network model-derived dynamics to jointly capture evolutionary, structural, and biophysical information. DynGeo-Pheno predicts disease-associated phosphosites and pathogenic missense mutations with high accuracy on independent test datasets. Ablation analyses indicate that protein dynamics provide complementary information beyond sequence evolution and structural topology for pathogenicity prediction. Beyond predictive performance, DynGeo-Pheno reveals that disease-associated perturbations preferentially localize to functional structural regions, including ligand-binding pockets and PPI interfaces. Mechanistically, phosphosites and missense mutations appear to exhibit distinct yet convergent dynamic signatures. Phosphosites preferentially occur in flexible regulatory regions, whereas pathogenic mutations are enriched in ordered structural elements. Despite these differences, both perturbation types display enhanced long-range coupling, increased perturbation responsiveness, and elevated mechanical stability, indicating that pathogenic residues preferentially occupy mechanically constrained and allosteric regulatory sites. This study provides compelling evidence that intrinsic protein dynamics is an important complementary determinant of pathogenicity and establishes a unified framework for interpretable AI predictions and mechanistic understanding of how genetic and regulatory perturbations may shape protein function.

On Rashomon Sets, the Mathematics of Simplicity, and Why We Don’t Need Black Boxes: An Interview with Cynthia Rudin

Wed, 19 Aug 2026 12:34:11 +0000

In this episode of AI Pioneers, Cynthia Rudin, a pioneer in interpretable machine learning and winner of the 2022 Squirrel AI Award, discusses her work on black boxes and Rashomon sets. She recounts her early project with Con Edison on the NYC power grid, where black-box models failed and simple interpretable models outperformed them. Rudin argues that for high-stakes problems like loan decisions or crime prediction, noisy data cannot support complex models, so black boxes are not necessarily more accurate. She explains her efforts to prove this mathematically and mentions applications from cancer detection to interpretable AI-generated music.

Hyra Math Update: Improved Lower Bound for 3D Constant-Width Bodies

Wed, 19 Aug 2026 09:06:37 +0000

Hyra continues to push the frontier. Since our last math update, four more advances have landed: In 3D Blaschke–Lebesgue, the universal lower bound for constant-width bodies has been raised from 0.380799w³ to 0.411040w³, now reaching 97.9% of the conjectured Meissner optimum. Additional advances include Beurling–Ahlfors and others.

Together AI's DeepSWE Evaluation: GPT-5.6 Sol Leads on Single Run, DeepSeek V4 Pro Surpasses with Retries

Wed, 19 Aug 2026 05:22:12 +0000

Together AI conducted a comparative evaluation on 113 real-world software engineering tasks using DeepSWE, running each task four times for a total of 904 rollouts. On single-run execution, GPT-5.6 Sol performed significantly better with a pass@1 of 72.7%, compared to DeepSeek V4 Pro 0813's 62.8%. However, when retries were allowed, the results reversed: DeepSeek V4 Pro achieved a pass@4 of 88.5%, surpassing Sol's 85.8%.

Stanford Research: X Algorithm Prioritizes Outrageous Content to Boost Engagement

Wed, 19 Aug 2026 04:39:05 +0000

A study published in PNAS found that X's recommendation algorithm tends to prioritize content inconsistent with users' values, i.e., anger-inducing content, to increase engagement, forming a cycle of 'more anger, more interaction, more recommendations.' Conducted by Stanford and other institutions, the study involved 715 U.S. users who installed browser extensions for data collection and completed value surveys. The authors highlight the lack of transparency in recommendation algorithms and their impact on public opinion. X has not officially responded, but a former product lead indicated the algorithm was adjusted, reducing such recommendations by an order of magnitude.

The Little Scientist: LLM Agent-Driven Discovery via the Scientific Method

Wed, 19 Aug 2026 04:00:00 +0000

What happens when you teach an LLM-based agent the scientific method? Motivation: Scientific discovery emerges from cycles of hypothesis, implementation, empirical testing, and feedback. Can this process be automated? We approach automated algorithm design through the lens of the scientific method, where an LLM-based agent goes through each step of the process in an ordered, iterative fashion. Results: We present The Little Scientist, a framework in which a "Scientist agent" works inside an evaluation environment that benchmarks its code and returns structured per-instance diagnostics. When the Scientist plateaus at a local optimum, a "Kuhn agent" injects a paradigm-shifting conjecture paired with a cross-disciplinary inspiration, forcing exploration of a different region of the LLM's latent space. We demonstrate the framework on two problems that require fundamentally different modes of discovery. For protein fitness prediction, the Scientist discovered Delta V, an ensemble calibration strategy that ranks first on the ProteinGym DMS Substitutions Zero-Shot leaderboard across all five official evaluation metrics, exceeding the #2 model (VenusREM) by +0.033 mean Spearman correlation across 217 DMS assays. For DNA motif discovery, the Scientist wrote an algorithm from scratch--DALE (Dual-seed Algorithm for Latent Enumeration)--that outperforms STREME (the default in the MEME Suite) across 132 ENCODE transcription factors (mean AUROC 0.842 vs. 0.803, Wilcoxon p < 10^{-6}) while running 11x faster. This demonstrates that the framework can produce genuinely novel algorithms, not just optimize existing components. Together, these results show that an LLM agent stepping through the scientific method can discover both new algorithms and new ensemble strategies that outperform prior solutions. The entire research program consumed 704M tokens on a single virtual machine with no GPUs

A decodability criterion predicts when hidden-state selection beats majority voting in large language models

Wed, 19 Aug 2026 04:00:00 +0000

Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by majority voting. Voting is unreliable on difficult questions, where the sampled answers share correlated errors, so the wrong answer can win and drawing more samples makes the decision worse. Selecting a candidate by reading a correctness signal from the model's hidden states is a promising alternative, but its accuracy varies across models and tasks, and no measure indicates when it can be trusted. In this paper, we propose CASE (Correctness-Axis SElection), a dynamic selection combiner that trains a linear gate on the answer-token hidden state and selects the highest-scoring candidate. Its main contribution is decodability, a leakage-free measure of how well the gate ranks a question's correct candidates above its incorrect ones, which predicts whether hidden-state selection will outperform voting. A conventional probe appears accurate only because of question-identity leakage, which vanishes under question-grouped evaluation. On held-out data, decodability predicts the accuracy gain of selection over voting with a Pearson correlation r=0.75 and a decision threshold near AUC=0.60. Across general and medical LLMs, CASE improves over voting by up to 19 points on medium-difficulty questions and 16.8 points on hard questions. Decodability depends on the aligned knowledge a model must recall, not on its scale, and its prediction transfers to an unseen scientific domain within 3.8 points. It thus provides a practical criterion, measurable in advance for a given model and task, for choosing between learned selection and majority voting.

PIKFNO: An Interpretable Neural Operator Based on Physics Informed Kernel Function

Wed, 19 Aug 2026 04:00:00 +0000

This work proposes a new interpretable neural operator framework, termed the Physics Informed Kernel Function Neural Operator (PIKFNO), which explicitly incorporates physics informed kernel functions derived from governing equations into the neural operator architecture. Unlike traditional neural operators such as DeepONet, which rely on deep networks to implicitly learn basis functions, PIKFNO constrains the trunk network through physics informed kernel functions, thereby aligning its operator structure with the kernel expansions used in meshless collocation methods. Two construction strategies are introduced: one learns kernel functions directly from data, where the learned kernel can be regarded as a nonsingular fundamental solution, while the other builds them through transformations of analytical fundamental solutions. Numerical experiments demonstrate that PIKFNO achieves high predictive accuracy with substantially improved interpretability and superior generalization under limited training data. The proposed framework offers a new pathway for developing efficient, physically consistent, and interpretable neural operators.

Reduced-Order Physics-Informed Neural Network with Adaptive Basis Refinement for Structural Identification

Wed, 19 Aug 2026 04:00:00 +0000

Physics-informed neural networks (PINNs) offer a flexible framework for forward and inverse problems, but their direct use in structural dynamics is limited by high dimensionality and model-form errors. Existing PINN-ROM couplings often rely on fixed reduced subspaces or assume complete physics. This work proposes a Reduced-Order Physics-Informed Neural Network (RO-PINN) with adaptive basis refinement, embedding reduced governing equations into the PINN loss via projection and updating the basis during training to realign the latent space with evolving parameters or residual restoring forces. Validated on a four-story steel frame with nonlinear hysteretic braces under sparse and noisy measurements, RO-PINN achieves parameter identification comparable to or more accurate than Bayesian model updating at lower computational cost, recovers unmodeled nonlinear restoring forces under incomplete physics, and jointly identifies residual forces and parameters in a unified framework.

Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication

Wed, 19 Aug 2026 04:00:00 +0000

This article investigates physics-informed and hybrid machine learning strategies that incorporate physics knowledge into experimental data-driven deep-learning models for predicting bond quality and porosity of fused filament fabrication (FFF) parts. Three strategies are explored: incorporating physics constraints into the loss function, using physics model outputs as additional inputs, and pre-training with physics model data then updating with experimental data. Eight combinations are tested. Results show that combining multiple strategies yields accurate models even with limited experimental data, ensuring physical consistency between bond quality and tensile strength.

Large-scale AI-Ready Data for Anti-Cancer Drug Response Modeling

Wed, 19 Aug 2026 04:00:00 +0000

Drug response prediction (DRP) models are an active area of research in pharmacogenomics, with growing potential to accelerate the identification of effective anticancer drugs. However, their predictive performance is often constrained by limited dataset scale and insufficient coverages of cancer and chemical spaces. In addition, inconsistent benchmarking practices hinder reliable comparison across models. Standardized frameworks, such as the Innovative Methodologies and New Data for Predictive Oncology Model Evaluation (IMPROVE) project, provide unified data schemas and evaluation protocols for consistent benchmarking, but improving model generalizability requires larger and more diverse training data. In this work, we substantially expand the IMPROVE benchmark through large-scale integration of pharmacogenomic data, primarily from PharmacoDB, together with additional smaller data sources. The expanded resource includes millions of drug response measurements, broader multi-omics coverage, and a major increase in chemical diversity, adding more than 50,000 compounds. To evaluate the impact of the new dataset compared to the original IMPROVE benchmark dataset, we trained DRP models using the two datasets and assess their prediction performance using a common test set and several evaluation strategies, including drug-blind, cancer-blind, and disjoint data splits. While cancer-blind performance remained comparable to the original benchmark, models trained on the expanded dataset showed consistent improvements in drug-blind and disjoint settings, indicating enhanced generalization to previously unseen compounds. These results position the expanded dataset as a community resource that provides a richer foundation for developing DRP models intended to aid in the discovery of novel anticancer drugs.

Discovery of novel magnetic Y-Mn-B compounds via advanced machine learning guided framework

Wed, 19 Aug 2026 04:00:00 +0000

Rare-earth transition-metal borides offer critical structural motifs for permanent-magnet design; however, the manganese-rich regions within these compositional phase spaces remain largely unexplored. In this work, we develop an advanced machine-learning-assisted discovery framework to explore Y-Mn-B ternary system. Starting from over one million hypothetical structures generated from known structures in databases, we filtered promising candidates by first applying graph neural networks to predict material stability, then using machine-learning-interatomic-potential to relax their structures, and finally validating the results with first-principles calculations. We identify 5 stable and near-stable Y-Mn-B phases along with 61 metastable compounds with the formation energy within 100 meV/atom with respect to the ternary convex hull. Among them, Y2Mn7B7 and YMn4B4 are structurally analogous to the previously synthesized $R_{1+\epsilon}Fe_4B_4$ 1D incommensurate composite chain compounds. In striking contrast to the strongly suppressed Fe moments reported, our first-principles calculations reveal that the predicted Mn-chain phases preserve sizable local Mn moments (approximately 1.1 $\mu_B$) and favored ferromagnetic ordering. Electronic structure analyses elucidate the microscopic origin of moment recovery via an enhanced exchange splitting driven by a Stoner-like instability. We also perform systematic Mn-Fe substitution to confirm the thermodynamic continuity and a monotonic enhancement of the macroscopic magnetization, from Fe to Mn. These findings indicate that targeted transition-metal substitution within a one-dimensional boride family can recover transition-metal magnetism, offering a physically interpretable route for designing new magnetic rare-earth transition-metal borides.

Probabilistic Inverse Modeling of Contaminant Transport via a Conditioned-on-Design Bayesian Physics Informed Neural Network

Wed, 19 Aug 2026 04:00:00 +0000

We address the inverse problem of reactive transport in heterogeneous porous media, where unknown model parameters must be inferred from sparse experimental observations. The problem is complicated by strong nonlinearities, spatial heterogeneity, and limited data availability. We propose a Conditioned-on-Design Bayesian Physics-Informed Neural Network (CoDe-BPINN), which combines a domain-decomposed PINN solver with a Bayesian inference network that learns the conditional distribution of model parameters given experimental design variables. The framework is trained by maximizing a physics-informed Evidence Lower Bound (ELBO), enabling simultaneous reconstruction of spatiotemporal concentration fields, probabilistic parameter estimation, and uncertainty quantification. We demonstrate the approach using laboratory experiments on contaminant transport through a multilayer porous column with an iodinated contrast medium. The model accurately reproduces breakthrough dynamics while revealing systematic parameter dependence on flow conditions. In particular, it identifies a nonlinear decrease in effective sorption capacity with increasing flow rate and porosity, consistent with kinetic limitations and reduced adsorbent mass. The Bayesian formulation also uncovers a strong negative correlation between sorption affinity and sorption capacity, quantifying the intrinsic non-identifiability of the inverse problem. CoDe-BPINN provides a robust framework for parameter inference and uncertainty quantification in data-scarce reactive transport problems.

Information Fusion and Machine Learning for Sensitivity Analysis Using Physics Knowledge and Experimental Data

Wed, 19 Aug 2026 04:00:00 +0000

This paper investigates physics-informed machine learning strategies for global sensitivity analysis (GSA) when both a physics-based model and experimental observations are available. Two machine learning techniques, deep neural networks (DNN) and Gaussian process (GP) modeling, are considered, and two strategies for incorporating physics knowledge are explored: enforcing physics constraints via loss functions, and pre-training and updating the model with simulation and experimental data. Four different models are built for each type, and uncertainties are included in Sobol indices computation. The DNN-based models yield smaller bounds on sensitivity estimates compared to GP-based models. The methods are illustrated with additive manufacturing and lake temperature modeling examples.

GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks

Wed, 19 Aug 2026 04:00:00 +0000

GEqTrain is a configuration-driven framework that separates dataset semantics, model composition, and training objectives, enabling reuse of equivariant graph neural networks across diverse 3D scientific tasks. Raw data are mapped to typed fields, and models, losses, and workflows are assembled declaratively via Hydra configurations. The framework is demonstrated on three tasks within one software stack: coarse-grained-to-atomistic backmapping of biomolecular systems, NMR chemical shift prediction in molecular solids, and equivariant generative modeling. It also introduces GEqDiff, a generative extension based on equivariant flow matching, which treats user-defined equivariant fields as first-class targets and jointly transports Cartesian positions and non-scalar node fields up to l=3. Validated on a synthetic benchmark inspired by protein secondary-structure motifs, GEqDiff shows high-fidelity joint reconstruction of fields with heterogeneous transformation properties. GEqTrain aims to make equivariant modeling more reproducible, extensible, and reusable.

Palomar – a registry of Lean verified mathematics

Wed, 19 Aug 2026 02:40:46 +0000

In recent months there has been a proliferation of AI-generated proofs of various old and new results, some of which have been formalized in the proof assistant language Lean. However, checking that a given Lean repository actually proves the claimed statement is somewhat non-trivial, especially for an audience which is not expert in the use of Lean: one has to first check that the claimed formal Lean statements have proofs that typecheck, that the proofs do not contain any “cheats” such as adding additional axioms, and that the formal statements also match (in a semantic sense) the informal description of the claimed results. To help bring some clarity to this situation, I am happy to announce that Palomar registry of Lean verified mathematics , which is an initiative incubated by the Lean FRO and by ICARM , is now open for submissions. I am serving in several roles on this registry, including on the scientific advisory board, together with Jeremy Avigad , Matthew Ballard , Jaume de Dios , Nestor Guillen , Bryna Kra , Kim Morrison , Ravi Vakil , and Akshay Venkatesh . A detailed motivation for Palomar can be found here , and further information about Palomar can be found here . A zeroth approximation of what Palomar intends to be is the analogue of a preprint server for Lean proofs. More precisely, Palomar (which is named after the astronomical observatory ) is a registry of external Github repositories (or more precisely, “snapshots” of such repositories, as represented by a specific Github commit) containing Lean code adhering to the current best practices for such formalizations, in particular containing A “challenge file” containing a short, human readable description in Lean of the results claimed. A “solution module” containing an (arbitrarily long) proof of the results claimed in the challenge file. A “ formalization.yaml ” file describing the results in informal language, and also containing a number of other relevant metadata and disclosures. (There are also some additional technical requirements for the repository which I will omit here.) If a snapshot of a repository is submitted to Palomar, it will check both (a) that the solution module typechecks and proves exactly the results claimed in the challenge file, and that (b) the informal description of the result in the formalization.yaml file appears to match the result claimed in the challenge f…

SPADE: Self-Play in Adaptive Synthetic Executable Environments

Wed, 19 Aug 2026 00:00:00 +0000

SPADE is a self-play RL framework where a single LLM acts as both an Environment Designer, which writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that learns to act in them. The designer optimizes the agent's regret signal (the gap between reward with and without privileged hints) to generate environments at the edge of capability while keeping them feasible. Key components include grounding the designer on documents from a large pretraining corpus and giving it accumulated environment memory. Scaling to 30B-parameter models, SPADE improves over the strongest fixed-environment baseline by +5.3 on average across eight held-out benchmarks, and lifts tool-use by +5.7 on BFCL-v4 multi-turn and +13.9 on ACEBench-Agent. This makes environment design a learnable component, advancing open-ended self-improvement.

VA-Judger: Reward Modeling for Joint Video-Audio Generation from Human Preference Feedback

Wed, 19 Aug 2026 00:00:00 +0000

Reward modeling for joint video-audio generation typically combines metrics for audio quality, visual fidelity, and synchronization, which evaluate perceptual dimensions separately and fail to capture the overall semantic and temporal coherence among text, video, and audio, encouraging reward hacking. To address this, the authors construct a large-scale human-preference dataset VAPref-10K with 9K prompts and 10.3K fine-grained paired comparisons from open-source generation models. They introduce VA-Judger-Bench for in-domain and out-of-domain evaluation, and propose VA-Judger, a chain-of-thought omni-reward model. VA-Judger first learns from pairs with clear quality gaps, then distills reliable preference explanations for harder near-quality comparisons, and finally performs dimension-wise reinforcement learning to decompose human feedback into individual quality dimensions for denser rewards. Experiments show VA-Judger outperforms metric baselines in predicting human preferences on both in-domain and out-of-domain evaluations, and using its rewards for post-training improves generation quality.

VITAL: Quantitative and interface-aware prediction of peptide–protein interactions

Wed, 19 Aug 2026 00:00:00 +0000

Nature Machine Intelligence, Published online: 19 August 2026; doi:10.1038/s42256-026-01291-z Chen, Wang, Li et al. introduce VITAL, a dual-channel deep learning framework that co-learns sequence and structural contexts to quantitatively predict peptide–protein interactions, map binding interfaces and estimate affinity.

Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis

Wed, 19 Aug 2026 00:00:00 +0000

Single-step retrosynthesis is central to computer-aided synthesis planning, but its one-to-many nature is poorly captured by single-answer benchmarks. To address this, the authors introduce Top-K prompting as a training and inference paradigm for diverse, plausible predictions. They compile CREED-CCV-2+USPTO-XL, an ultra-large-scale dataset of ~45.6 million verified reactions, to train C3LM (Chemistry Constraint-Consistent Language Model). Fine-tuning with ChemCensor-based and novelty-oriented rewards yields state-of-the-art performance on the OOD URSA-expert-2026 benchmark. Analysis of reaction uniqueness shows LLMs and conventional models explore complementary spaces, suggesting ensemble-based systems. This establishes Top-K plausibility-aware training as a practical direction for robust LLM-based synthesis planning.

FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis

Wed, 19 Aug 2026 00:00:00 +0000

Training terminal agents requires scalable executable supervision, yet synthesizing high-quality terminal tasks is challenging. Each task couples an instruction, an initialized environment, a reference solution, and an executable verifier; inconsistent assumptions can make tasks unsolvable or mis-evaluated. Multi-stage synthesis may discard original goals, dependencies, state transitions, and procedural constraints. We present FACET (Fine-grained Agentic Construction of Executable Tasks), a framework addressing information preservation and cross-artifact consistency. FACET reconstructs related agent skills into coherent scenarios, then realizes and repairs the execution environment before generating final artifacts. The container state serves as shared grounding for instruction, solution, and verifier, with execution-based validation and targeted repair fixing errors without regenerating valid components. FACET produces complex tasks with dense executable checks, and successful trajectories provide effective supervision. Fine-tuning models at multiple scales improves Terminal-Bench 2.1 performance, and analyses support environment-grounded construction for task validity and solution-verifier alignment.

Benchmarking Framework for Cross-Context Perturbation Signature Prediction

Wed, 19 Aug 2026 00:00:00 +0000

Perturbational transcriptomics is a powerful tool for understanding gene function and drug effects, yet predicting how perturbations manifest across different biological contexts remains a central challenge, limiting translation from model systems to clinically relevant tissues. Despite growing interest in this problem, benchmarking efforts have been hindered by inconsistent evaluation tasks, heterogeneous metrics, and limited assessment across perturbation types and biological systems. Here, we introduce a benchmarking framework for cross-context perturbation-signature prediction (a task we define as signature recontextualization), grounded in explicit definitions of the prediction task, target-data availability, and evaluation metrics centered on signature recovery. The framework evaluates prediction performance across three target-context data regimes: (1) control only, where only control profiles from the target context are measured; (2) low coverage, where a limited subset of perturbations in the target context are measured; and (3) high coverage, where most perturbations in the target context are measured. This design enables systematic assessment of how prediction performance depends on target-context sample size while providing a standardized basis for comparing methods. We evaluate newly developed projection-based (projectCor) and network-based (netProp) methods alongside deep learning-based foundation models (scGPT, STACK) and statistical baselines. The benchmark spans four diverse perturbational datasets: CRISPR knockdowns and drug perturbations in cell lines, plus in vivo chemical perturbations in rat tissues from DrugMatrix, extending evaluation beyond isolated cell-line models to tissue-level responses. Across tasks, projection and network propagation approaches show strong flexibility across perturbation types and biological contexts, and in several cases match or exceed the performance of deep learning and foundation models, suggesting that model complexity does not inherently improve cross-context generalization. We further show that perturbation predictability varies substantially with pathway conservation, transcriptional response strength, and baseline similarity between source and target contexts. All datasets, methods, and evaluation utilities are released as an open-source R package (sigRecon), providing a foundation for reproducible…

Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts

Wed, 19 Aug 2026 00:00:00 +0000

This paper presents a multi-dimensional analysis of human-like behaviors in large language models (LLMs), focusing on their prevalence, potential effects, and controllability. Using LLM-as-a-judge and human evaluation, the authors analyzed 21,000 multi-turn conversations from four widely used models: gpt-4o, gpt-4.1-mini, claude-sonnet-4.6, and gemini-2.5-flash. They found that human-like behaviors are pervasive but vary across models and user factors such as conversation goals and user profiles. Human evaluators judged self-referential and relationship-building behaviors as less appropriate from LLMs than from humans, but boundary-maintaining behaviors as more appropriate from LLMs. The study also demonstrates that system prompting can control these behaviors, though careful evaluation is required to avoid unintended effects. The findings have implications for responsible LLM design and evaluation.

Explainable Clinician-Supervised AI as an Implementation Framework for Cardiovascular-Kidney-Metabolic Population Health: Synthetic Data Validation of the CHAPERONE-CKM Framework

Wed, 19 Aug 2026 00:00:00 +0000

Abstract Background: Cardiovascular-kidney-metabolic (CKM) syndrome is an increasingly prevalent multisystem condition associated with morbidity, fragmented care, recurrent hospitalization, and rising healthcare costs. While cardiovascular risk models estimate future disease risk, fewer frameworks support multidisciplinary CKM care, clinician decision-making, and population health management. Synthetic data environments can assess implementation readiness while preserving privacy. Methods: We validated the explainable, clinician-supervised CHAPERONE-CKM framework using a reproducible synthetic cohort of 10,090 simulated patients with 128 demographic, laboratory, imaging, treatment, and healthcare utilization variables across the CKM continuum. Synthetic data generation was separated from framework evaluation through probabilistic modeling and independent validation to reduce deterministic relationships. The framework generated CKM stage assignments, implementation priorities, clinician-readable rationales, multidisciplinary referral pathways, and guideline-directed therapy prompts. Evaluation focused on implementation readiness, consistency, calibration, subgroup stability, fairness, workflow simulation, and explainability. Results: The synthetic population represented CKM-related conditions including diabetes (52%), hypertension (65%), chronic kidney disease (20%), heart failure (32%), and prior CKM hospitalization (27%). The framework showed stable internal behavior across demographic and clinical subgroups, favorable calibration, and biologically plausible prioritization of advanced CKM disease. Workflow simulations suggested earlier identification of patients suitable for multidisciplinary review, therapy optimization, and coordinated care compared with reactive workflows. Traditional performance metrics supported framework behavior but were treated as secondary evidence rather than proof of clinical effectiveness. Conclusions: In a synthetic validation environment, the CHAPERONE-CKM framework demonstrated implementation readiness, transparent decision pathways, and compatibility with multidisciplinary CKM population health management. These findings are an early translational milestone, not clinical validation, and support external validation, prospective implementation studies, and Learning Health System integration to assess effects on care delivery,…

How Claude is accelerating protein design and analytical chemistry

Tue, 18 Aug 2026 14:45:00 +0000

In this post, we share two results that show how Claude can help life scientists increase the pace of their research. In the first, we tested Claude’s ability to design protein binders from scratch, a key step in creating protein-based drugs that has historically taken a specialist weeks or months per target. In the second example, we evaluated whether Claude can accelerate chemical analysis. Claude Opus 5, a generally available model, was given NMR and LC-MS data (the data that allows chemists to assess the identity and purity of the compounds they work with).

When a model reads a drug's class from its name—not its knowledge

Tue, 18 Aug 2026 08:00:00 +0000

When a model reads a drug's class from its name—not its knowledge August 18, 2026 Ai2 People increasingly bring health and medication questions to AI models, often naming specific drugs. But a model can sound knowledgeable without knowing much about the drug itself. Many drug names encode their class through affixes – often suffixes like -pril, -olol, and -azoline – giving a model a shortcut: infer the answer from the name rather than from learned facts about the medication. In a new study of Olmo 3 accepted to EMNLP, researchers found that 51–59% of the drugs they tested elicited little sign of drug-specific knowledge, while another 12–18% appeared affix-driven. By tracing those behaviors back to the model’s open training data, the team showed not only that the shortcut exists, but where it comes from—and why that matters when models answer health questions. Kaijie Mo at the University of Texas at Austin, working with collaborators at Northeastern University and MD Anderson Cancer Center, led the study. The team set out to determine when a model was relying on the morphology of a drug’s name rather than drug-specific knowledge—and to trace that behavior back to the data the model was trained on. They chose Olmo 3 because the analysis required a fully open model with public weights and training corpora, documentation spanning pretraining through post-training, and released intermediate checkpoints. "Olmo 3 was particularly valuable because of its openness and because its training corpus includes substantial medical content," says Mo, "allowing us to connect behavior, mechanisms, and training data. Open models make it possible to move from observing a behavior to understanding its cause." On Olmo 3 7B Instruct, the team built a diagnostic that isolates three things a model might draw on when it answers drug-related health questions: the affix of a drug’s name (the shared ending, like -pril), the stem (the part of the name unique to a drug), and real knowledge of a specific drug. They swapped the stem, the affix, or both for invented, meaningless ("nonce") strings and measured how the model's answers shifted. The diagnostic found that Olmo 3 knows less about drugs than it lets on. Depending on the evaluation format, about 12–18% of drugs appeared affix-driven. For a much larger 51–59%, the model showed no real sign of knowing a particular drug—its answer to a…

Prediction Inference of Time Series with Standard ReLU Deep Neural Networks

Tue, 18 Aug 2026 04:00:00 +0000

We propose a methodology based on the standard ReLU Deep Neural Networks (DNN) to make predictions and quantify their uncertainty. Classically, people rely on linear, non-linear, or non-parametric kernel methods to fit and then predict the time series. As the universal approximation ability was revealed for DNN, its application has become more and more popular for prediction tasks in various scientific areas. However, the corresponding uncertainty quantification has not been studied thoroughly. Particularly, the uncertainty in prediction will consist of two parts: (1) the future variability; (2) the estimation variability within training data. To capture both variabilities, we build the so-called pertinent prediction interval (PPI) with the DNN model estimator. We first explore the consistency property of the DNN estimator with beta-mixing dependent data. Subsequently, we show that the implied forward bootstrap series is still beta-mixing and possesses the same stationary distribution as the original time series in probability, which is a key condition to enable the PPI. Lastly, the desired PPI is built after imposing minimal conditions on the limiting distribution of predictive roots. Simulations and real-data analysis are deployed to challenge our approach with standard non-parametric methods.

BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization

Tue, 18 Aug 2026 04:00:00 +0000

Bayesian optimization (BO) is a sample-efficient, surrogate-based approach to black-box optimization (BBO), but its evaluation remains dominated by synthetic functions and hyperparameter optimization (HPO) tasks that are typically low-dimensional and single-objective. Engineering design poses a substantially different regime: problems are physics-based, often high-dimensional, constrained by requirements such as cost and manufacturability, and may involve multiple objectives or mixed variables. To close this benchmarking gap, we introduce BOCoDe, an open-source, PyTorch-native benchmark comprising 307 BBO problems, including 159 engineering design tasks and widely used synthetic and HPO benchmarks. Each problem includes cited provenance and machine-readable metadata that supports programmatic discovery, including by LLM-based agents, and all tasks are exposed through a unified API compatible with open-source BO libraries. We evaluate 31 BO and evolutionary algorithms across five problem classes spanning single- and multi-objective optimization, constrained and unconstrained settings, and mixed-variable search spaces. Analyses of problem structure show that engineering tasks uniquely span constrained and multi-objective settings that synthetic and HPO suites rarely cover, while embeddings from a tabular foundation model separate them most clearly from HPO tasks. Algorithm rankings also vary substantially across domains; in several problem classes, rankings obtained on standard benchmarks do not transfer to engineering tasks. BOCoDe establishes a reproducible and extensible foundation for developing and evaluating BO methods that better reflect the demands of engineering design. Code & data can be found at https://github.com/rosenyu304/BOCoDe

Unlocking Multi-Component Bulk-Materials Molecular Dynamics with a Small-Footprint Machine Learning Interatomic Potential

Tue, 18 Aug 2026 04:00:00 +0000

Bulk materials, as opposed to nanomaterials, require molecular dynamics (MD) simulations on a large spatial scale (~10^9 atoms or more) to adequately capture their atomic-scale physical properties. Previously, the introduction of machine-learning interatomic potentials (MLIPs) has extended MD to this scale, but even single-component bulk systems require tens of thousands of GPUs on high-end supercomputers. However, multi-component bulk MD simulations remain barely achievable, as the HBM footprint of existing MLIPs - already substantial for single-component systems - grows explosively in multi-component scenarios. This paper proposes an MLIP with a small HBM footprint - less than 3% that of existing MLIPs - unlocking multi-component bulk MD using only hundreds of GPUs. This is achieved by first identifying feature vectors and intermediate tensors as the two primary contributors to HBM footprints in existing MLIPs. To address these two sources, the dimensionality of the feature vectors has been reduced by introducing physical and chemical knowledge, and intermediate tensors have been eliminated by aggressively fusing all kernels into a single mega-kernel. In evaluation, the proposed MLIP has used 144 NVIDIA A100 GPUs to perform MD simulations on a 6-component bulk system with 1.14x10^9 atoms, while previously such MD simulation spatial scale has been restricted to unary systems and typically achieved on high-end supercomputers equipped with tens of thousands of GPUs.

Discovering Physically Interpretable Mathematical Expression for Predicting CO2 Adsorption in Metal-Organic Frameworks via Machine Learning-Symbolic Regression

Tue, 18 Aug 2026 04:00:00 +0000

This work presents a machine learning-symbolic regression (ML-SR) strategy to develop a physically interpretable formula for predicting low pressure CO2 adsorption capacity in hypothetical metal-organic frameworks (hMOFs). Four ML models were trained on a small dataset of 1,000 samples, and five key descriptors—largest cavity diameter, pore limiting diameter, void fraction, gravimetric surface area, and number of hydrogen atoms—were identified through SHAP and feature importance analyses. Symbolic regression derived a concise adsorption formula, Q=aA, where a is an adsorption baseline (mmol/g) and A is a dimensionless adsorption number incorporating four structural descriptors. The authors interpret A as the ratio between adsorption binding force and diffusion driving force, revealing joint effects of pore topology and surface chemistry. Validation against 137,652 hMOFs achieved over 70% prediction accuracy for 62,448 structures, confirming applicability within defined structural and operational ranges. Unlike black-box ML models, this physics-guided expression enables efficient prediction and clearer mechanistic insight.

Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra

Tue, 18 Aug 2026 04:00:00 +0000

Automated structure elucidation from routine spectroscopy remains a major challenge. Researchers introduce MACROS, a multi-agent system that emulates expert iterative hypothesis testing. Trained on 100 million simulated and 1.6 million experimental spectra-molecule pairs, it natively supports arbitrary combinations of routine spectroscopic techniques and achieves unprecedented zero-shot generalization to diverse real-world samples, correctly identifying synthetic compounds, natural products, and metabolites above 500 Da with 1D NMR. Remarkably, MACROS spontaneously recovers textbook spectroscopic correlations from unassigned data and exhibits emergent chemical intuition such as a ring-first parsing preference, learning fundamental principles rather than memorizing database patterns. When collaborating with chemists, it delivers sixfold faster and 40% more accurate elucidation, establishing a scalable foundation for fully automated structure elucidation and accelerating molecular discovery toward autonomous laboratories.

Large Language Models Show Metacognitive Sensitivity in Medical Reasoning

Tue, 18 Aug 2026 04:00:00 +0000

Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty. We developed a controlled, psychophysics-inspired clinical benchmark to test diagnostic choice and confidence behavior in a medical LLM. The benchmark focused on probable Alzheimer-type neurocognitive disorder (AT-NCD) versus depression-related cognitive impairment (DRCI). We generated 45 synthetic vignettes varying evidence strength, conflicting evidence, and missing information. Each vignette was presented under three prompt variants, yielding 135 trials. In a pilot run with gpt-4.1-nano, all trials produced valid structured outputs. Across forced-choice trials, diagnostic accuracy was 93.5%, mean confidence was 78.4%, and AUROC2 was 0.876. Confidence increased with evidence distance from the diagnostic boundary, decreased when information was missing, and remained higher on correct than incorrect trials after adjustment for evidence strength and prompt format. These findings indicate partial metacognitive sensitivity rather than globally uninformative confidence. However, errors clustered in moderate, conflicting AT-NCD cases, where the model shifted toward DRCI and retained more confidence than empirical accuracy justified. Model comparison suggested that confidence quality should be measured directly rather than inferred from benchmark accuracy or model capability alone. This study establishes a reproducible framework for evaluating evidence sensitivity, metacognitive sensitivity, and localized calibration failure in medical LLMs.

FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment

Tue, 18 Aug 2026 04:00:00 +0000

AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs. While reporting Floating Point Operations (FLOPs) is a traditional approach for assessing computational costs, the relationship between FLOPs and execution time is not straightforward, as layers with the same number of FLOPs may not have the same execution time because some operations are more easily parallelized than others. This paper sets out to replicate the original experiments from a study that proposed the $\alpha-FLOPs$ estimation formula to verify whether the results remain applicable on newer, more powerful hardware. During the replication process, we identify limitations in the replication materials provided by the original study, including a lack of specific dependency details and transparency regarding regression data. Our results validate the thesis that raw FLOPs alone are not an appropriate metric for execution time, as spatial dimensions remain more easily parallelized than kernel dimensions. However, fine-grained measurements reveal that the relationship is much less straightforward than previously shown, with newer hardware exhibiting instabilities and discontinuities in execution time, including jumps and oscillations, that the $\alpha-FLOPs$ formula generally underestimates. Ultimately, this work validates the empirical findings from the original study but shows negative results when applying the $\alpha-FLOPs$ estimation. We also highlight the critical need for complete and accurate replication packages for research on hardware-dependent efficiency assessment and provide a complete replication package for our implementation to facilitate further study.

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

Tue, 18 Aug 2026 00:00:00 +0000

We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimization. PTXBench measures functional correctness, whether selected target instructions execute at runtime, and speedup over frontier libraries across GEMM and attention workloads on H100 and B200 GPUs. Our evaluation shows that architecture-specific PTX capability remains uneven: success rates fall substantially on complex attention backward workloads, and executing the target instructions does not necessarily translate into competitive performance. No evaluated model consistently matches frontier libraries across the suite. We further adapt Qwen3.6-27B using supervised fine-tuning. Repair-conditioned training improves several tasks, but generalization remains uneven; data coverage, balance, and the quality of the reasoning teacher matter in addition to dataset size. PTXBench provides an auditable testbed for measuring and improving LLMs' ability to exploit evolving GPU architectures.

Chain-of-Experience for Continual LLM Improvement

Tue, 18 Aug 2026 00:00:00 +0000

This paper introduces Chain-of-Experience (CoE), a paradigm enabling large language models (LLMs) to improve at test time through iterative interaction with self or environmental feedback. Across math, coding, and knowledge domains, using 8 LLMs including GPT-5, Gemini-2.5 Pro, and Claude-4.5 Sonnet, the authors show that leveraging iterative experience consistently outperforms feedback-free baselines, with self-feedback alone yielding substantial gains, a 5.6% overall improvement, and 19% lower API cost. Combining complementary feedback channels (e.g., model and correctness signals) brings additional gains, and CoE achieves higher accuracy per token than existing test-time strategies. The study finds a positive correlation between base ability and improvement capacity, and robustness under weak or spurious feedback, with most gains emerging early in iterations.

ASI-Bench: At the Dawn of Artificial Superintelligence

Tue, 18 Aug 2026 00:00:00 +0000

ASI-Bench is the first benchmark to jointly evaluate AI systems' capabilities in innovative exploration and autonomous scientific execution across general research domains, and the first to progressively withdraw human methodological guidance within the same project to test how far AI can proceed independently. Built by over 40 experts with 31,000+ human hours, it includes 60 project-level tasks across 11 scientific domains. Across 18 state-of-the-art agent-model configurations, average scores drop from 50.91 with full guidance to 29.10 with only method specified, and 26.62 when agents must determine methods themselves, showing current systems heavily depend on human guidance and are far from autonomous end-to-end research. The benchmark is open for global contributions.

LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents

Tue, 18 Aug 2026 00:00:00 +0000

LEGO-RL is a framework that bridges native coding-agent harnesses with scalable policy-gradient optimization without modifying their internal control flow. It ensures faithful optimization via in-process LLM proxying for token-level alignment, reliable execution through scalable sandbox orchestration with image caching and stage-wise defenses against reward hacking, and observable training via an integrated plugin and Live UI. When training the sparse MoE model Qwen3.5-35B-A3B with GSPO across three native harnesses, LEGO-RL improved performance on SWE-bench Verified: OpenHands SDK from 64.0% to 70.4%, Claude Code from 62.4% to 68.2%, and OpenCode from 57.2% to 66.6%, while maintaining a rollout-training probability correlation above 0.99.

LifeSciBench: Evaluating Language Models on Realistic, Expert-Level Tasks in the Life Sciences

Tue, 18 Aug 2026 00:00:00 +0000

We introduce LifeSciBench, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work. The majority of existing life sciences benchmarks have a narrow scope or are purely knowledge-based, and therefore fail to capture the complexity of real-world research, which often involves ambiguities and requires the accurate execution of multiple dependent judgment calls. Additionally, almost all existing benchmarks span at best a small collection of subdomains within the life sciences; there is at present no existing life sciences benchmark with both the requisite breadth and depth required to convincingly measure proficiency in real-world professional research settings. LifeSciBench addresses this gap by spanning seven representative scientific workflows and seven life science domains, with each constituent task paired with a human expert-written rubric. Across five frontier and domain-specialized models, GPT-Rosalind performs best, with a task-weighted mean normalized rubric score of 0.576 and a task-weighted response pass rate of 36.1% (response-level values are first averaged within each task, and the resulting task-level values are then averaged with equal weight). LifeSciBench remains unsaturated, with 171 tasks (22.8%) having no observed passing response from any evaluated model and 261 tasks (34.8%) having a best-model pass rate below 20%. LifeSciBench therefore serves as a high-resolution evaluation of practical scientific reasoning and operational decision-making in the life sciences.

SemComp-Bench: Benchmarking Semantic Task Completion in Video Generation

Tue, 18 Aug 2026 00:00:00 +0000

We introduce Semantic Task Completion Video Generation, an outcome-oriented task requiring both the achievement of the intended outcome and semantic grounding, which characterizes the correspondence between the reference image and the generated outcome in terms of high-level task-relevant semantics. Evaluation focuses on the generated outcome and does not require a complete sequence of intermediate steps or appearance consistency. We construct SemComp-Data, an evaluation dataset covering six domains, with each instance consisting of a reference image, a detailed instruction, a brief instruction, and an outcome-centric video clip. A scalable four-stage curation pipeline converts raw videos into standardized instances. We further propose SemComp-Bench, an evaluation protocol using a vision-language model (VLM) to answer structured binary questions, reporting OA Score and GR Score for Outcome Achievement and Generation Reliability. Experiments on representative models show that achieving intended outcomes while maintaining task-relevant semantic grounding remains challenging.

Agent Lightning v1.0: Toward Harnessed Agentic RL

Tue, 18 Aug 2026 00:00:00 +0000

Modern agents operate within agent harnesses that manage tools, context, and control flow. The original Agent Lightning proposed a disaggregated architecture connecting arbitrary agents to RL training via an LLM endpoint proxy—a paradigm termed harnessed agentic RL. v1.0 is a lightweight framework of about 3,500 lines of code, supporting arbitrary harnesses and offering a reproducible pipeline for coding-agent RL. With only 6K training examples and modest compute, it improves Qwen3.5-9B on SWE-bench Verified from 41.8% to 56.4%, a 14.6-point absolute gain.

Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements

Tue, 18 Aug 2026 00:00:00 +0000

Reinforcement learning (RL) has shown promise in single-turn LLM fine-tuning, but long-horizon agentic reasoning introduces increasingly branching interactions and sparse rewards, exposing limitations: heavyweight backpropagation-based training stacks and harder credit assignment. This paper argues that evolution strategies (ES) are a better choice, offering model scalability (full-parameter optimization with inference-level GPU memory), flexibility (composable with prompt-space evolution), and long-horizon scalability (trajectory-level attribution). They propose Agentic ESOpt, a full-parameter fine-tuning framework with online reward-weighted updates and a cosine decay schedule for perturbation scale. On WebArena-Lite, full-parameter optimization of Qwen-3.5-27B improves the No Skill baseline by 6.69%. In test-time automatic heuristic design, it improves its matched baseline in 28 of 36 settings.

Will you be wearing an exoskeleton in ten years? The tech that could change how we move

Tue, 18 Aug 2026 00:00:00 +0000

Nature reports that robotic supports, once designed for clinical use, are now being developed as cheaper, smaller devices aimed at everyone — from people with ageing knees to twenty-somethings seeking to climb mountains.

A Digital Holography-Based Dataset of Red Blood Cells for Machine and Deep Learning Analysis of Hereditary Anemias

Tue, 18 Aug 2026 00:00:00 +0000

A study published online in Scientific Data on August 18, 2026, presents a dataset of red blood cells captured using digital holography, designed for machine and deep learning analysis of hereditary anemias. This resource provides a standardized collection to support the development and application of algorithms for identifying and classifying morphological features associated with these conditions, facilitating computational research in the field.

Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery

Mon, 17 Aug 2026 21:05:41 +0000

Google Research demonstrates the feasibility of PhotoScan, a deep learning approach that estimates body composition from smartphone photos to predict insulin resistance with accuracy comparable to DXA scans in a clinical research setting. Insulin resistance, a critical driver of metabolic disease, often precedes type 2 diabetes by years, and a HOMA-IR score above 2.9 indicates insulin resistance. The study highlights that additional body composition biomarkers, such as the Android-to-Gynoid fat ratio and visceral-to-subcutaneous fat ratio, provide deeper clinical insights than total body fat alone. While DXA scans are the gold standard for body composition measurement, they are expensive, require specialized infrastructure, and involve low-dose radiation, making PhotoScan a promising alternative for everyday screening.

AUGURY: A Tiny Model for Soil Health Insights

Mon, 17 Aug 2026 13:44:59 +0000

Developer @RightSideOfAI built AUGURY, an open-source AI pipeline that uses the MiniCPM5-1B model to turn weed observations into meaningful soil-health insights, avoiding reliance on large cloud models.

AAMAS 2026 Blue Sky Award Winner: Foundation World Models for Agents in Changing Environments

Mon, 17 Aug 2026 12:04:55 +0000

Florent Delgrange won the Best Blue Sky Paper Award at AAMAS 2026 for his work 'Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments.' The paper proposes a research agenda that merges reinforcement learning and reactive synthesis into a single loop: a foundation world model is learned from experience but structured for a verifier to reason about. As the agent learns a policy, it updates the model, measures the reliability of its abstraction, and checks whether the policy still satisfies its specification. The verifier's feedback can reject unsafe updates, request data from uncertain regions, or trigger model revision. This matters because real environments change, and in multi-agent systems, every adapting agent alters the environment perceived by others. Reliability cannot be a one-time certificate at deployment; it must be maintained as the agent continues learning.

Data-driven techniques for translational neuroscience and personalized neuro-health

Mon, 17 Aug 2026 04:00:00 +0000

Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible, neuronal loss has already occurred, creating an urgent need for quantitative tools that can detect subtle, early, and individual-specific brain changes from neuroimaging data. This review surveys a broad and rapidly evolving toolkit of data-driven techniques for translational neuroscience and personalized neuro-health, organized around four complementary methodological pillars. Throughout, we emphasize how these methodologically diverse approaches converge on a common translational goal: personalized, mechanistically grounded, and clinically actionable models of individual brain health, and we close by discussing the principal open statistical, computational, and clinical challenges that remain.

Modular Cognitive Architecture Emerges in Large Language Models

Mon, 17 Aug 2026 04:00:00 +0000

The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about other minds, and reasoning about the physical world. Is this modular organization a fundamental principle of how intelligent systems must be built, or an evolutionary accident specific to biological brains? Here, we test whether a similar organization emerges in Large Language Models--another class of intelligent systems created through a very different optimization process. Using circuit analyses across N=46 tasks spanning four cognitive domains (language, formal reasoning, social reasoning, physical reasoning), we find that LLMs develop a modular architecture that mirrors the human brain: tasks drawing on the same network in humans recruit overlapping neurons in LLMs, whereas tasks drawing on different networks recruit distinct neurons. The convergent emergence of modularity in brains and neural networks suggests that it may be a fundamental property of intelligent systems.

Knowledge-Data Dual-Driven Reinforcement Learning for Autonomous Vehicle Control in Mixed Traffic

Mon, 17 Aug 2026 04:00:00 +0000

In mixed traffic, decision-making for autonomous vehicles faces three interrelated challenges: physics-based priors fail to capture latent interactive intentions and diverse driver behaviors; abrupt maneuvers cause non-stationarity, leaving long-tail safety events under-explored; and hybrid action spaces destabilize unified RL training due to temporal scale differences between continuous car-following and discrete lane-changing. To address these, we propose Knowledge-Data Dual-driven Reinforcement Learning (KDDRL). A conditional deep generative model synthesizes intention-aware future trajectories; a knowledge-data dual-driven paradigm fuses probabilistic data-driven insights with physical constraints to guide safe exploration; and a coupling module compresses intention-aware trajectories and physical constraints into compact shared embeddings, enabling asynchronous multi-timescale optimization. Evaluations on dataset-calibrated simulations show KDDRL effectively handles intention uncertainty, accelerates training convergence, and outperforms baselines in safety, efficiency, and comfort.

No Universal Signal Predicts Sample-Level LLM Regression under Version Updates

Mon, 17 Aug 2026 04:00:00 +0000

Frontier LLMs are updated frequently and typically outperform their predecessors in aggregate. But aggregate gains say little about individual samples: an update can still cause sample-level regression, where a response correct under the old model becomes incorrect under the new one. This paper studies how to predict such regressions from signals available at inference time. We compare single-model signals (confidence, logit margin, attention entropy) against cross-version signals (output KL divergence, likelihood drift, token-level KL, representation drift) under a unified added-value test that isolates each signal's gain over a confidence baseline. Across six benchmarks in three task families (multiple-choice question answering, or MCQ; math reasoning; code generation) and six model update pairs, we find that (1) signal effectiveness is task-dependent: confidence is strongest on MCQ and simpler math, while likelihood/KL signals give the most frequent gains on harder math and code; (2) no signal is universally best across model updates either; and (3) some cross-version signals stay informative even when confidence fails, including without labels, which supports a proof-of-concept selective fallback that routes high-risk samples back to the old model. Practitioners can use these task-level patterns to choose which regression signal to trust for a given update. Code is available at https://github.com/jiashengsally/llm-regression-signals.

Don't Claim Benchmark-Oriented Optimization Improves General Coding Capability -- Diverse Evaluation Is Required

Mon, 17 Aug 2026 04:00:00 +0000

Post-training papers, model cards, and blog posts often treat scores on a small set of coding benchmarks (e.g., SWE-bench and LiveCodeBench) as evidence of broad coding capability, both for research artifacts and user-facing systems. We argue that optimization for these benchmarks leads to measuring task-specific performance, creating a meaning gap between measured scores and claims of general coding ability. We examine this gap with a Django-based case study benchmark suite we create. Evaluating foundation models and checkpoints post-trained on SWE-bench trajectories, we find that benchmark rankings frequently fail to generalize. Post-trained checkpoints show little cross-task transfer, and SWE-bench optimization yields limited or no gains on our tasks or on LiveCodeBench. Similarly, fine-tuning on individual Django modalities fails to transfer. We conclude that a small number of benchmarks is insufficient for evaluating diverse models under benchmark optimization pressure. We encourage the community to use differentiated evaluation - holistic assessment for frontier models, multi-task suites for research, and human-in-the-loop studies for narrow task applications. Finally, we argue for creating a capability taxonomy and sustained benchmark maintenance, rather than one-off benchmark releases. Without reliable evaluation standards, engineers and researchers using LLMs and agents have to rely on insufficient evidence to make research, development, and deployment decisions.

Offline Deep Q* Estimation with Diffusion Models

Mon, 17 Aug 2026 04:00:00 +0000

In offline reinforcement learning, estimating the optimal action-value function Q* can be formulated as solving the optimal Bellman equation based solely on offline observations. A fundamental challenge is that the reward function and transition kernel are unknown, so the optimal Bellman operator is not directly observable. To address this, we propose a novel framework that decouples operator estimation from value function learning. We first formulate conditional diffusion models to estimate the reward law and transition kernel, inducing a data-driven approximation of the optimal Bellman operator. We then plug these estimators into the Bellman equation and obtain a deep estimator of Q* by minimizing the empirical Bellman residual over a neural network function class. Theoretically, we establish sharp nonasymptotic convergence rates for learning the optimal Bellman operator via end-to-end analysis of conditional diffusion estimation in total variation distance, and then derive oracle value-stage rates for excess Bellman residual risk and L2 convergence rates under a concentrability condition. Our analysis does not rely on completeness assumptions. Extensive numerical experiments demonstrate effectiveness.

Benchmarking data-driven material models on the classic Treloar dataset

Mon, 17 Aug 2026 04:00:00 +0000

Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling paradigms. But with a growing number of machine-learning-based approaches available, how do they compare in practice? In this paper, we use the classic experimental data of Treloar to benchmark popular frameworks for hyperelasticity: (Generalized-Invariant) Constitutive Artificial Neural Networks, Physics-Augmented Neural Networks, (Adaptive) Material Fingerprinting, and Efficient Unsupervised Constitutive Law Identification & Discovery. We compare their fitting performance, computational cost, hyperparameter sensitivity, and ease of implementation. Furthermore, we discuss the trade-offs between predictive accuracy and model complexity. The latter is assessed by quantifying both the number of material parameters in the discovered models and the computational time required to evaluate the constitutive model and its derivatives. The results show that all methods can reproduce the benchmark data remarkably well. Rather than identifying a single winner, we highlight the strengths and limitations of each approach and provide practical guidance for their use. The source code for all six methods, including the training and comparison scripts, as well as all results and data used in this study, is publicly available via https://doi.org/10.5281/zenodo.21915635.

Active Perception for Embodied Disambiguation

Mon, 17 Aug 2026 04:00:00 +0000

Natural language offers robots a flexible task interface, but target ambiguity in embodied environments arises not only from user intent; it can also result from missing task-relevant physical evidence in the current observation. Existing interactive disambiguation methods primarily obtain additional information by asking the user, whereas occlusion, restricted viewpoints, unreadable text, and unobserved targets require the robot to actively change its observation. We propose an active-perception framework for embodied target disambiguation that uses active observation as the backbone for information acquisition and uses a vision-language model to decide, based on accumulated visual evidence and interaction information, whether to continue observing, request clarification, or complete target selection. Active observation can both directly recover missing discriminative evidence and reveal object names, labels, and semantic attributes, thereby improving user clarification when it remains necessary. Real-robot experiments show that the framework combines physical information acquisition and user-intent clarification within a unified embodied disambiguation process.

Unifying Graph Neural Networks Through a Common Layer Equation

Mon, 17 Aug 2026 00:00:00 +0000

This paper introduces a common layer equation that unifies many graph neural network architectures via seven components: update domain, channel set, propagation bank, per-channel message maps, channel-fusion operator, ego/residual map, and update map. The central factorization separates where information moves (propagation bank) from what moves (message maps). Function-valued fillings extend the equation to local message passing, attention, spectral filtering, global communication, relation-specific channels, higher-order domains, and geometric messages. Worked reductions and component assignments cover seven nonexclusive architectural families, organizing over 200 architectures in a common design space. The framework enables component-wise comparison and generation of structurally consistent architectures, and connects propagation choices to oversmoothing, oversquashing, heterophily, and expressivity. It also exposes the empirical inverse problem of mapping measurable graph and task properties to validated component choices. Theoretical insights include operator support bounds and conditions for one-layer global mixing under stated hypotheses.

Cross-Model Memory Transfer via Target-Side Reader Adaptation

Mon, 17 Aug 2026 00:00:00 +0000

Methods for improving knowledge use in LLMs typically fall into non-parametric retrieval or parametric adaptation. Engram-style hashed memory stores learned information in an external, addressable table, consumed through a small learned reader. This study investigates what matters more when such memory is transferred across backbones: the frozen memory or the target-side reader, via cross-model frozen-memory extraction. Ablations show that learned memory content and correct addressing both matter, but the transferred table becomes useful only through a reader aligned to the target model. In downstream QA tasks, a dual-layer, four-branch reader nearly closes the same-model versus cross-model reuse gap, achieving an average score of 38.8. When the provider reader is directly compatible, the frozen artifact provides utility without target-side training, and optional adaptation yields further improvement. These results suggest Engram can serve as a reusable external knowledge artifact with a compatible reader interface.

Towards Real-Time and Adaptable LiDAR Scene Completion

Mon, 17 Aug 2026 00:00:00 +0000

We present RapidLiDAR, a LiDAR scene completion method that treats initialization as a learned, data-driven component. An adaptive initialization module predicts spatially varying displacement for each partial input point, avoiding manual noise tuning. A multi-scale reconstruction module refines point positions using voxel and BEV feature maps. On SemanticKITTI and KITTI-360, our method achieves state-of-the-art completion performance while completing a full scene in 0.1 seconds—2.3 times faster than the fastest prior method—matching the 10 Hz acquisition rate of typical automotive LiDAR sensors.

PixRestore: Unified Image Restoration via Pixel Diffusion Transformer

Mon, 17 Aug 2026 00:00:00 +0000

PixRestore is a VAE-free pixel-space Diffusion Transformer for unified image restoration (UIR), performing flow matching directly on patchified pixels to preserve fine details. It adapts to degradations by predicting layer feature reliability via LQ-HQ DINO similarity, fusing reliable layers as dense conditioning while supervising less reliable layers to remove degradation. Trained on a large-scale corpus and finetuned into a one-step generator with DINO-based adversarial objectives, PixRestore achieves the best overall fidelity, perceptual quality, and robustness among competing UIR models with only about 50M parameters and single-step inference, outperforming them in efficiency. Larger variants further boost performance, demonstrating scalability.

AFilter: Improved Antibody Epitope Prediction by Machine Learning-Optimized Interface Energy Filtering of AlphaFold3-Predicted Complex

Mon, 17 Aug 2026 00:00:00 +0000

AlphaFold3 (AF3) predicts protein-complex structures from sequence with near-experimental accuracy on many targets, substantially lowering the cost of mechanistic and therapeutic discovery. However, application to antibody epitope prediction is hampered by an approximately 63% failure rate. Comparing successful and failed AF3 predictions across antibody-antigen and nanobody-antigen complexes, we found that failed predictions share a distinctive energetic signature: distorted CDR-loop geometries and elevated van der Waals strain at the interface. Building upon these observations, we developed a machine learning-based interface energy filtering framework, designated AFilter, capable of eliminating over 90% of erroneous predictions while retaining >90% of true positives. Compared with ipTM-based filtering, AFilter improved accuracy from 82.7% to 97.7% for nanobody-antigen complexes and from 79.4% to 96.3% for antibody-antigen complexes, while simultaneously raising the true positive rate from 69.8% to 96.4% and from 63.1% to 92.5%, respectively. When applied to NeuroMab antibodies of unknown structure, AFilter prioritized high-confidence epitope predictions that AF3 sampling alone could not reliably surface. As a lightweight post-hoc filter ( < 5% computational overhead) that requires no re-docking, AFilter is directly compatible with existing AF3 prediction pipelines and, in principle, transferable to other diffusion-based complex predictors, providing a practical quality-assurance layer for antibody epitope mapping in early-stage drug discovery.

From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents

Mon, 17 Aug 2026 00:00:00 +0000

Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments. Existing UQ methods often rely on local signals, such as token probabilities or predictive entropy, overlooking long-range dependencies where errors accumulate across an execution trajectory. We propose RUPA, a trajectory-level UQ framework that represents an execution history as a directed trajectory graph, with nodes for reasoning states, tool interactions, and environment feedback, and edges for temporal and semantic dependencies. Uncertainty is propagated over this graph to capture how execution risk accumulates and transfers across steps, combined with trajectory-level behavioral features and goal alignment to produce a confidence estimate. Evaluated on τ-2, Terminal-Bench-2, and GAIA using 6 open-source LLMs, RUPA outperforms existing methods by providing more accurate uncertainty estimates, enabling earlier failure detection, and improving uncertainty-guided agent execution, demonstrating the importance of relational dependency modeling for reliable UQ in long-horizon agents.

R^3-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets

Mon, 17 Aug 2026 00:00:00 +0000

Resource rationality in cognitive science asks how agents should allocate limited computation to maximize expected value. Most reasoning benchmarks use independent per-task budgets, and existing shared-budget studies do not calibrate against single-problem competence. We introduce R^3-Bench, evaluating six-problem suites under shared budgets across mathematics, competitive programming, and abstract reasoning, in both tool-free and agentic settings. Matched single-problem response curves define an offline empirical oracle. Across 72 main-table cells for six models, the oracle mean meets or exceeds the contest mean in all cells, strictly higher in 71. Under moderate tool-free pressure, equal-allocation replay also outperforms contest for four of six models. Trajectory diagnostics reveal limited strategy updating and pressure-dependent failures. In a three-model diagnostic under strong agentic pressure, at least one fixed scheduler exceeds contest mean in six of nine cells, but no policy dominates across domains. The results expose a persistent gap between demonstrated competence and shared-budget realization.

Early Detection of Erythropoietic Protoporphyria Using Sequential Machine Learning on Longitudinal Electronic Health Records

Mon, 17 Aug 2026 00:00:00 +0000

Objective: Erythropoietic protoporphyria (EPP) is a rare photodermatosis marked by multi-year diagnostic delays. We developed and externally validated machine learning models to identify patients with EPP earlier from longitudinal electronic health record (EHR) data and estimate undiagnosed disease burden. Materials and Methods: In a retrospective case-control study at two San Francisco health systems, an academic referral center (UCSF) and a safety-net hospital (ZSFG) we identified 74 confirmed EPP cases using combined diagnostic coding, biochemical criteria, and specialty chart review. Symptom-enriched controls were sampled at a 40:1 ratio. Longitudinal diagnoses, laboratory results, medications, procedures, and encounters preceding the outcome date were modeled with a gradient-boosting classifier (CatBoost) and a state-space sequence model (MAMBA). The best model was deployed across the UCSF population and externally validated at ZSFG without retraining. Results: On the UCSF held-out test set (n=1,865; 43 cases), MAMBA outperformed CatBoost (AUC ROC 0.91 vs 0.89; average precision 0.42 vs 0.27; precision 65% vs 20%), flagging cases a median of 229 days before documented diagnosis. Deployed across 297,967 symptom-compatible patients, it identified 310 high-risk individuals, implying a prevalence approaching genetic estimates. External validation at ZSFG showed attenuated performance (AUC ROC 0.72; average precision 0.10) while preserving early detection (median 264 days). Discussion: A sequence model integrating temporal EHR signals detected EPP months before clinical recognition, corroborating genetic evidence of substantial underdiagnosis. Cross-site attenuation reflects population and documentation differences and underscores the need for local recalibration. Conclusion: Longitudinal EHR-based machine learning can shorten EPP diagnostic delay and prioritize patients for confirmatory testing, supporting proactive rare-disease case finding.

The Problem Is the Problem: Towards Scalable Mathematical Discovery

Mon, 17 Aug 2026 00:00:00 +0000

AI systems are increasingly contributing to mathematical research, but frontier-model reasoning and expert review are scarce resources that must be allocated efficiently. Current AI-for-math workflows concentrate human effort on problem selection and artifact review, which are becoming bottlenecks. To address this, the authors propose a new human-AI discovery paradigm in which humans specify a research direction, and the system searches a large literature corpus for candidate problems. They build Find, Attempt, and Recommend (FAR), a literature-to-review cascade. In a combinatorics pilot, the pipeline starts from 5,245 papers, recovers 6,453 candidate conjectures or open problems, filters to 4,717 apparently well-posed and still-open conjectures, surfaces 598 potential resolutions, and selects 77 for author-team review. Among these, they identify discoveries related to conjectures by Davies–Jenssen–Perkins–Roberts, Erdős–Straus, Ikenmeyer–Pak–Panova, and Lund–Saraf–Wolf, demonstrating the effectiveness of this new mode of human-AI collaboration.

DiSCO: Defending Text-to-Image Generation via Distribution-Guided Contrastive Prompt Optimization

Mon, 17 Aug 2026 00:00:00 +0000

As text-to-image generative models advance, they raise safety concerns, particularly generating Not-Safe-For-Work (NSFW) content, further exacerbated by red-teaming attacks. Existing defenses often rely on white-box assumptions, while black-box alternatives based on LLM prompt rewriting fail in what we identify as the benign adversarial problem: prompts that are linguistically safe but still trigger harmful generation. We propose DiSCO, a zero-shot, strictly black-box defense that operates entirely at the prompt level as a plug-and-play module, requiring no model retraining or access to internals. DiSCO performs distribution-guided suffix expansion via beam search, optimized through contrastive scoring over safe and unsafe image pools generated by the target model, with iterative adaptive feedback until safe content is produced. On the I2P benchmark, DiSCO reduces attack success rate (ASR) by 37.7% on undefended models and 25.13% on defended models under multiple red-teaming attacks, while maintaining semantic fidelity and improving image coherence. Being architecture-agnostic, DiSCO can be applied to any text-to-image system without altering the model.

Gemini 3.7 Flash turns Victorian botanical PDFs into a modern plant taxonomy

Mon, 17 Aug 2026 00:00:00 +0000

Google AI Developers demonstrated a workflow that gave Gemini 3.7 Flash hundreds of PDFs from a Victorian-era book of botanical illustrations, extracted the historical plants, classified them with the modern APG IV system, and presented the result as an interactive visualization.

τ_0-VLA: A Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation

Mon, 17 Aug 2026 00:00:00 +0000

Long-horizon robot manipulation requires both reliable execution of individual skills and coherent sequencing over extended tasks. Most hierarchical vision-language-action (VLA) models make decisions with a single forward pass, lacking a mechanism to allocate additional computation to difficult choices. We introduce τ_0-VLA, a hierarchical robot foundation model that frames high-level subtask generation as a compute-scalable inference problem via world-model-guided test-time computation. At each step, the high-level policy uses execution memory to generate a subtask, searching over alternatives when needed, while a low-level policy executes across embodiments. Trained on 40,115 hours of heterogeneous real-world data with multimodal co-training, the model shows that additional test-time computation substantially improves next-subtask prediction accuracy in both in-domain and distribution-shifted settings, translating to higher closed-loop success on long-horizon tasks.

Why AI systems are most useful as designers of new scientific tools

Mon, 17 Aug 2026 00:00:00 +0000

A Nature editorial argues that scientific breakthroughs are often sparked by innovative instruments, and AI's ability to help design them could be its most significant scientific contribution. Drawing on historical examples, the piece suggests that tool innovation is key to progress, and AI-assisted tool design may drive future discoveries.

Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence

Mon, 17 Aug 2026 00:00:00 +0000

Embodied agents are increasingly used to close the gap left by end-to-end policy models, but existing harnesses remain largely open-loop, reflecting only after episodes, which cannot govern real-time physical interaction. We present Zetta, a closed-loop embodied harness that evolves code-based runtime critics and recovery skills online while keeping the base policy frozen. Through three timescale-separated loops, Zetta provides action-frequency governance, rollout-level critic-recovery proposals, and validation-gated skill updates. Together with Z-Infra, a rollout infrastructure decoupling agent logic from heterogeneous execution resources, Zetta achieves state-of-the-art success on LIBERO-Pro and RoboCasa under the current rollout budget, reaching 90.8% and 93.6%, with an 11.1x inference speedup; success continues to scale with self-exploration experience; learned skills transfer zero-shot, and clear robotic "Aha Moments" emerge. These results show that closed-loop harness self-evolution opens a scaling path for reliable physical intelligence.

Systematic Benchmarking of AI-Based Molecular Generation Models for Structure-Based Drug Design

Mon, 17 Aug 2026 00:00:00 +0000

Generative artificial intelligence is accelerating molecular design, yet the relative suitability of available models for different targets and stages of preclinical drug discovery remains unclear. Here we benchmarked 12 molecular generation and optimization methods across 176 curated protein ligand systems spanning diverse therapeutic target classes, with experimentally validated ligands providing reference chemical space. The evaluated methods encompassed pocket conditioned 3D generation, diffusion and flow based modeling, autoregressive construction, reference conditioned optimization and synthesis-aware design. Performance was assessed using operational robustness, chemical validity, uniqueness, molecular and scaffold diversity, quantitative estimate of drug likeness, synthetic accessibility, docking, physicochemical and ADMET properties, and computational resource requirements. The results revealed architecture dependent trade off such as receptor conditioned methods exploited binding pocket geometry, flow based approaches enabled efficient sampling, reference-conditioned methods favored analogue generation, and synthesis-aware approaches improved chemical feasibility, but no method consistently optimized all criteria. To address the functional potential of generated molecules, we further developed a state aware functional classifier (SAFC) that integrates molecular dynamics derived receptor ensembles, ensemble docking and protein ligand interaction graphs. SAFC provided dynamics aware functional activity rankings for generated molecules that were partly complementary to docking, drug likeness and synthetic accessibility scores. These findings support hybrid, stage specific deployment of generative models rather than reliance on any single architecture or evaluation metric. This study provides practical guidelines for generative AI based preclinical drug development processes.

Looped Language Models Improve Compositional Tool Calling

Mon, 17 Aug 2026 00:00:00 +0000

Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We study this question in compositional tool-calling settings, where models must coordinate multiple API calls, maintain intermediate state, and preserve dependencies across tool interactions. We evaluate native and retrofitted looped language models on API-Bank, BFCL, and NESTful, comparing looped and non-looped models trained under matched supervised fine-tuning recipes and varying recurrent depth at inference time. In controlled experiments, recurrent computation generally benefits compositional and dependency-aware tool use, while providing smaller and more model-dependent gains on isolated API invocation. Accuracy on multi-step tool use generally increases with recurrent depth; adaptive inference, however, achieves a more favorable compute-performance trade-off by allocating additional computation only when needed. Our results suggest that looped language models are a promising architecture for agentic systems that require reliable planning, coordination, and execution of compositional tool use workflows.

Dynamic Multi-Byte Prediction with Hierarchical Language Models

Sun, 16 Aug 2026 00:00:00 +0000

Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to subword tokenization. However, generating one byte at a time remains a bottleneck for inference speed. To address this, we introduce multi-byte prediction (MBP), which generates multiple bytes in parallel, speeding up inference with minimal performance impact and no additional parameters. MBP builds on the popular multi-token prediction (MTP) paradigm with two crucial innovations: a variable-length prediction window aligned with the latent tokens (segments) of a hierarchical LM, and a novel attention-masking scheme enabling parallel byte prediction without violating causality. We show that multi-byte prediction strikes a Pareto-optimal trade-off across generative tasks—instruction following, question answering, summarization, and machine translation—achieving the best balance between performance and inference throughput.

MathCode brings a Lean 4 formalization engine into a terminal coding agent

Sun, 16 Aug 2026 00:00:00 +0000

MathCode converts natural-language mathematics problems into Lean 4 theorem statements and attempts formal proofs. Its public project combines a persistent Lean REPL, reusable theorem and axiom libraries, agentic proving, parallel subgoal trees and an Obsidian theorem graph.

Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation

Sun, 16 Aug 2026 00:00:00 +0000

Synaptic vesicle glycoprotein 2C (SV2C) is a vesicular protein enriched in dopaminergic neurons of the basal ganglia that modulates dopamine storage and release, and its disruption is implicated in Parkinson's disease (PD). Despite strong genetic and pathological links to PD, there are no selective small-molecule probes for SV2C. Here, we describe an AI-enhanced virtual screening (VS) and experimental campaign that identified multiple novel chemotypes with low-micromolar affinity and marked selectivity for SV2C over SV2A and SV2B, starting from a large, general-purpose commercial library. Because no full-length high-resolution SV2C structure was available, we built a homology model using SV2A cryo-EM structures as templates and characterized its conformational landscape by molecular dynamics (MD) and Gaussian accelerated MD (GaMD) simulations in apo form and in complex with known SV2 ligands (plosaracetam, levetiracetam, brivaracetam, and padsevonil). A convolutional neural network-based scoring function (CNN_VS), retrospectively validated on a manually curated 39-ligand SV2A benchmark (r = 0.72 vs experimental pIC50), was then applied in a multi-stage funnel to 5.96 million Mcule in-stock compounds, which were sequentially filtered to 3.19 million CNS-relevant molecules before docking and rescoring. From 94 VS-prioritized candidates, 71 compounds were experimentally profiled in an orthogonal primary assay cascade combining a thermal shift assay (TSA) with a [3H]-padsevonil scintillation proximity assay (SPA), followed by Ki determination and isoform selectivity profiling for key hits. This campaign yielded 22 active molecules (31% hit rate) that naturally segregated into two categories: compounds that showed primary site competition, and compounds that did not show primary site competition with [3H]-padsevonil. A subset of competitor compounds also showed thermostabilization activity. Among these, compounds 36 and 56 emerged as particularly attractive leads, with Ki values of 24.6 uM and 3.25 uM at SV2C, respectively, and greater than 10-fold selectivity versus SV2A; compound 56 also maintained approximately 12-fold selectivity relative to SV2B. A complementary subset of SV2C-selective hits behaved as padsevonil-site competitors, providing a lead set that will serve as a template for functional characterization and future drug development for conditions th…

LLMs Get Smarter from Targeted Synthetic Multilingual Data

Sun, 16 Aug 2026 00:00:00 +0000

Language-specific competency (LSC) refers to the phenomenon where language models perform differently depending on the prompt language, attributed to internal misalignment of semantic representations. Existing solutions like English routing or language-balanced data have drawbacks. This paper introduces HOTFIXR, a data generation framework that probes a student model's multilingual weaknesses and generates targeted synthetic training data. Evaluated on three in-distribution tasks, three out-of-distribution tasks, and four out-of-distribution languages, HOTFIXR improves in-distribution performance by 6.2%, reduces catastrophic forgetting on OOD tasks by 3.7%, and on OOD languages by 7.1%. Code will be released upon acceptance.

Measuring Autonomous AI Research

Sun, 16 Aug 2026 00:00:00 +0000

Prime Intellect ran 153 internet-isolated autonomous runs across 18 frontier models on the nanoGPT optimizer speedrun, releasing the traces, monitor reports, per-run ledger and harness.

Bounded Agents: Delegation Security for Multi-Agent AI Systems

Sun, 16 Aug 2026 00:00:00 +0000

LLM-based agents act on behalf of users to access cloud services, call tools, or invoke agents, but permissions remain static and each request is evaluated independently, allowing deviations from delegated tasks, prohibited combinations of actions, or unrestricted sub-delegation. The Agentic Principal Chain (APC) tracks delegated authority and evaluates requests against accumulated session state using six checks, carrying forward restricted scope and budgets, and enforcing decisions outside the model via composition closure. In evaluations across 3,154 instances (InjecAgent, AgentDojo, ASB), APC reduced AgentDojo exfiltration from 75-100% to 0% across all domains, blocked all 544 InjecAgent data-stealing cases, and reduced destruction from 38.6% to 4.0% and manipulation from 90.5% to 12.1%. Authorization latency was 0.24 ms at the 99th percentile, but utility dropped 8.6 and 13.9 percentage points in AgentDojo task-injection pairs. Implementation, evaluation tools, and data are public.

Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search

Sun, 16 Aug 2026 00:00:00 +0000

Scientific discovery often involves optimizing expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs. Generative models like large language models (LLMs) provide expressive priors, but their likelihoods and self-assessments are unreliable for objectives and calibrated epistemic uncertainty, especially for novel candidates. The Large Discovery Model (LDM) couples a generative model with a Bayesian non-parametric reward surrogate, where the generative model proposes and refines candidates, and the surrogate predicts performance and quantifies uncertainty. The discovery memory and surrogate are continually updated with new observations. Evaluated on neural-network training, antibody design, and molecular optimization, LDM achieves a 2.4-fold greater reduction in validation BPB, an 18.2% relative decrease in binding energy, and over 60% relative gains in multi-objective performance compared to LLM-only reflection or traditional statistical search, suggesting LDM as a general-purpose discovery engine.

TinyCast: Probabilistic Zero-Shot Forecasting with Computed Periodicity

Sun, 16 Aug 2026 00:00:00 +0000

We introduce TinyCast, an attention-free zero-shot forecaster that emits a predictive distribution from 146,505 parameters, on the premise that at this size the periodic structure of a context is worth computing rather than learning. A zero-parameter spectral detector supplies the dominant periods, the context is folded on their phase, and a dilated convolutional encoder and a block-autoregressive quantile decoder model the rest. It is smaller than every zero-shot entry on the GIFT-Eval board whose parameter count can be established. On probabilistic accuracy it defines the size-accuracy frontier. Among zero-shot entries declaring no test-data leakage it is the only one below 1.4M parameters that emits a predictive distribution, and every entry scoring better carries at least that budget. On Chronos-ZS and fev-bench every neural model ahead of it carries at least 28 times its parameters. Because the mixing path is convolutions and matrix multiplications only, it exports to static INT8 and forecasts end to end on an embedded device without per-signal fitting.

Assessing Codon Language Models for Context-Aware Codon Optimization in Nucleic Acid-Based Medicines

Sun, 16 Aug 2026 00:00:00 +0000

Codon optimization uses synonymous sequence changes to improve the expression and therapeutic performance of nucleic acid-based medicines. Masked language models (MLMs) have recently been proposed as alternatives to traditional, frequency-based codon optimization approaches, yet whether they offer a meaningful advantage over such simpler methods remains unclear. Here we benchmark three prominent MLMs --- CaLM, EnCodon and CodonTransformer --- across backtranslation fidelity, sequence generation and nine molecular phenotype prediction tasks, and experimentally evaluate model-designed sequences using a secreted embryonic alkaline phosphatase (SEAP) reporter. The models differed markedly in amino-acid fidelity and generated distinct synonymous sequence variants. However, no single model performed best across all benchmark tasks and simple sequence features remained competitive in several settings. Our interpretability analysis revealed that the models integrate a large window of codon context for making predictions, as opposed to frequency-based approaches. Our in vitro data showed that MLM-designed variants outperformed conventional and commercial vendor-derived sequences in both transient and stably integrated expression, supporting the models' ability to capture translational context beyond codon frequency. Together, our results establish MLMs as effective and complementary tools for codon optimization and suggest that sampling across multiple models may improve the likelihood of identifying high-performing therapeutic sequences.

WorldRover: A Scalable Synthetic Video Data Engine for World Exploration with Rich Annotations

Sun, 16 Aug 2026 00:00:00 +0000

WorldRover is a data engine for generating richly annotated, long-range explorations of artist-built environments. At its core, WorldRover-Engine is an Unreal Engine pipeline that executes and offline-renders minute-scale routes while preserving their full trajectories and scene geometry. The same exploration can be replayed from first-person, third-person, and 360 panoramic cameras under different environmental states. Using WorldRover-Engine, the authors construct WorldRover-10M, whose sequences pair RGB with metric depth, camera trajectories, and trajectory-derived action signals throughout each exploration. Third-person subsets additionally provide dense optical flow, long-range 2D/3D point tracks with visibility, and a character trajectory distinct from the camera trajectory. The engine can render a traversal from first-person, third-person and 360 panoramic viewpoints, under different environmental states or with a neutral white material, while preserving the route and scene geometry. WorldRover thus turns long-horizon world exploration into a scalable data-generation problem, providing supervision for models that must build, maintain, and revisit coherent representations of an explorable world.

Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning

Sun, 16 Aug 2026 00:00:00 +0000

Multimodal large language models increasingly use visual chain-of-thought (Visual CoT), which generates intermediate reasoning images for spatial, temporal, and embodied reasoning, but introduces significant inference overhead. This paper introduces Internalized Visual Thinking (IVT), a post-training framework that jointly optimizes textual prediction and next-embedding prediction on unlabeled videos, enabling the model to directly generate answers at inference without synthesizing future frames. Across all six evaluation settings, IVT improves over direct-answer fine-tuning, and compared with explicit Visual CoT, it achieves comparable or better performance while reducing average end-to-end latency by more than 5x. The findings suggest that explicit pixel-space generation at inference may not be necessary for effective proactive video reasoning.

Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents

Sat, 15 Aug 2026 00:00:00 +0000

Memory is becoming core infrastructure for long-horizon LLM agents, yet existing evaluations offer limited guidance on which memory substrate—the underlying medium in which memory is represented and stored—should be used under different operating regimes. We present a controlled harness evaluation of memory substrates for memory-augmented agents, covering dense and sparse indices, text records, structural stores, hierarchical stores, refinement-based memories, parametric updates, and activation-compatible context mechanisms. Across three backbone models and four benchmark suites spanning user-centric question answering and agent-centric decision-making, we instrument 26 performance and efficiency metrics under a unified harness. Our results show that no single substrate consistently dominates: broad retrieval benefits long-context factual QA, while excessive retrieval can harm sequential decision-making by shifting attention away from action-critical context. Scalability introduces a further routing axis, as substrates that perform well at moderate history lengths can become costly or brittle at longer horizons. These findings motivate substrate routing as a necessary component of adaptive agent memory systems and provide empirical guidance for designing efficient, reliable, and regime-aware long-term memory for LLM agents. Code will be made available upon acceptance.

PRISM: Prototype-Rectified Iterative Self-Supervised Manifold Denoising

Sat, 15 Aug 2026 00:00:00 +0000

Audio-Text Foundation Models (ATMs) are prone to catastrophic failure under severe acoustic noise, but existing adaptation methods either reinforce noise instead of signal or require privileged noise annotations. PRISM (Prototype-Rectified Iterative Self-Supervised Manifold Denoising) is a training-free, source-free TTA framework that leverages the Affine Noise Hypothesis, positing that severe acoustic noise induces a low-rank affine shift in the latent space, with over 90% of distortion energy in the leading 60 principal components. PRISM uses frozen text prototypes as geometric anchors to estimate and reverse this distortion via three closed-form corrections combined into one static projection matrix by Affine Bias Regression. At inference, adaptation is a single matrix-vector multiplication taking 0.0009 ms. On UrbanSound8K, PRISM improves over the zero-shot baseline by 12.94 percentage points and surpasses an oracle-assisted TTA baseline by 9.41 percentage points without using its privileged noise prompts. It also identifies the Polyphonic Trap, a failure mode of subspace deflation, and resolves it via Confidence-Aware Regression, recovering up to 8.16 percentage points for the worst-affected class.

FlexAutoDock: A Flexible Platform for Automated Molecular Docking and Virtual Screening of Natural and Synthetic Compounds

Sat, 15 Aug 2026 00:00:00 +0000

Drug discovery (DD) is a complex, time-consuming, and resource-intensive process that involves the identification of therapeutic targets, selection of bioactive compounds, and extensive experimental validation. The discovery of promising therapeutic compounds from large libraries of phytochemicals and synthetic molecules remains a major challenge in modern drug development. Screening millions of compounds through conventional experimental approaches requires substantial time, cost, and computational resources. In recent years, in-silico molecular docking has emerged as an important computational approach for predicting interactions between small molecules and target proteins, thereby helping researchers prioritize promising compounds for further investigation. Several molecular docking webservers, including iScreen, SwissDock, CB-Dock2, DockThor, and MTiOpenScreen, have been developed to support virtual screening studies. However, many currently available platforms still face some important limitations. Most existing tools lack integrated repositories of medicinal plant-derived phytochemicals and organism-derived bioactive compounds, automated mapping between plants and their associated phytochemicals, and flexible ligand retrieval using chemical names, SMILES strings, PubChem CIDs, or drug names. In addition, many platforms require extensive manual protein and ligand preparation, provide limited support for AlphaFold-predicted protein structures, and lack efficient large-scale multi-target virtual screening. Most existing docking platforms offer limited support for interactive inspection of docked protein-ligand complexes, often requiring users to download the results and analyse them using external molecular visualization software. To address these limitations, we developed FlexAutoDock, an automated cloud-based molecular docking platform that provides a unified environment for protein-ligand docking and large-scale virtual screening. Unlike existing web servers, FlexAutoDock integrates curated repositories of medicinal plant-derived phytochemicals, organism-derived bioactive compounds, and synthetic compounds from the ZINC database while supporting flexible ligand acquisition through medicinal plant or organism selection, chemical names, SMILES strings, PubChem CIDs, and drug-name queries. The platform further streamlines the docking workflow through aut…

An LLM-driven Chinese Corpus of Human Olfactory Descriptions and Entity Annotations

Sat, 15 Aug 2026 00:00:00 +0000

A study published online on August 15, 2026, in Scientific Data introduces a Chinese corpus of human olfactory descriptions and entity annotations, driven by large language models (LLMs). The corpus aims to collect and annotate Chinese expressions related to smell, providing a data resource for olfactory research. The paper's DOI is 10.1038/s41597-026-08117-y.

Synthetic PET and CT Data using Generative Model in Projection Domain

Sat, 15 Aug 2026 00:00:00 +0000

The rapid advancement of artificial intelligence has substantially increased the demand for large-scale datasets; however, available datasets remain insufficient for data-intensive tasks, particularly in nuclear medicine. While large-scale datasets exist for computed tomography (CT), few provide paired projection-domain and image-domain data across diverse lesion types. This issue is more pronounced for positron emission tomography (PET), where datasets are scarce, particularly for multiple tracers such as 18F-DOPA, 18F-FDG, and 68Ga-PSMA, anatomical regions, and disease categories. This work presents SPC-Gen, a large-scale Synthetic dataset for PET and CT, Generated using a latent diffusion model. The dataset covers diverse disease types, lesion characteristics, tracers, anatomical regions, and modalities, while preserving the intrinsic correspondence between projection and image domains. SPC-Gen contains 50,104 released synthetic slice records, including PET projection-image records and CT image-domain records, forming a standardized large-scale dataset. Experimental results show that the generated samples closely align with real data distributions and can support improved performance on downstream tasks such as lesion segmentation. Compared with conventional VAE models and more recent GAN- and NCSN-based baselines, SPC-Gen achieved stable KID values around 0.021 across the single-modality evaluation and an FID of 40.960 for the PET Trunk image-domain setting, indicating improved global distributional alignment between synthetic and real data.

MOSS-VL Technical Report

Sat, 15 Aug 2026 00:00:00 +0000

MOSS-VL is an open vision-language model family that treats real-time interaction—perceiving while speaking—as a first-class capability. It is co-designed across the stack: the language decoder attends to vision only through gated cross-attention, enabling natural perception of incoming frames during generation; a synthesized interaction corpus supervises when to speak, stay silent, or revise; and a staged curriculum concentrates real-time-specific training in a light final stage over a strong offline foundation. Offline, MOSS-VL-Instruct is competitive at comparable scale and leads temporal-reasoning video sets. Across four streaming benchmarks, MOSS-VL-Realtime achieves the best average on three (second on the fourth) among open-source streaming models, sweeping the three subsets that squarely test proactive behavior—e.g., 66.0 vs. 37.5 for the best baseline on OmniMMI Proactive Alerting. With 11.3B parameters but visual tokens outside the decoded sequence, MOSS-VL widens its time-to-first-token advantage over same-backbone Qwen3-VL-8B from 2.8x to 5.1x as visual context grows. All five checkpoints, the training curriculum, and real-time inference code are released.

Arc Institute's Patrick Hsu on Building an App Store for Biology with AI

Fri, 14 Aug 2026 23:51:26 +0000

Arc Institute’s Patrick Hsu on Building an App Store for Biology with AI Patrick Hsu, co-founder of Arc Institute, discusses the opportunities for AI in biology beyond just drug development, and how Evo 2, their new biology foundation model, is enabling a broad ecosystem of applications. Evo 2 was trained on a vast dataset of genomic data to learn evolutionary patterns that would have taken years to find; as a result, the model can be used for applications from identifying mutations that cause disease to designing new molecular and even genome scale biological systems. Listen Now Summary Patrick Hsu, co-founder of the Arc Institute and Berkeley professor of bioengineering, brings a unique perspective on the convergence of AI and biology, emphasizing that machine learning’s potential extends far beyond drug discovery to understanding and programming fundamental biological systems. His insights reflect both the immediate possibilities and long-term vision for how AI will transform our understanding of life itself. - AI can model biological systems by learning from evolution’s natural experiments: The ability to predict the functional consequences of genetic mutations—from disease-causing variants to subtle physiological changes—represents a fundamental shift in how we can understand and manipulate biology. - Biology needs better predictive models: AI models like Evo 2 can overcome the time-consuming “guess and check” paradigm that dominates current research. While wet lab validation remains essential, AI models that can accurately simulate biological systems could dramatically accelerate the scientific process by allowing parallel testing of thousands of hypotheses. - The future of medicine will require integrating multiple data streams: Personalized health predictions and interventions will require learning from genetics as well as real-time biomarkers. Current fragmented approaches need to evolve into systems that can holistically model the interconnections between brain, body and behavior. - Success in computational biology requires true bilingual expertise: Arc Institute looks for people who deeply understand both machine learning and biological systems. While specialists in either field are common, the ability to bridge these domains and identify meaningful connections is rare but essential for breakthrough innovations. - Drug discovery is not the bottle…

MathForm: Scaling Mathematical Autoformalization with Knowledge Retrieval and Verification-Guided Refinement

Fri, 14 Aug 2026 11:51:12 +0000

Autoformalization is commonly framed as translating natural-language mathematical statements into machine-verifiable formal languages such as Lean 4. However, faithful formalization requires more than translation; models must map concepts to the complex hierarchy of types and definitions in formal libraries like Mathlib. Existing approaches struggle because they rely heavily on parametric memory and lack feedback-driven revision. To address this, we introduce MathForm, a framework that constructs verified training data through Mathlib knowledge retrieval and verification-guided iterative refinement. Using this framework, we build FormalVerse, a Lean 4 dataset with approximately 367K verified examples, and train MathForm-8B via supervised fine-tuning and reinforcement learning. Across six benchmarks, MathForm-8B achieves average Pass@8 rates of 88.06% under Syntax Check (SC) and 72.37% under Consistency Check (CC), outperforming multiple specialized 32B autoformalizers. On the challenging FATE-H and FATE-X subsets, it attains CC pass rates of 63% and 37%, exceeding the strongest specialized baselines in both cases.

ForgeWM: Progressive Causal Training for Few-Step Action-Conditioned Video World Models

Fri, 14 Aug 2026 07:13:34 +0000

Action-conditioned video world models require low-latency causal generation, but extending causal distillation to interactive world models is challenging due to the need to align discrete keyboard and continuous mouse control with temporally compressed latent chunks. ForgeWM uses a progressive four-stage recipe—domain adaptation, teacher-forced causal training, causal consistency distillation, and on-policy distribution matching against a bidirectional teacher—to transform a bidirectional generator into few-step world models with steady-state denoising budgets of 1, 2, and 4 steps. It also supports a dual-path deployment with optional replay-time refinement, where the one-step student re-noises and refines its saved draft. On paired Minecraft trajectories, ForgeWM leads in imaging quality, reference-aligned motion-profile agreement, action-sign accuracy, and mouse-control accuracy, achieving the lowest reference LPIPS; the same recipe transfers to gamepad-controlled FPS. Replay-time refinement matches four-step reference quality while staying about three times closer to the experienced trajectory than regeneration from noise.

Pushmeet Kohli on AlphaEvolve and AI's Scientific Revolution

Fri, 14 Aug 2026 06:26:57 +0000

DeepMind’s Pushmeet Kohli on AI’s Scientific Revolution Pushmeet Kohli leads AI for Science at DeepMind, where his team has created AlphaEvolve, an AI system that discovers entirely new algorithms and proves mathematical results that have eluded researchers for decades. From improving 50-year-old matrix multiplication algorithms to generating interpretable code for complex problems like data center scheduling, AlphaEvolve represents a new paradigm where LLMs coupled with evolutionary search can outperform human experts. Pushmeet explains the technical architecture behind these breakthroughs and shares insights from collaborations with mathematicians like Terence Tao, while discussing how AI is accelerating scientific discovery across domains from chip design to materials science. Listen Now Summary Pushmeet Kohli, VP of Research at Google DeepMind, has been at the forefront of using AI to make groundbreaking scientific and mathematical discoveries, notably with AlphaFold, FunSearch and now AlphaEvolve. The episode emphasizes how combining large language models with structured search and evaluation protocols is already transforming both the pace and nature of scientific progress across domains from mathematics to materials science. The “harness” architecture is everything: True AI breakthroughs come from pairing powerful generative models with robust, domain-specific evaluators—what Pushmeet calls “the harness.” This architecture distinguishes between hallucinations and brilliant insights. For AI founders, investing in strong, trustworthy evaluators is as important as advancing core models. Remove constraints to unlock true discovery: AlphaEvolve’s breakthrough came from removing human-provided templates and searching entire algorithms rather than code snippets. This broader, less restrictive search space leads to more significant breakthroughs. Consider loosening system constraints without sacrificing safety or evaluation rigor. Multi-agent systems extract hidden value: Different models critiquing, refining and ranking each other’s outputs yield results beyond single models. DeepMind’s AI co-scientist system improved dramatically over days of computation. Models are often better at evaluating ideas than generating them, so separate generator, critic and editor roles. Interpretability remains essential for adoption: Engineers prefer interpretable code over b…

Alignment with experimental data improves protein generative modeling

Fri, 14 Aug 2026 00:00:00 +0000

Nature Methods, Published online: 14 August 2026; doi:10.1038/s41592-026-03138-2 Direct preference optimization (DPO) aligns a pretrained protein language model with experimental stability data, yielding ProteinDPO, a model that scores and generates thermostable protein sequences. Applied to H5N1 influenza hemagglutinin, it achieved large improvements in thermal stability of hemagglutinin while preserving antibody recognition.

RobustiPy: An efficient next-generation multiversal library with model selection, averaging, resampling, and explainable AI

Fri, 14 Aug 2026 00:00:00 +0000

RobustiPy is a frontier open-source library for asking whether empirical scientific conclusions survive all possible yet defensible modeling choices. It builds analytical multiverses and summarizes uncertainty. Through controlled simulations, time profiling, and empirical replications—including re-analyses of discrepancies in published work—it makes robustness analysis transparent, reproducible, and interpretable.

Training Leaves Traces: Centered Residual Signatures for Language Model Lineage Verification

Fri, 14 Aug 2026 00:00:00 +0000

Open-weight language models are often fine-tuned, quantized, pruned, or merged without documented provenance. We propose a data-free, white-box method for lineage verification: by removing the identity-aligned component that residual training imparts to branch products, we isolate checkpoint-specific structure across residual blocks and compute a symmetric lineage score. On residual-MLP and GPT-2 benchmarks, the score perfectly separates fine-tuned, LoRA-merged, pruned, and quantized descendants from independent and distilled models (AUROC=1.0), distinguishing weight ancestry from behavioral similarity. Under function-preserving checkpoint laundering, weight-space baselines lose margin or fail, while our score remains unchanged and runs 76x faster than the nearest robust baseline on GPT-2. The projection-pairing signal generalizes across six language-model families and beyond; a case study correctly identifies 3 related and 7 unrelated LLaMA-2 public checkpoints. These results establish a passive, data-free provenance signal for compatible open-weight language-model checkpoints.

Aligning protein-generative models to experimental fitness with ProteinDPO

Fri, 14 Aug 2026 00:00:00 +0000

Nature Methods, Published online: 14 August 2026; doi:10.1038/s41592-026-03137-3 This Article demonstrates that direct preference optimization (DPO) can be used to effectively align an unsupervised structure-conditioned language model with biophysical information. The aligned model, ProteinDPO, achieves stability prediction competitive with that of task-specific models and consistently outperforms unsupervised and fine-tuned versions of the model.

Personalized Auto-Research: Towards a True AI Co-Scientist

Fri, 14 Aug 2026 00:00:00 +0000

AI co-scientists are beginning to transform research by generating hypotheses, retrieving related work, designing experiments, executing code, and drafting papers, but current systems ignore the individual researcher, optimizing generic metrics like novelty and reviewer scores. This paper introduces personalized auto-research, which conditions every stage on a representation of the researcher, arguing that personalization is essential for a genuine co-scientist. The proposed framework features three components: graph-grounded researcher representations, full-pipeline personalization, and individualized evaluation. It highlights the failure mode where different researchers receive identical output, erasing tacit knowledge crucial for novel ideas, and discusses open challenges.

Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion

Fri, 14 Aug 2026 00:00:00 +0000

High-quality creative writing data for LLMs is dominated by story-centric data, limiting models' adherence to diverse creative formats. The authors propose an attribute-guided genre expansion framework that uses human-authored story prompts as creative seeds and manually curated genre attributes to enforce structural, stylistic, and formatting conventions. This produces genre-faithful query-response pairs, which are quality-filtered. The resulting Multi-Genre Collection contains 50K examples across 13 genres, including story, rap, lyrics, scripts, game design, and character design. Experiments on out-of-distribution writing benchmarks and held-out genre diagnostics show that models fine-tuned on this data outperform baselines and models trained on existing writing corpora. Genre-count ablations indicate that controlled genre expansion, rather than story-centric scaling alone, is key to robust creative writing capability.

The More Popular, The Harder to Forget: Adaptive Popularity for LLM Unlearning

Fri, 14 Aug 2026 00:00:00 +0000

Popular facts are memorized more deeply during pretraining and resist removal longer than rare ones, yet existing LLM unlearning methods apply uniform gradient pressure regardless of training-data frequency. We propose AdaPop (Adaptive Popularity), which combines local token confidence with a per-fact popularity-dependent exponent derived from an external proxy (e.g., Wikidata sitelinks, LLM-as-Judge), and automates the forget-retain balance via a dual-ascent controller that adjusts the retain penalty each epoch. Across three model families and two benchmarks, AdaPop leaks ~5x less forgotten content than competing methods under paraphrased queries and ~1.6x less under adversarial reformulations. Internal metrics support our analysis: under our method, forget-set hidden states move further from the pre-unlearning model's states than under other methods, while retain-set representations remain close.

How Do Agents Fail on AutoResearch: End-to-End Diagnostic Evaluation on 100 Real-World Frontier Research Tasks

Fri, 14 Aug 2026 00:00:00 +0000

AutoResearchEval introduces 100 tasks spanning 7 scientific domains and the full research lifecycle, including ideation, retrieval, execution, analysis, writing, and review. Evaluating 8 harness-model combinations yields 800 trajectories with process-level annotation, leading to the AutoResearch Failure Taxonomy (ARFT), a framework of 45 empirically-grounded failure patterns. A human-calibrated agent-as-a-judge pipeline enables scalable fine-grained attribution. The failures converge on a single overarching limitation: current agents lack a metacognitive loop—the ability to check what they produced against what they found, revise when it doesn't hold up, and question the soundness of their path. This pattern recurs across all combinations, including the strongest models, locating the deficit at the model level rather than in any particular scaffold. The work publicly releases AutoResearchEval and ARFT to facilitate further research.

Laboratory Differences May Mislead Scientific AI Models, Researchers Warn

Thu, 13 Aug 2026 17:21:25 +0000

UNIVERSITY PARK, Pa. — Artificial intelligence could dramatically accelerate the search for technologies that transform carbon dioxide into useful fuels, but a new multi-laboratory study shows that even the most advanced model can be undermined by something as simple as inconsistent stirring. Researchers from four U.S. laboratories have demonstrated that experimental variability can substantially alter measurements of catalyst performance, creating a serious challenge for scientists who want to use real-world laboratory data to train artificial intelligence and machine-learning systems. The work, led by scientists at SLAC National Accelerator Laboratory with contributions from Penn State, Stanford University and the University of California, Santa Barbara, was published in Nature Catalysis. The study focused on a rhodium-based catalyst used in carbon dioxide hydrogenation, a chemical process in which carbon dioxide reacts with hydrogen to produce carbon-containing compounds. In the experiments, the catalyst was designed to promote the formation of carbon monoxide, an important intermediate that can be converted into synthetic fuels and other industrial chemicals. Methane was also produced as an undesirable side product. By comparing results from laboratories that followed a shared experimental protocol, the researchers hoped to measure how reproducible catalyst testing would be when performed by independent teams. The experiment was structured as a “round-robin” study, in which the same material and testing instructions are distributed among multiple laboratories so that differences in performance can be traced to procedures, equipment or operating conditions rather than to the catalyst itself. The motivation is closely tied to the growing use of artificial intelligence in chemistry and chemical engineering. A well-trained model can examine relationships among catalyst composition, temperature, pressure, reaction time and product yield, then predict which combinations are most likely to work. Instead of testing thousands of possibilities experimentally, researchers could use the model to identify a smaller set of promising candidates and validate those predictions in the laboratory. Such an approach could reduce the time, cost and energy required to develop catalysts for carbon capture, fuel production and industrial chemical manufacturing. However, machine…

QuoteBench: How Matched Scores Can Hide Command-Path Failures

Thu, 13 Aug 2026 00:00:00 +0000

LLM coding agents issue Bash commands through interfaces that may serialize, wrap, and reparse model output. Matched execution scores alone cannot distinguish command-generation errors from failures introduced after generation. QuoteBench measures this boundary with exact final-state validation on 56 one-shot tasks from 14 incident-derived families, crossing the generation contract with the execution transport around one deliberately unescaped added parser. Escaping at the interpolation point reproduces each replayed reply's raw-path outcome, so any recovery under a disclosed boundary must come from the model changing its generation. Across eight same-window configurations, replaying the same reply through the added parser lowers success by 55.4 to 73.2 percentage points; disclosure recovers 30.4 to 60.7 points for six configurations, and zero or slightly negative for the other two. Raw generation is nearly saturated at the frontier; boundary adaptation is what still separates models. GPT-5.6-sol's matched gap of -3.6 points hides -64.3 points of damage and +60.7 points of compensation. The deployment configuration reorders models: one reversal among 26 comparable pairs is unambiguous and four more sit on single-task margins. Evaluations of command-issuing agents should report the model configuration, generation contract, execution path, operating point, and final-state validator rather than treat a matched score as an intrinsic model property.

OmniScientist: An Omni-Modal Omni-Discipline AI Scientist

Thu, 13 Aug 2026 00:00:00 +0000

Existing AI scientist systems typically reason over text, code, or precomputed summaries, missing crucial spatial, temporal, and cross-channel evidence. To address this, researchers introduce OmniScientist, an end-to-end omni-modal AI scientist that directly conducts multidisciplinary research from heterogeneous raw evidence. The system features a perception layer and three autonomous agents for ideation, experiment, and writeup, operating within a deterministic pipeline, allowing observations to shape research throughout the lifecycle. It enforces novelty, rigor, and claim checks via code. Evaluated on 36 real-data cases spanning 5 discipline families and 4 evidence types, including images, signals, and 3-D structures, it completed full pipelines in all cases, achieving a mean paper score of 6.3. Compared to a blind variant using precomputed scalar features, direct perception improved all 7 evaluation dimensions and won 85% of head-to-head judgments.

CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation

Thu, 13 Aug 2026 00:00:00 +0000

CardioState-JEPA is a cardiac foundation model that learns a shared representation across ECG, PPG, and PCG using a physiology-aware joint-embedding predictive architecture. It maps heterogeneous waveforms into a common token space, processes them with a shared Transformer encoder, and learns by predicting masked latent cardiac states. A learned delay aligner handles temporal offsets between electrical, mechanical, and hemodynamic events. Evaluated as a frozen encoder across 25 downstream tasks, it improves average PPG classification by 8.2 AUROC points, PCG murmur detection by 18.8 AUROC points, and ECG classification by 15.5 AUROC points over the best self-supervised baseline, and matches or exceeds models trained with privileged clinical text or supervised labels on several ECG benchmarks.

V-RAE: Rethinking Video Latent Spaces for Generation

Thu, 13 Aug 2026 00:00:00 +0000

V-RAE is a video representation autoencoder that builds compact generative latents on top of frozen vision foundation model representations. A lightweight temporal pooling module removes temporal redundancy while preserving semantic structure, and a video decoder reconstructs continuous motion from compressed features. On K600, V-RAE achieves an rFVD of 2.13, outperforming all evaluated large-scale pretrained video VAEs. Under matched generation settings, its best variant attains gFVD scores of 117.86 and 19.16 on UCF101 and K600, respectively, while converging up to 6x faster. The authors introduce tFVD, a temporal-coherence diagnostic that correlates more reliably with downstream generation quality. Additionally, V-RAE improves future video prediction on Cityscapes over the Wan 2.2 VAE latent space under matched settings. Experiments demonstrate that frozen semantic representations can support video reconstruction, generation, and predictive modeling.

A Digestion of the Proof of Sendov’s Conjecture

Wed, 12 Aug 2026 23:10:54 +0000

This post concerns the following conjecture of Sendov , as well as its strengthening by Phelps–Rodriguez : Conjecture 1 (Sendov’s conjecture) Let , and let be a degree polynomial with all zeroes in the unit disk. Then for every zero of , there exists a critical point of with . Conjecture 2 (Phelps–Rodriguez conjecture) Let , and let be a degree polynomial with all zeroes in the unit disk. Then for every zero of , there exists a critical point of with , unless is on the unit circle and is a scalar multiple of . By applying a rotation around the origin, we can normalize to be a real number with . From the work of Rubinstein , both conjectures were already established in the case, so one can restrict to the case. Both of these conjectures then follow from Conjecture 3 (Sendov’s conjecture in interior) Let . Let be a degree polynomial with all zeroes in the unit disk. Then if is a zero of , there exists a critical point of with . All three of these conjectures were established for (in a sequence of papers culminating in this paper of Brown and Xiang ) and for sufficiently large (in a paper of myself , which in turn built upon several partial results in this setting). This left the case of intermediate to be settled. My arguments used some qualitative ingredients (most notably analytic continuation) and as such did not easily lend themselves to quantifying the threshold of above which the argument was valid. Recently, Lech Mazur was able to use an AI tool to resolve Sendov’s conjecture for all , with the proof verified in Lean . However, the AI-generated proof was not human-digested to be in the form of a publication-ready preprint; and it has taken me several days (with heavy AI assistance ) to perform such a digestion, to place the proof in proper context with previous literature and to simplify and streamline the argument to highlight the main ideas. (Note: the above chat log only represents a portion of the digestion work: the rest was performed with pen and paper, or using some further AI agents.) The same arguments also give a new proof of Rubinstein’s theorem, which I also give below the fold. One consequence of this digestion is that the argument in fact demonstrates Conjecture 3 , and thus resolves both the Sendov conjecture and the Phelps–Rodriguez conjecture in full generality. The proof ends up being remarkably elementary. No complex analysis is used…

MindTopo reveals VLMs’ spatial reasoning abilities

Wed, 12 Aug 2026 16:00:00 +0000

At a glance MindTopo is a new benchmark for testing topological reasoning in AI, evaluating whether multimodal models can understand concepts such as connectivity, enclosure, order, separation, and knots. The benchmark measures both reasoning and planning, testing not only whether models can recognize topological relationships in static images but also whether they can preserve and manipulate those relationships through a sequence of actions. Current multimodal models perform much better on static recognition than interactive tasks, suggesting they struggle to maintain a consistent understanding of topology over time. Failures often emerge during planning rather than perception, with models losing track of structural relationships as scenes change or proposing actions that violate physical constraints. The findings highlight an important opportunity to advance AI systems for robotics and interactive environments, where understanding what stays connected, enclosed, ordered, or knotted is essential for reliable decision-making. Can AI determine whether two rooms remain connected after a wall is added? Can it recognize whether an animal is inside a fence, distinguish a true knot from a tangled loop, or rearrange several ropes without allowing them to pass through one another? These questions concern 3D topology, a form of spatial understanding based not on exact distances, angles, or shapes, but on structural relationships that persist as objects bend, stretch, or deform. Connectivity, enclosure, ordering, and knottedness are examples of topological properties. These properties are a foundational layer of human spatial understanding in Cognitive Science, yet they remain largely absent from how multimodal AI systems are evaluated. In a new research study, we introduce MindTopo (opens in new tab) , a benchmark designed to evaluate whether multimodal large language models possess this kind of topological intuition. Our findings reveal a substantial gap between recognizing topology in a static image and maintaining an innate understanding of that topology while planning and acting. Current models can sometimes identify a connected path, enclosed region, or knot in a single scene, but that understanding often breaks down once the model must manipulate the scene through a sequence of actions. How MindTopo defines topological space Most spatial evaluations for multim…

Feeling the heat: how the AIFS learns the urban heat island

Wed, 12 Aug 2026 10:00:00 +0000

The summer of 2026 has been defined so far by unprecedented levels of heat. Since late May, a rapid succession of heatwaves has moved across Europe. June was the hottest on record for western Europe, some three degrees above the 1991–2020 average. England and France both recorded their hottest June ever, while several countries broke all-time records, and more than 10,000 excess deaths were reported across the continent in the final week of June alone. A further heatwave followed in early July. Heat of this kind is felt most acutely in cities. Dense built-up surfaces, reduced vegetation, and trapped heat from urban infrastructure contribute to the urban heat island effect, causing urban areas to remain warmer than their rural surroundings. The difference is often most pronounced at night, when cities release stored heat more slowly. During heatwaves, persistently high overnight temperatures can substantially increase health risks by increasing cumulative heat exposure and reducing opportunities for recovery. For a forecasting system to capture the urban heat island effect, it needs to know where the cities are. As such, the next version of the Artificial Intelligence Forecasting System (AIFS v3) is set to include a static field representing urban extents as forcing; here we show what the machine learning (ML) model does with that field. Giving the AIFS a view of the city The AIFS has been steadily growing beyond its initial core atmospheric remit, using Anemoi to represent more of the Earth system with machine learning. Static fields – fixed maps that give the model information about the environment in which weather develops – are an important part of that expansion. The urban field is one such addition. It tells the model, at every grid point, the impervious fraction of the surface – the proportion covered by roads and buildings – giving a static description of the urban environment that complements the atmospheric state. Isolating the urban signal To see what the model attributes to the urban field, we first run the AIFS prototype as normal, forced with the standard urban field, then repeat the forecast with the urban field set to zero everywhere. This effectively removes cities from the model's view, while keeping the initial conditions identical. The difference between the two forecasts isolates the model's learned urban response, separating it from the…

[AINews] How to steal a Reasoning Trace

Wed, 12 Aug 2026 07:11:08 +0000

AINews reports on a new paper demonstrating how encrypted reasoning traces from frontier models can be decoded and replayed to other models, sessions, or users, leading to dramatic improvements in open models. Since the release of o1, reasoning models have obscured their traces with cryptographic signatures to prevent distillation, but this study shows that these encrypted blocks can be replayed to weaker models and prompted to transcribe the reasoning. The paper also warns that publicly shared encrypted reasoning sessions could leak personal data; a preliminary scan of about 7,000 public traces found 62 unique API keys, 33 email addresses, 33 passwords, and other sensitive data. Additionally, the authors discuss alignment issues, such as CoT summarizers hiding answers and unintelligible reasoning.

Robust generative transition-state models for unseen chemistry

Wed, 12 Aug 2026 00:00:00 +0000

Transition states (TSs) govern the rates and outcomes of chemical reactions, making their accurate prediction a central challenge in computational chemistry. Although recent machine learning models achieve near-chemical accuracy in the prediction of TS structures and the associated reaction barriers for small organic reactions, their ability to generalize beyond the training domain remains largely unexplored. Here we introduce targeted benchmarks to probe chemical and structural novelty in generative TS prediction. Building on Transition1x, a large-scale dataset of reactions involving small organic molecules, we curate extensions incorporating controlled elemental substitutions and diverse transition metal complexes (TMCs), which reveal fundamental limitations of generative models in the generalization to previously unseen elements. To address this challenge, we introduce a self-supervised pretraining strategy based on equilibrium conformers that exposes generative TS models to novel chemical environments before targeted fine-tuning. Across the proposed benchmarks, self-supervised pretraining substantially improves TS prediction for previously unseen systems, lowering the median root-mean-square deviation of TS geometries on Transition1x-TMC reactions and reducing fine-tuning data requirements, enabling reliable performance even in low-data regimes.

AVA-Encoder: Towards Agent-Native Video Representation Learning

Wed, 12 Aug 2026 00:00:00 +0000

Creative agents still lack an effective way to learn from high-quality human films, limiting their ability to produce cinematic-grade videos. To address this, researchers propose the Agentic Video Auto-Encoder (AVA-Encoder), a framework for learning agent-native video representations via agentic auto-encoding. AVA-Encoder transforms a video into a knowledge graph (KG) representation and reconstructs it back into video. Its hierarchy and state nodes store structured text, while a linked asset layer holds generated images, audio, and video. Typed edges preserve relations between text descriptions and assets, enabling agents to understand, query, and edit. Reconstruction differences drive a textual-gradient optimization framework, with data-independent encoding policy pseudo-training in the outer loop and optional data-dependent KG refinement at test time. Experiments show AVA-Encoder improves by 20.7 percentage points over the strongest external baseline. In a controlled policy-only setting, its pseudo-trained shot-level Agentic Video Encoder policy outperforms a carefully human-tuned policy while using 74.3% fewer system-prompt tokens. The complete framework, a reliable video reconstruction benchmark, and the first dataset of high-quality film KG representations are released.

Learning contact representations in real-world clutter for universal robotic grasping

Wed, 12 Aug 2026 00:00:00 +0000

Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s42256-026-01292-y Wang et al. design efficient robot–environment interaction representations that achieve generalization across articulated robotic hand models and task adaptability in diverse cluttered grasping scenarios, suggesting a path towards general-purpose robotics.

Accuracy and Order Sensitivity Diverge Under Label-Free Strategies

Wed, 12 Aug 2026 00:00:00 +0000

Multiple-choice benchmarks are widely used to evaluate large language models, but MCQ scores conflate knowledge with sensitivity to option order, making them unreliable measures of model knowledge. This paper tests whether preventing the model from seeing option labels while committing to an answer removes positional influence and improves performance. Two strategies are evaluated: generation-then-matching and scoring options in isolation (the latter positionally unbiased by construction). Neither reliably improves accuracy. A complete decomposition shows the bottleneck is withholding options, not the matching step. The only configuration consistently matching the baseline shows all options paired with an LLM matcher. However, eliminating positional influence entirely still does not reliably yield accuracy gains, while cyclic permutation often improves them. For two-stage prompting, both an aggregate recall-imbalance measure and a direct per-question order-sensitivity measure fail to show reliable debiasing.

What sort of maths are LLMs good at?

Wed, 12 Aug 2026 00:00:00 +0000

For the sake of anyone who might read this blog post in the distant future (a month from now, say), let me mention that I am writing it a few days after OpenAI announced that it had solved ten major problems in mathematics and theoretical computer science, including the first construction of a non-sofic group, and a proof that the multicolour Ramsey number (where there are 3’s) grows superexponentially in . The first was, to judge from various talks I have been to, one of the most important unsolved problems in group theory, and the second was a major open problem in Ramsey theory that I didn’t necessarily expect to see solved in my lifetime, though of course such expectations now have to be revised. The reason I want to be clear about the timing is that I shall be discussing the current capabilities of LLMs in the full expectation that those will continue to change rapidly. So it is likely that in not too long from now, if there is anything interesting in what I write, it will be interesting mainly as a record of what the situation looked like in early August 2026. These results, and the other eight on the list, are extraordinarily impressive, but it still doesn’t seem to be the case that LLMs are better than all humans at all aspects of mathematics. If they were, then their big speed advantage over us would mean that there would be much more of a flood of results. So it is natural to wonder about what kinds of problems LLMs are good at, and about where there is still room for improvement. I don’t pretend to have a good answer to this question, where a good answer would be a crisp classification that would fit the current examples well, but it is an interesting exercise to try to rule out some bad answers, and to try to identify potential answers that aren’t obviously contradicted by the evidence. Are LLMs particularly good at finding counterexamples? A first remark here is that LLMs are not just good at finding counterexamples: they can find proofs of difficult statements as well. However, it is notable that the most famous problems they have solved have almost all been with counterexamples rather than proofs. That is true of the two problems mentioned above, and also of the Jacobian conjecture and the unit distance conjecture. If one wants to theorize that LLMs are particularly good at finding counterexamples, then there are two things it would be good t…

Introducing CARE-X: Towards Clinically Useful Radiology VLMs

Tue, 11 Aug 2026 16:00:00 +0000

Microsoft Research introduces CARE-X, a unified chest X-ray vision-language model (VLM) designed for diverse clinical interpretation tasks. It combines free-text generation with structured prediction to provide both narrative reports and deterministic outputs. The model uses reinforcement learning (DAPO) to reward clinical correctness in a multi-task setting. In a separate research experiment, the team paired Qwen3-VL-4B-Instruct with deterministic measurement tools to evaluate whether direct computation could improve performance on measurement-dependent conditions compared with visual approximation alone. CARE-X was validated on real-world Indian clinical data from Narayana Health, including rare ICU pathologies and CT-confirmed enlargement conditions. It is a research model, not approved by regulatory authorities, and not intended for clinical use.

Interview with Akari Asai – beyond scaling: frontiers of retrieval-augmented language models

Tue, 11 Aug 2026 13:26:36 +0000

In this crosspost from AI Matters, a publication of ACM SIGAI, Ella Scallan interviews Akari Asai, winner of the 2025 AAAI Doctoral Dissertation Award. Asai's doctoral research focused on retrieval-augmented language models (LMs), arguing for moving beyond monolithic LMs to augmented LMs that are designed, trained, and deployed with complementary modules. She pioneered retrieval-augmented LMs, which retrieve relevant knowledge from large-scale text collections at inference time rather than relying solely on memorized training data, enabling more accurate, up-to-date, and verifiable outputs. Her work challenged the prevailing assumption that simply scaling models would solve hallucination and outdated knowledge issues, offering a more effective and efficient alternative. This approach led to OpenScholar, an open-source model aiding scientists in synthesizing vast literature, and has been widely adopted in academia and industry, powering systems like generative search engines.

A universal metamaterial generative model

Tue, 11 Aug 2026 00:00:00 +0000

A study published in Nature Computational Science introduces InfoMetaGen, a universal generative model that provides an inverse design strategy for subwavelength meta-atoms and inhomogeneous metamaterial distributions. The model holds potential to accelerate the design process of metamaterials.

Learning more about Claude's mathematical capabilities

Mon, 10 Aug 2026 17:00:00 +0000

Science Aug 10, 2026 Recently, a member of staff at Anthropic gave Claude an unreasonable challenge. It was about one of the most famous unsolved problems in mathematics: Take a real stab at the Riemann hypothesis. Claude did take a real stab, but as you might have expected if you’re familiar with the difficulty of the task (the Riemann hypothesis dates back to 1859 and has a million-dollar bounty), it didn’t succeed. Nevertheless, during its attempt, it unexpectedly made strides on a related problem. An unreleased research version of Claude has improved on a longstanding lower bound for the fraction of zeros of the Riemann zeta function that satisfy the Riemann hypothesis. Drawing on extensive prior research by mathematicians over the past decades, it has increased this bound from 41.6% to 67.2%. Two mathematicians at Anthropic studied and validated Claude’s paper, and produced an informal note for experts stating Claude’s proof concisely. Claude also produced a formally verifiable proof of its result. We are grateful to Brian Conrey and Dan Goldston, two experts in this area, who generously examined the paper on short notice. We don’t expect that the techniques Claude used will lead to proving the Riemann hypothesis. But its work serves as the latest example of the speed of progress in AI models’ mathematical capabilities. In this post, we discuss how Claude approached this problem and what it found. The Riemann zeta function describes the distribution of prime numbers: each place that the function takes the value of zero contributes successively finer detail to the sequence of primes. The Riemann hypothesis is that the zeros that determine the primes all exist along a certain vertical line. This has become one of the most consequential conjectures in mathematics: many results assume it in order to provide a form of randomness in the primes. No one has yet been able to prove or disprove the Riemann hypothesis, but mathematicians have made progress in many related directions studying the Riemann zeta function and its zeros. One of these, as above, is quantifying a minimum proportion of zeros that are on the line: over time, they’ve gradually increased this known constant proportion to 41.6%. Another direction concerns the distributi…

How generative AI and physics can help design new antibiotics

Mon, 10 Aug 2026 13:43:57 +0000

By 2050, antibiotic-resistant infections could be associated with over eight million deaths annually worldwide. Developing new antibiotics is slow and costly, but generative AI models, guided by scientists, can propose novel molecules, and physics-based simulations can quickly assess their potential as drugs. Peptides like insulin and vancomycin serve as important starting points. A good AI model includes a generator that dreams up millions of designs and a discriminator that filters them. This combined approach offers a cost-effective way to discover new antibiotics.

So How Is AI Drug Discovery Doing, Really?

Mon, 10 Aug 2026 03:34:19 +0000

An important article by field experts points out that despite widespread AI application in drug discovery, evidence of clinically relevant impact remains 'disappointingly limited'. They conclude we are at a stage of 'absence of evidence', not necessarily 'evidence of absence'. Desired outcomes like better decision-making in selecting disease areas, targets, lead molecules, and trial designs are among areas current AI is least equipped to assist. However, there is no fundamental obstacle, but improvements will be slower and costlier than press releases suggest. Even without AI, obtaining good data on these effects is difficult due to natural attribution biases.

Impact-resistant, autonomous robots inspired by tensegrity architecture

Mon, 10 Aug 2026 00:00:00 +0000

Johnson et al. report an autonomous three-bar tensegrity robot capable of robust locomotion across varied terrains, maintaining functionality even after extreme impacts, including a 5.7-meter drop onto asphalt. The findings, published online in Nature Machine Intelligence, highlight the potential of tensegrity architecture for enhancing impact resistance and adaptability in robotics.

Model-guided data production enables reliable reaction prediction

Mon, 10 Aug 2026 00:00:00 +0000

Nature Computational Science, Published online: 10 August 2026; doi:10.1038/s43588-026-01018-5 A newly generated high-throughput experimentation (HTE) dataset is integrated with open-source data to produce a large and high-quality Buchwald–Hartwig reaction dataset for training machine learning models. The combination of HTE and active learning enabled the development of a robust machine-learning model with reliable out-of-distribution predictive power.

Science Signaling | Insilico Medicine Contributes to Research Using AI to Reveal NDRG1-Associated DNA Repair Mechanisms and Potential Synthetic Lethal Effects in Colorectal Cancer

Sun, 09 Aug 2026 07:00:00 +0000

A joint study by Insilico Medicine, the University of Copenhagen, and academic partners identifies NDRG1 as a key regulator of DNA damage repair, demonstrating that targeting this pathway via the antimalarial drug quinacrine triggers synthetic lethality in specific cancer subtypes. Insilico Medicine (HKEX:3696, "Insilico"), a clinical-stage biotechnology company pioneering generative artificial intelligence (AI) for drug discovery and development, announced publication of a new study in Science Signaling. The paper details how computational biology and experimental research converged to uncover a novel target in cancer DNA damage repair centered on the stress-response protein NDRG1. Led by researchers at the University of Copenhagen in collaboration with Insilico Medicine and international partners, the multidisciplinary study combined computational transcriptomic analyses, high-content genetic screening, molecular biology, and large-scale cancer cell profiling. The team demonstrated that pharmacological or genetic disruption of the NDRG1–VCP interaction impairs the recruitment of key DNA repair proteins, triggering synthetic lethality in specific cancer subtypes—most notably colorectal cancers harboring MLH1 or PARP3 mutations. “DNA damage repair is essential to cell survival, making it an attractive target for cancer therapy. Mkrtchyan et al. identified a therapeutically exploitable role for the stress-response protein NDRG1 in the DNA damage response (DDR). High-throughput screens revealed that high NDRG1 expression in cancer cell lines correlated with sensitivity to the antimalarial drug quinacrine, which impaired the ubiquitylation-dependent recruitment of a critical DDR protein to sites of DNA damage. Colorectal cancer cells with mutations in the DNA repair–associated genes MLH1 and PARP3 were particularly sensitive to quinacrine or NDRG1 knockdown. The findings reveal potential strategies to target the DDR based on NDRG1 expression. ” summarized by Leslie K. Ferrarelli, Senior Editor at Science Signaling From Computational Hypothesis to Biological Discovery Cancer cells frequently become dependent on the DNA repair pathways that remain functional after accumulating genetic alterations. Identifying these dependencies can reveal new therapeutic opportunities and help guide precision medicine approaches. The study combined computational transcriptomic a…

Hierarchical Self-Improvement: A Framework for Task-Specific Evolvable Agent Harnesses

Sun, 09 Aug 2026 00:00:00 +0000

This work introduces Hierarchical Self-Improvement (HSI), a framework where a single frozen LLM operates across three hierarchical scopes: a task harness, an evolver that rewrites the harness, and a meta-evolver that rewrites the evolver's strategy. The harness is task-specific and continuously evolvable via a fixed injection seam, using environment feedback. A thinking-on/off design isolates the contribution of harness evolution. Experiments on BALROG with DeepSeek-V4-Flash-Preview as the frozen backbone show consistent gains on moderate-difficulty tasks (BabyAI +39.3, Crafter +33.0, TextWorld +25.0, MiniHack +15.0 in raw % Progress) and strong held-out generalization on BabaIsAI sub-suites (0.98 on BreakStop, 1.00 on GoTo from a 20% unseen split). However, tasks beyond the backbone's capability (NLE) showed no improvement, indicating limits due to feedback fidelity and backbone capability. Code is available.

TutorMoments: Do AI tutors know when to help and when to hold back?

Fri, 07 Aug 2026 08:00:00 +0000

Today we're introducing a preview of TutorMoments, a framework to measure whether cutting-edge LLMs can balance one of the hardest trade-offs in education: when to step in and help a student and when to hold back and let the student do more of the work. TutorMoments is a replay-based evaluation built off real one-on-one math tutoring sessions. Experienced math teachers go through transcripts collected from a U.S. tutoring program and flag the moments where a tutor had to choose between making a problem easier to get started on and pushing the student to do more of the reasoning themselves. TutorMoments then takes the transcript up to that decision point, hands it to a language model, and has the model take over as the tutor in a simulated session – with the student played by another language model – to see what the LLM tutor does. Told only to “tutor well,” we find that models tend to over-help by giving too much support and rarely pushing students to do deeper thinking. Spelling out the trade-off (when to help versus when to hold back) in the tutor's prompt improves performance, but it doesn't close the gap to human tutoring that consistently fits the moment, and LLMs still differ widely in how reliably they make that call. As part of our commitment to open research, we're releasing a dataset of de-identified tutoring transcripts, the code for running our replay pipeline, and the model tutor replays of the key moments we evaluated in those transcripts for reproducibility. We hope TutorMoments gives educators, researchers, and the teams building AI tutors a sharper way to ask how a model handles the pedagogical decisions that matter most—and helps the field build tutors that adapt to each student instead of doing the work for them. What makes a good tutor? Ask a good math tutor for help and you’ll likely get a question back like, “What do you know about what the problem is asking?” That isn't unhelpfulness–part of strong teaching is diagnosing what students do know and providing the right support for them in the moment. Immediately volunteering support would rob a student of the intellectual work that helps them learn. Sometimes support is needed; other times what’s most effective is a push to solidify understanding by explaining a correct answer. Language models, though, are trained to be helpful, and a helpful assistant tends to do the hard part for you—e…

Anthropic Adjusts Fable 5 Biology Safeguards the Same Day Stanford Work Shows AI Can Design Viral Genomes

Fri, 07 Aug 2026 07:00:00 +0000

Anthropic updated its Fable 5 biology safeguards on August 7, the same day researchers from Stanford and the Arc Institute published a paper in Science demonstrating that their Evo 1 and Evo 2 models can design fully functional viral genomes. The timing is not coincidental. The question Anthropic is answering is not whether AI can do biology — Stanford just proved it can — but who gets to control the terms of access. The update reduces biology-related fallbacks on benign queries by roughly 85 percent. Anthropic rewrote and retrained its safety classifier’s constitution to better distinguish everyday health and educational questions — interpreting lab results, understanding symptoms, learning about biology — from dual-use research that remains blocked. Queries touching virology, toxicology, molecular design, and drug design still get routed to Opus 5, a less capable model. Fable 5 is not yet usable for professional biology research or drug development. But the company is building the infrastructure to change that. Anthropic says it is developing trusted access pathways for frontier biology capabilities — a vetted, gated model for researchers who need the full power of Fable 5 without open-ended exposure. The company’s own capability assessments acknowledge that Fable 5 could provide significant uplift to a malicious actor — capabilities they could not find anywhere else. The contrast with Stanford’s work is the structural frame. Evo 2 is an open-weight model, available for public download. As The Guardian and CNN reported, the ability to compose viral genomes with generative AI now exists while governance does not. A companion commentary in Science from Johns Hopkins researchers warned that the study raises urgent biosafety and biosecurity questions. The US Intelligence Community 2026 Annual Threat Assessment explicitly notes that advances in biotechnology could lead to novel biological threats from state actors maintaining offensive weapons programs. Anthropic’s response — restrict the dangerous queries, open the benign ones, build a gated pathway for everything in between — maps directly onto the regulatory asymmetry the company needs. The White House AI Framework, finalized August 4, excludes open-weight models from federal security review entirely. Closed-source labs face 30-day voluntary early access review delays; open-weight developers ship with zero…

A partial digestion of the HRT counterexample

Thu, 06 Aug 2026 18:26:54 +0000

Terence Tao discusses the Heil-Ramanathan-Topiwala (HRT) conjecture, which asserts that a nonzero function with reasonable decay cannot satisfy a finite linear relation among its time-frequency shifts. He notes that the conjecture holds when all shifts are frequency shifts or collinear. For the lattice case, commutativity allows a diagonalization that reduces the relation to a pointwise equation, using restrictions to cosets of integers. The post is a partial digestion of an HRT counterexample, but does not present the counterexample itself.

WeatherNext: AI model achieves breakthrough in forecasting cyclones

Thu, 06 Aug 2026 15:06:15 +0000

WeatherNext: AI model achieves breakthrough in forecasting cyclones WeatherNext enables accurate cyclone forecasts that can give an extra day of warning. Now we are open sourcing the model. Predicting how dangerous cyclones develop is a longstanding challenge where every hour counts. Tropical cyclones — also known as hurricanes or typhoons — are among the most destructive weather phenomena on Earth, responsible for more than 700,000 deaths and $1.4 trillion in economic losses globally over the past 50 years. For forecasters, issuing timely, accurate warnings is a constant race against time. Today, in a paper published in Nature, we show that our WeatherNext AI model achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, our model gives forecasters an extra day’s worth of predictive accuracy: our three-day forecasts are as good as what prior models were able to provide for only the next two days. This scale of improvement corresponds roughly to a decade’s worth of meteorological progress. This collaborative work brought together AI researchers and engineers at Google DeepMind and Google Research, with expert forecasters at the National Hurricane Center (NHC), the Cooperative Institute for Research in the Atmosphere (CIRA), the UK Met Office, and weather agencies around the world. Our research has already had real-world impact. During the 2025 hurricane season, our model helped the NHC to make a historic forecast for Hurricane Melissa by predicting the storm’s rapid intensification and landfall in Jamaica. This enabled the NHC to issue an advance warning, giving teams on the ground critical time to prepare. This year, we continue to work together and are now predicting 1,000 possible scenarios for each cyclone to help support forecasters in their decision-making. Weather affects everyone. Given this broad impact, we are now open sourcing our WeatherNext 2 and WeatherNext Cyclones models used during the hurricane season. By making this technology openly available, we hope to empower the research community and amplify AI's impact in building more resilient communities – whether that be providing local forecasters with the tools they need to prepare for natural disasters, supporting the growth of renewable energy, or anticipating extreme weather. How WeatherNext predicts weather and cyclones Predicting cycl…

Ai2 Expands Collaboration with Hugging Face to Accelerate Open Science

Thu, 06 Aug 2026 08:00:00 +0000

Ai2 expands collaboration with Hugging Face to accelerate open science August 6, 2026 Ai2 Ai2 and Hugging Face are expanding their partnership to make fully open AI easier to access and deploy. Hugging Face serves as a central platform for the AI community, and the expanded collaboration gives us substantially more storage and bandwidth to support our growing portfolio of models, datasets, benchmarks, and applications. Beginning immediately, Hugging Face is roughly tripling our storage on the Hub to nearly two petabytes. Our traffic will also no longer be subject to the Hub’s standard rate limits, allowing even its largest datasets and multi-checkpoint models to download at full throughput. Since spring 2024, our models and datasets have been downloaded more than 50 million times from the Hugging Face Hub. The added capacity reflects the scale of our presence on the Hub—according to Hugging Face’s official heatmap, we publish more new artifacts annually than any other organization it tracks. Releasing model weights is only one part of open research. We also publish training data, intermediate checkpoints, data mixes, evaluations, ablations, demos, and other materials that let researchers reproduce our work and examine how our models were built. The expanded infrastructure will help keep those resources available as our portfolio grows, including forthcoming work on multimodal models, tool-using agents, and open models for scientific communities. "Ai2 has been showing us what 'fully open' looks like, sharing everything end-to-end from training scripts to intermediate datasets behind impactful models like Olmo and Molmo,” says Clem Delangue, Co-Founder and CEO, Hugging Face. “We are proud to be the infra behind Ai2 to enable their impactful work that varies from MolmoAct VLAs for real robots to OlmoEarth tackling climate and many more: 900+ models, 1,200+ datasets at huge scale." "Developing models completely in the open is core to how we work at Ai2, and that means publishing everything leading up to a model – as well as the model itself – so the rest of the field can study and build on it," says Peter Clark, Interim CEO, Ai2. "Keeping those artifacts available and easy to reach takes dedicated storage and bandwidth, and this partnership with Hugging Face strengthens our ability to do that as our portfolio of open releases — across many domains — continues t…

CogTwin: A framework for adaptable digital twins

Wed, 05 Aug 2026 07:51:30 +0000

CogTwin, presented at IJCAI 2025, is a hybrid cognitive architecture framework designed to bring autonomous reasoning and real-time adaptation to digital twins. It addresses the gap in current digital twins that are largely reactive, relying on pre-programmed rules and static models, and thus struggle with unforeseen events. By integrating symbolic AI, sub-symbolic AI, and neuro-symbolic AI, CogTwin aims to equip digital twins with human-like cognitive abilities, enabling them to perceive, reason, learn, and act autonomously in dynamic environments. The architecture features dual knowledge graphs—a static domain knowledge repository (DKR) and a dynamic internal knowledge graph (DIKG)—to balance stability and adaptability, along with a hybrid attention mechanism for focusing on relevant data. This approach shifts the emphasis from simulation and monitoring to genuine intelligence and adaptability, aiming for true cognition in digital twins.

Insilico Medicine Demonstrates AI-Powered Target Discovery for Rare Sinonasal Cancer in NPJ Precision Oncology

Wed, 05 Aug 2026 02:42:57 +0000

Collaborative Study with University of Chicago and Johns Hopkins University Establishes First Multi-Omic Map and Actionable AI Targets for Rare Head and Neck Malignancy CAMBRIDGE, Mass. — Aug 4, 2026 — Insilico Medicine (“Insilico”), a clinical-stage generative artificial intelligence (AI)-driven biotechnology company, today announced the publication of a collaborative study in the journal npj Precision Oncology. The peer-reviewed paper demonstrates how advanced artificial intelligence combined with comprehensive multi-omic analysis can successfully identify promising therapeutic targets in one of the rarest and least understood forms of head and neck cancer. The study represents the first comprehensive molecular characterization of inverted papilloma-associated sinonasal squamous cell carcinoma (IP-SNSCC). Conducted with leading investigators from the University of Chicago and Johns Hopkins University, the research illustrates how AI-powered biological target discovery can generate actionable therapeutic hypotheses even when conventional research is constrained by small patient populations and scarce molecular data. By integrating whole-exome sequencing, RNA sequencing, mitochondrial DNA sequencing, and AI-driven target prioritization, the research team reconstructed the molecular evolution of disease progression while identifying potential therapeutic opportunities for future clinical investigation. "Rare cancers have historically suffered from limited molecular data and few opportunities for therapeutic discovery," said Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine and co-corresponding author of the study. "By combining comprehensive multi-omic profiling with PandaOmics, we were able to generate actionable therapeutic hypotheses in a disease where conventional approaches have struggled. This study demonstrates how AI can accelerate the identification of novel biology and potential therapeutic targets, helping researchers move from molecular understanding toward translational opportunities—even in diseases that have received relatively little attention." By integrating whole-exome sequencing, RNA sequencing, mitochondrial DNA sequencing, and AI-driven target prioritization, the research team reconstructed the molecular evolution of disease progression while identifying potential therapeutic opportunities for future clinical investigation. "R…

Trajectory forecasting lifts the femtosecond ceiling in molecular simulation

Wed, 05 Aug 2026 00:00:00 +0000

A new deep learning method enables molecular dynamics simulations over longer time scales while still achieving accurate physical property prediction. Published online in Nature Machine Intelligence on August 5, 2026, the method promises to overcome the femtosecond ceiling that limits conventional simulation time steps, potentially expanding the applicability of molecular simulations in fields such as materials science and biomolecular research.

Hybrid AI and quantum annealing as an optimization layer in drug discovery

Wed, 05 Aug 2026 00:00:00 +0000

Artificial intelligence has significantly expanded candidate generation in drug discovery, but selecting optimal compounds under real-world experimental constraints remains a major bottleneck. This review examines quantum annealing as a potential downstream optimization layer within hybrid computational workflows. By focusing on constraint-dominated decision tasks formulated as quadratic unconstrained binary optimization (QUBO) problems, the authors analyze how annealing architectures complement AI-generated scores. They further discuss practical hardware limitations, reporting considerations, and the critical role of rigorous benchmarking against classical baselines.

Sarah Urbut: Predicting Your Health Arc

Tue, 04 Aug 2026 15:54:52 +0000

Sarah Urbut is a physician-scientist at Harvard Medical School and the Massachusetts General Research Institute. She and her colleagues published a landmark paper in Nature on patient health journeys for 3 different cohorts—the Mass General Brigham patients, the UK Biobank, and the All of Us research program—with a cumulative 683,000 individuals, 348 diseases, and up to 52 years of follow-up. Integrating electronic medical records and polygenic risk scores (N=36). An AI model was developed and validated called ALADYNOULLI, with each individual represented latent disease signatures (N=21), mathematically and biologically driven. An example of a patient’s dynamic life journey (health arc) is provided in the Figure below (adapted from the paper, made with NotebookLM). The longitudinal story for each individual is key here. Using a Gaussian process, the risk continually shifts like a GPS navigation when you make a wrong turn and you are re-routed. Considerable effort was dedicated to addressing selection bias with inverse probability weighting. Share Ground Truths Here are 20 temporal pattern signatures for the UK Biobank cohort, which help to provide the biologic mechanism of a person’s disease, as shown for multiple conditions like heart attack, breast cancer, and depression. That is, the same disease phenotype can link to different genomic pathway underpinnings. An example for breast cancer could be related to signature 8 or signature 7; the latter tied to inflammation and metabolic abnormalities. This biological pattern could also be used to detect likely medication failure (such as SSRI treatment for depression) and predict rare diseases. Subscribe now In fact, the model was used to discover genomic signatures for different diseases, as shown below. This model predicted 1 and 10-year risk of diseases at the individual level and far surpassed routinely used clinical prediction calculators such as the pooled cohort equation (PCE) for coronary artery disease (0.89 vs 0.68, respectively, 1-year risk ) and the GAIL model for breast cancer (0.783 vs 0.54, respectively). ALADYNOULLI also surpassed accuracy for precision when compared with the recent Delphi-2M model that I wrote about here The ALADYNOULLI paper is packed with information and equations and is not easy to get through without a sophisticated math background. That’s why the summary infographic below (…

Improving tropical cyclone intensity forecasts with a simple AI correction

Mon, 03 Aug 2026 09:00:00 +0000

Tropical cyclones (TCs) are among the most destructive weather systems. Improving forecasts of their track and intensity is critical for helping communities to prepare for severe winds, storm surge and flooding. Forecasting TCs has been one of the more demanding tests for machine-learning weather models. Artificial intelligence (AI) weather models have made remarkable progress in predicting the tracks of TCs. However, forecasting their intensity poses a much greater challenge, with models exhibiting a persistent tendency to underestimate storm strength. Much of this difficulty stems from the need to accurately represent the compact, often rapidly evolving inner core, where small-scale processes can strongly influence storm evolution and trigger rapid intensification. A new collaboration with Anna Allen, Wessel Bruinsma and Richard Turner at the University of Cambridge explores a simple way to address this problem. Rather than building a new TC forecasting model from scratch, the approach starts with the existing Artificial Intelligence Forecasting System (AIFS) forecast and learns how to correct its intensity errors. The result is a system that, when tested on TCs from the 2025 season that were not used in training, achieves performance comparable to both leading operational guidance and AI state-of-the-art systems in forecasting maximum wind speed and minimum central pressure. Correcting the intensity weakness rather than rebuilding the model The approach, called AIFS-TC, is based on a straightforward idea. The AIFS forecast provides an initial estimate of a storm's future intensity. A second machine-learning system then learns the errors that AIFS tends to make and predicts a correction to that forecast. The correction uses information already contained in the AIFS forecast. This includes the predicted track and intensity of the storm, as well as three-dimensional atmospheric fields such as wind, temperature, geopotential and humidity in a region around the cyclone. The correction is then applied to the original AIFS intensity forecast. This is particularly useful because the underlying problem is clear. At around 0.25° resolution, the AIFS grid is too coarse to fully resolve the inner core of a TC, and the deterministic training methods further prevent AIFS from skilful TC intensity predictions. The resulting forecast tends to smooth the strongest winds…

OpenAI Announced Its Next Frontier Model 'Astra'

Mon, 03 Aug 2026 07:00:00 +0000

How OpenAI Announced Its Next Frontier Model 'Astra' The US AI firm OpenAI, led by CEO Sam Altman, has announced its latest model, Astra. In a shocking introduction to the AI model, the company shared a selection of 10 results, each of which resolves or makes substantial progress on a long-standing open problem. The problems span high-dimensional geometry, coding theory, arithmetic circuit complexity, group theory, operator algebras, quantum complexity, lattice cryptography and extremal combinatorics. OpenAI’s next major model The US AI giant says the results were achieved by “an internal version of Astra”. Sam Altman, OpenAI’s CEO, is believed to have shown US politicians a demo of the Astra system while visiting Washington last week, according to reporting from The Telegraph. Noam Brown, a Research Scientist at OpenAI, wrote in a post on LinkedIn about the development: “An internal version of Astra, OpenAI’s next major model family, solved 10 major open problems in mathematics, quantum complexity and theoretical computer science. “We’re excited to see what scientists and researchers are able to create with our upcoming Astra models!” The research problems solved The model covered advancements in high-dimensional sphere packing, binary and spherical codes, non-sofic groups, Connes’s rigidity conjecture, arithmetic circuit complexity, quantum parallel repetition, closest vector problem, Ehrhart’s volume conjecture, multicolor Ramsey numbers and extremal number conjectures. On Conne's rigidity structure, the model offered a disproof of a longstanding conjecture that certain groups are uniquely determined by their von Neumann algebras. Meanwhile, the high-dimensional sphere-packing set New upper bounds on sphere-packing density down to the Cohn–Elkies threshold. “I'm in awe, and excited to see what other things we will learn from the AIs. There are a number of problems I've spent a long time thinking about, and maybe I will learn how to answer them soon,” Henry Yuen, Associate Professor at Columbia University, wrote on X. Henry said: “And then - the kicker - something that I personally spent a couple years on in grad school, leading to some of my proudest work: quantum parallel repetition theorems. I spent many hours, days, nights, weekends in cafes, in my office, at home, trying to understand Ran Raz's classical parallel repetition theorem and whether I coul…

Healthcare benchmarks are only as good as their assumptions

Mon, 03 Aug 2026 06:13:18 +0000

In healthcare settings, LLM performance differs between evaluation and deployment, with a recent study finding a 61 percentage point gap. The authors argue this gap arises not from poorly designed benchmarks but from implicit assumptions in evaluation protocols that fail at deployment. They propose a taxonomy categorizing assumptions into task and outcome types to diagnose where the gap arises and what is needed to close it. Closing the gap requires making assumptions explicit, testing them, and updating protocols accordingly. The findings highlight the need for evaluation paradigms that align with real-world deployment.

Reinforcement learning steers generative crystal design

Mon, 03 Aug 2026 00:00:00 +0000

Nature Machine Intelligence, Published online: 03 August 2026; doi:10.1038/s42256-026-01282-0 Generative machine learning methods have led to progress in crystal discovery, but cannot fully explore the space of material candidates that are both novel and useful. A reinforcement learning-based method steers candidate generation to these areas, enabling the design of novel functional materials.

OpenAI says AI system advances 10 major math problems

Sun, 02 Aug 2026 07:00:00 +0000

OpenAI says an internal version of its next major artificial intelligence model has produced new results on 10 longstanding problems in mathematics and theoretical computer science, highlighting the growing role of AI systems in advanced scientific research. The company said the results were generated by an internal version of Astra, described as its next major model. The issues had remained open with no progress on the main result for at least a decade in each case, and in many instances much longer.. The research spans areas including high-dimensional geometry, coding theory, group theory, operator algebras, quantum complexity, lattice cryptography and extremal combinatorics. OpenAI said the model produced results for issues of substantial interest to their respective mathematical communities, with several also having broader relevance across mathematics. Among the results, the system produced new upper bounds for high-dimensional sphere packing that reach down to the Cohn-Elkies threshold. It also generated improved bounds for the maximum size of binary codes at prescribed minimum distances, alongside related results for high-dimensional spherical codes. In group theory, OpenAI said the work included a construction establishing the existence of non-sofic groups, addressing a longstanding open question. The system also produced a disproof of Connes’s rigidity conjecture, which concerns whether certain groups are uniquely determined by their von Neumann algebras. Other results involved arithmetic circuit complexity, including new lower bounds for computing the permanent using arithmetic circuits and formulas. OpenAI said the work included an arithmetic-formula lower bound of order n⁴/log n. READ: AI just got cheaper: OpenAI slashes prices by up to 80% (July 31, 2026) The model also produced an exponential parallel repetition theorem for general two-player quantum games, extending a foundational principle from classical complexity theory. In lattice-based mathematics, the research produced a polynomial-factor hardness of approximation result for the closest vector problem, a foundational question with connections to post-quantum cryptography. The remaining results covered Ehrhart’s volume conjecture, multicolor Ramsey numbers and extremal graph theory. OpenAI said the work on multicolor Ramsey numbers produced a superexponential lower bound for multicolor t…

Ten advances in mathematics and theoretical computer science

Sat, 01 Aug 2026 00:00:00 +0000

OpenAI released ten results on long-standing problems together with human-prepared manuscripts and Lean certificates.

Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling

Fri, 31 Jul 2026 00:00:00 +0000

The development of 0.1^{circ} global weather forecasting models based on machine learning (ML) is constrained by the limited availability of high-resolution data, as decades of reanalysis are only available at 0.25^{circ} resolution. While existing approaches fine-tune 0.25^{circ} forecast models on limited 0.1^{circ} samples, we show that this transfer is hindered by the irreversible information loss inherent in coarse-resolution forecasting. Therefore, we propose BaguanHR, a framework that shifts the focus from transferring models to transferring data. We first show that super-resolution (SR) has lower conditional entropy and input amplification than forecasting, making it a more robust vehicle for resolution transfer. By leveraging this advantage through variable-wise SR, we synthesize extensive 0.1^{circ} data from ERA5. BaguanHR's performance on the synthetic-plus-real dataset exceeds both ML-based methods and IFS-HRES, achieving superior performance across over 85% of the lead times within 72 hours. Furthermore, our findings highlight a power-law scaling effect, as a twofold increase in data reduces RMSE by 4.6% for 72-hour forecasting and 4.9% for 120-hour forecasting. Our results demonstrate that scaling high resolution ML-based forecasting is primarily a data bottleneck, and that variable-wise super-resolution provides a simple yet general solution to unlock long coarse-resolution reanalyses for high-resolution training.

Echoverse: Deep, Evolving Environments for Computer-Use Agents

Thu, 30 Jul 2026 17:00:00 +0000

Microsoft introduces Echoverse, a suite of twelve training worlds for computer-use agents: ten deep domain worlds and two capability worlds, each drilling a single control (e.g., date pickers, nested filters) in varied forms. Depth is key: these worlds replicate real application behavior, come seeded with realistic data, and maintain coherent state across screens and users. Training on all twelve nearly doubles a 9B model's base score from 36.5% to 67.1%, coming within fourteen points of GPT-5.4. Lessons include: high simulation fidelity is essential, as shallow worlds hurt performance; agents struggle with common UI elements, but varied drilling teaches generalization to unseen domains; co-evolving the model, world, and verifier improves all; and reinforcement learning with a grounded verifier as reward surpasses imitation, improving held-out performance and reducing steps to goal. Four worlds are released with code, data, and grounded graders.

EvoLib: Turning Experience into Evolving Knowledge

Thu, 30 Jul 2026 16:00:00 +0000

Microsoft Research introduces EvoLib, a framework that enables large language models to learn from their own experience during inference without ground-truth labels or external feedback. EvoLib transforms past attempts into reusable skills and reflective insights, continually refining, consolidating, and reweighting them to turn instance-specific observations into increasingly general knowledge over time. This evolving knowledge transfers across tasks, helping AI models learn from successes and failures, and consistently improves downstream performance. Since EvoLib does not require model updates, it can be applied to any black-box language models and AI systems deployed through APIs.

Humans can be trained to spot AI faces, helping fight deepfake fraud

Thu, 30 Jul 2026 08:31:23 +0000

A study led by the Australian National University (ANU) Emotions and Faces Lab has shown that humans can be trained to improve their ability to spot AI-generated faces. Deepfake faces have become so realistic that they are hard to distinguish from real photos, contributing to AI-related fraud. The training, which focuses on six perceptual qualities—distinctiveness, memorability, proportionality, symmetry, attractiveness, and expressiveness—improved all participants' detection accuracy, with high performers achieving near perfection. The training was effective for StyleGAN faces, and researchers are now testing its generalizability. The study was successfully replicated by a team at the University of Victoria, Canada.

Not All Bees Pollinate: Using Machine Learning to Predict Ecological Roles

Wed, 29 Jul 2026 14:20:29 +0000

Image credit: José Neiva Mesquita-Neto Understanding Pollination Through Ecological Function Pollination is one of the most important ecological processes on Earth, but understanding how different insects contribute to it remains surprisingly difficult. While many bee species visit flowers, they do not all transfer pollen. Distinguishing between different ecological functions has traditionally required labor-intensive field experiments that are difficult to scale. At our co-organized 2025 NeurIPS Workshop on AI for Non-Human Animal Communication , Alef Iury Siqueira Ferreira shared how machine learning can help shift the focus from identifying which species visited a flower to understanding the ecological role it played. You can see the paper here . Drawing on expertise in ecology and machine learning, the team developed acoustic models that classify bees according to their ecological function, offering a scalable way to study pollination. In this Community Spotlight, Alef discusses the challenges of measuring pollination in the field, why function-based monitoring may provide deeper ecological insights than species inventories alone, and how machine learning could help researchers better understand the complex relationships between pollinators, plants, and the ecosystems they support. What challenges do researchers typically face when trying to measure pollination quality in the field? While many insects visit flowers, they play different ecological roles. Some transfer pollen between flowers, contributing to plant reproduction, while others primarily collect nectar or pollen without transferring pollen in the same way. Simply observing that an insect visited a flower, or even identifying it taxonomically, does not directly tell us how much it contributed to pollination. In practice, measuring pollination quality often requires biologically grounded validation, such as single-visit pollen deposition experiments, which are labor-intensive, time-consuming, and difficult to scale under real field conditions. This is why assessing pollination in the field remains challenging: the real question is not only who visited the flower, but what ecological function that visitor actually performed. How did collaboration between ecologists and machine learning researchers shape this project? The collaboration between ecologists and machine learning (ML) researchers was…

Fields Medalist Terence Tao warns AI could produce more math proofs than humans can handle

Wed, 29 Jul 2026 07:00:00 +0000

Tao, 51, an Australian and American mathematician recognized for his contributions to partial differential equations, combinatorics, harmonic analysis, and additive number theory, made the remarks in his lecture, "Mathematics in the Age of AI," delivered at the International Congress of Mathematicians (ICM) last weekend. His speech drew widespread attention from the global mathematics community. He began by reflecting on mathematics between 1900 and 1930, when breakthroughs such as Russell's paradox (1901) and the Gödel incompleteness theorems (1931) forced mathematicians to reexamine the discipline's underlying assumptions. He said this "was a turbulent period for mathematics, but the end product was extremely valuable: an explicit, rigorous, and standardized foundational framework." Noting that there is scope for further improvement, he stated: "I believe we are entering a similarly turbulent period, a crisis in the foundations of mathematical values and practices." Tao cited his First Proof experiment, conducted in May, in which frontier AI models successfully solved seven out of 10 novel research-level mathematics problems under controlled scientific conditions. The results were refereed by experts for both correctness and exposition, with the resulting proofs considered to be of publishable quality. Based on those results, he developed a "Working Hypothesis" predicting that "AI tools will, reasonably soon, become capable of performing a reasonable fraction of research-level mathematical tasks, with reasonable levels of success, quality, supervision, and cost." Assuming the Working Hypothesis holds, he argued that the central question will shift from what AI is capable of to: "What are the precise goals, objectives, and values of our mathematical community, and the enterprise of mathematical research?" Traditionally, those goals have included solving unsolved pure and applied problems, developing new theories and techniques, and understanding the world. Additional goals involve building a community of mathematicians, training the next generation to guide future directions, contributing to a shared network of mathematical knowledge, and creating enduring works of aesthetic value. In the past, these goals have been largely positively correlated, with progress in one typically leading to progress in the others, Tao explained. However, he warned that "all m…

The OlmoEarth Platform: Geospatial inference at planetary scale

Tue, 28 Jul 2026 08:00:00 +0000

The OlmoEarth Platform: Geospatial inference at planetary scale July 28, 2026 Ai2 The OlmoEarth models are our family of Earth observation foundation models, pretrained on roughly 10 terabytes of multimodal satellite data. Governments, NGOs, and other mission-driven organizations are already adapting OlmoEarth for applications including deforestation monitoring, food security, and wildfire risk. At Ai2, we know how to train and release powerful open models, and for organizations with strong engineering teams, an open model is all they need to run with. But most organizations in the environmental space – the ones best placed to apply these models – don't have the infrastructure or engineering teams that can manage the full lifecycle: labeling data, fine-tuning models, and running large-scale inference. We’ve spent more than a decade operating platforms like Skylight and EarthRanger, software that users around the world rely on every day, so it has to work every day. That experience taught us what delivering impact takes: running models cost-effectively at the right time and place, monitoring performance, turning raw outputs into actionable insights, and verifying those outputs drive the outcomes partners want. That’s why we built the OlmoEarth Platform: infrastructure for taking geospatial models from fine-tuning and evaluation to large-scale inference. Inference at this scale presents its own set of challenges. Satellite imagery must be found and accessed across multiple providers, aligned across projections and resolutions, and processed efficiently. Results then have to be stitched into geographically consistent maps while the infrastructure recovers from the routine failures of distributed computing. Today, the platform can run inference across continent-scale areas in roughly a day, processing dozens of terabytes of imagery at a cost of fractions of a penny per square kilometer. Developing it meant confronting a series of engineering challenges that others working on large-scale geospatial systems are likely to encounter as well. This post walks through those challenges and the solutions we arrived at. Why satellite inference is challenging Most ML models take in a few megabytes of data and produce a result in under a second—think LLMs processing a paragraph of text or computer vision models analyzing a photo from a smartphone. Earth observation inferen…

Physics-Induced Deep Learning Unveils Hidden Tissue Composition for Precision Diagnosis

Tue, 28 Jul 2026 00:00:00 +0000

Liu et al. present a physics-induced deep-learning approach that extracts tissue composition information from routine single-energy CT scans. By incorporating X-ray imaging principles into model design, this approach improves material characterization and enhances performance across diverse clinical tasks. The framework expands the diagnostic value of standard CT imaging and supports more accessible precision medicine.

Valid Per-Field Selective Risk Control for Document Extraction: Three Failure Modes, a Validity Ladder, and When Conditioning Pays

Tue, 28 Jul 2026 00:00:00 +0000

Per-field accept/review with selective risk control at most alpha is the trust contract for document extraction, but the natural procedure silently violates it on real documents. On 13,859 genuine claude-sonnet-5 fields from 800 CORD receipts (49.0% correct), we diagnose three failure modes: document clustering (design effect 1.84–2.45), score-refit leakage (coverage 0.416 at risk 0.127, violating alpha=0.10 in 95% of splits), and a tie-mass pathology (a degenerate score collapses the threshold grid, 0.030 to 0.001). We organize fixes as a validity ladder with stated guarantees per tier. A fit/val split restores expected-selective-risk control for a learned fusion: coverage 0.318 at risk 0.096 at nominal alpha=0.10, but realized risk exceeds alpha in 47.5% of resplits. Mondrian Learn-then-Test with exact binomial tails yields per-group PAC certificates: field-iid 0.171 at risk 0.068, cluster-corrected 0.140, doc-iid 0.060—the only tier matching documents, honestly near-vacuous today. Support-bin, the pre-specified provenance taxonomy, wins every rigor tier on the sonnet CORD capture (p < 1e-4, Bonferroni-corrected), but does not replicate under haiku or qwen; on higher-accuracy corpora pooled thresholds win. A blind three-annotator human-gold audit verifies the practical tier's accepted-set risk at 1.3% against its 10% budget (Fleiss' kappa=0.83).

Why Models Are AI’s Next Training Dataset with Damian Borth - #772

Mon, 27 Jul 2026 21:40:00 +0000

As high-quality training data becomes scarce and pretraining costs rise, Damian Borth, professor of AI at the University of St. Gallen, argues that we've overlooked a key knowledge source: already-trained models. His group's work on weight space learning treats trained neural networks as data, learning from the distilled results of millions of GPU hours of optimization rather than starting from raw data. This episode explores what it means to build foundation models of neural networks, how knowledge can transfer across architectures and domains, how this approach could cut the cost of specialized model development, and whether future AI systems might be trained on collections of models rather than ever-growing datasets.

Towards experiment-guided AlphaFold

Mon, 27 Jul 2026 10:09:59 +0000

Researchers at ISTA and international collaborators have developed a method to guide AlphaFold with experimental data, enabling the modeling of protein structure ensembles that better reflect physical and biological reality. Although AlphaFold predicts protein 3D structures with remarkable accuracy, it tends to reduce heterogeneous structures to a single dominant conformation and overlooks experimental conditions affecting local structure. Published in Nature Biotechnology, the approach highlights the functional importance of protein motions and paves the way for improved predictive models. The team, led by Alex Bronstein, emphasizes that proteins are highly dynamic molecules, and modeling this dynamism is crucial for understanding their functions.

Thoughts about the Leiden Declaration

Sun, 26 Jul 2026 16:54:31 +0000

The author recounts attending a workshop on mathematics and AI, which produced the Leiden Declaration, now signed by over 3000 people. Although he did not sign, he clarifies it is not due to disagreement but rather uncertainty about some confident assertions and recommendations. He appreciates the declaration's role in focusing attention on pressing issues, and quotes its passage on characteristic values of mathematical research, including the role of proof, attribution, transparency, and independent verification.

AI Listens In to Help Protect Wildlife

Fri, 24 Jul 2026 11:20:44 +0000

The air in forests is filled with animal sounds, containing rich information about biodiversity. However, analyzing the massive recordings collected by autonomous devices is a huge challenge. Dan Stowell, a researcher at Naturalis Biodiversity Centre in the Netherlands, leads BioacAI, a four-year EU-funded project with European and UK partners, to develop AI tools that automatically identify species from sound recordings, aiming to monitor biodiversity more efficiently at large scales. The project also addresses a skills gap by training doctoral students in a 'full stack' of bioacoustic AI skills, spanning acoustics, AI, zoology, and ecology.

Who gets to understand AI?

Fri, 24 Jul 2026 08:00:00 +0000

As AI becomes critical infrastructure for science, medicine, industry, and government, one question matters more than ever: who gets to understand how these systems work? At Ai2, that question is central to our work, and it’s what motivates our commitment to open science and open models. For us, openness is more than a licensing choice—it shapes how we build, release, and evaluate our work. Open-weight models let people use and adapt AI for their own research and applications. They give organizations greater choice, reduce dependence on any single provider, and lower barriers to developing new applications and scientific tools. With our fully open releases, we go further by sharing the training data, code, methods, checkpoints, evaluations, and documentation behind the models. We do this because we believe the ability to understand how AI works shouldn't be locked up in a few hands—it should be available to researchers and institutions working to advance scientific knowledge. This is what open science looks like in AI: producing research that others can inspect, test, and extend. Trust requires open evidence Researchers need more than access to a model’s outputs. They need the data, code, checkpoints, evaluations, and training process behind it to reproduce results, investigate behavior, test limitations, and assess claims about safety and reliability. Trust comes from allowing independent researchers to examine the evidence rather than relying solely on a developer’s assurances. The clearest measure of open research’s value is the new science it makes possible. By releasing the evidence behind our models, Ai2 enables independent researchers to examine questions that closed systems leave difficult – or impossible – to investigate. For example, researchers at Northeastern and Johns Hopkins have used our Olmo models’ weights, internal representations, training data, and provenance to study demographic bias in clinical applications and whether models’ stated knowledge cutoffs match the information they actually rely on. Researchers at Arb Research and collaborating universities used Olmo 3’s training data and intermediate checkpoints to show that paraphrased copies of benchmark questions can inflate apparent model progress. Elsewhere, scientists at the University of Texas at Austin, Northeastern, and MD Anderson Cancer Center used Olmo 3 and infini-gram, Ai2’s…

UniPert-G2CP bridges genetic and chemical screens from molecular representation to phenotype modeling

Fri, 24 Jul 2026 00:00:00 +0000

UniPert-G2CP is an AI framework for jointly modeling molecular-level perturbation causes with phenotypic-level cellular effects across genetic and chemical domains. These complementary views enable more accurate perturbation prediction, mechanistic discovery, and efficient in silico drug screening.

Characterizing Consensus in Networks of Agents

Thu, 23 Jul 2026 08:43:49 +0000

A Belief Flow Network (BFN) is a directed graph where nodes represent agents holding epistemic states—complex logical beliefs including conditional statements. Agents exchange information asynchronously, revising their beliefs via iterated belief change operators, potentially with different rational policies (some skeptical, some credulous). The research asks whether consensus is inevitable if influence can eventually reach every agent, and if so, what exact beliefs they can agree upon. Unlike averaging numerical opinions, logical belief revision must preserve prior beliefs while accommodating new inputs. This builds on decades of work in belief revision, iterated belief revision, and improvement operators.

Accelerating Scientific Discovery: Google's $40M Commitment to the Genesis Mission

Wed, 22 Jul 2026 13:38:54 +0000

Accelerating the frontiers of scientific discovery: Google’s $40M commitment to the Genesis Mission Pushmeet Kohli VP for Science and Strategic Initiatives, Google DeepMind, and Chief Scientist, Google Cloud Karthik Narain Chief Product and Business Officer, Google Cloud Google AI for Public Sector Empowering an Adaptive, Responsible, Secure, and Intelligent way forward Learn more Scientists today face challenges of extraordinary scale and complexity. From shaping and simulating the intricate dynamics of fusion plasma, to exploring the vast search space of new materials, to making sense of the exabytes of data pouring out of the world's most advanced experimental facilities. The demands on modern research are unprecedented. Frontier AI can help address these challenges, while accelerating groundbreaking scientific discoveries. In December, we shared our commitment to the White House's Genesis Mission — the national effort to harness AI and double the pace of American scientific discovery within a decade. Since then, Google DeepMind (GDM) announced an early access program that provides AI for science tools to all 17 Department of Energy (DOE) National Laboratories, and Google Public Sector shared how Gemini for Government could serve as an AI backbone for the DOE. Today, at the DOE Genesis Mission Summit 2026, we are expanding this by committing $40 million of AI tokens and cloud credits for researchers in support of the Genesis Mission. Frontier AI tools for scientific discovery Under this expanded commitment, we will first provide DOE’s Genesis Mission awardees in-kind access to GDM’s frontier AI for science portfolio, including: - AlphaEvolve — a Gemini-powered coding and discovery agent, for designing advanced algorithms. - AlphaFold 3 — a model for predicting the structure and interactions of proteins and other biomolecules. - AlphaGenome — a tool for understanding how variation in DNA, including the non-coding genome, shapes biology and disease. - WeatherNext — a state-of-the-art family of AI weather forecasting models for mapping weather conditions. - AlphaEarth Foundations — a foundational AI model for mapping and understanding our planet in unprecedented detail. Second, we will provide Gemini for Government seats and tokens for one year to tens of thousands of users across the DOE National Laboratories’ operations, research, and management teams. Th…

Anyone can fake a scientific image with AI, tricking even academic journals – and undermining trust in science

Wed, 22 Jul 2026 07:00:00 +0000

In April 2026, a NASA Artemis II photo of Earth caught public attention, but AI can fabricate similar images in seconds, raising trust issues. The author, a visual science communication researcher, notes that AI-generated images infiltrate science: used for illustrations, synthetic data, editing lab images, and outreach, blurring lines between illustration, enhancement, and fabrication. In 2024, two papers were retracted for AI-generated biologically impossible structures; in April 2026, NEJM retracted a paper due to AI-manipulated clinical image. Publishers are adopting AI-detection tools, but detectors lag behind generators and only recognize trained patterns; these cases may be the tip of the iceberg.

A Digestion of the Jacobian Conjecture Counterexample

Tue, 21 Jul 2026 21:04:44 +0000

The notorious Jacobian conjecture can be formulated concretely over the complex numbers as follows. Conjecture 1 (Jacobian Conjecture) Let be a polynomial map in complex variables, whose Jacobian is a non-zero constant. Then is invertible (with polynomial inverse). The condition that the Jacobian is non-zero is equivalent to being locally invertible. (The implication of local invertibility from non-vanishing Jacobian follows from the inverse function theorem; the converse implication can be derived from the Weierstrass preparation theorem , but is omitted here; see also Lemma 5 of this previous blog post .) Also, from the fundamental theorem of algebra, once the Jacobian polynomial is non-zero, it must be constant. So the hypothesis “Jacobian is a non-zero constant” can be replaced with “ is locally invertible”. So the Jacobian conjecture can be viewed as an assertion that local invertibility implies global invertibility. The complex numbers can be easily replaced with other fields of characteristic zero by the Lefschetz principle , but I prefer to work in the concrete setting of the complex numbers. It was recently shown (using the Fable AI) that the conjecture is false in three dimensions (and thus in higher dimensions as well): Theorem 2 (Counterexample to conjecture) There exists a polynomial which has non-zero constant Jacobian, but is not invertible. The conjecture remains open in two dimensions, and is easy to establish in one dimension. The example can be stated completely explicitly: one can take and one can verify by a brief calculation that and While this is an extremely quick verification, the construction presented in this fashion appears like a massive miracle. The polynomial has degree seven, so a priori the Jacobian ought to be a polynomial in three variables of degree as large as , so the fact that all non-constant coefficients of this polynomial vanish looks like a massive cancellation involving equations, which is much larger than the degrees of freedom for a generic degree seven polynomial map of three variables. So finding such a polynomial looks highly unlikely to be located by brute force. The example has since been retroactively explained in more geometric terms . As a “digestion” exercise to myself, I sought to write this explanation with relatively little use of algebraic geometry, in a manner that minimizes the amount of “miracles…

Expanding Protein Language Modeling to Protein-Protein Interactions with MSA Pairformer

Tue, 21 Jul 2026 00:00:00 +0000

Protein language models have excelled at modeling individual proteins, but extending these capabilities to protein complexes remains a major challenge. MSA Pairformer, a parameter-efficient protein language model, generalizes to protein-protein interactions despite being trained exclusively on monomers, enabling accurate prediction of interface variant effects and subfamily-specific homo-oligomeric binding modes.

Patrick Hsu: How Is AI Catalyzing Life Science?

Mon, 20 Jul 2026 23:37:04 +0000

“The future of biology is programmable.” To give you a sense of how important this topic is, Patrick Hsu is the first guest we’ve had back to Ground Truths since the podcast began a few years ago. Nearly 2 years ago we spoke about digital biology but so much has happened since to help unravel the complexities in the language of life. Patrick is a co-founder and core investigator at the Arc Institute . His work is at the i ntersection of biology, engineering, and AI. At the ripe old age of 33, he’s a pioneer of genome editing technologies and my go-to for tracking progress in digital biology. Besides that he’s a great explainer with frequent use of metaphors and simplification of very complicated stuff. Share Ground Truths Here’s what we discussed with the relevant links: —Our recent review in Nature Biotechnology on the large language of life models (LLLMs). GBAI-generalist biological artificial intelligence —The 2 multi-agent AI Nature papers in May for accelerating drug discovery: Full House (Robin) and Google Deep Mind (Co-Scientist) Multiple drug candidates were described as an outgrowth of multi-agent AI , with a 200-fold decrease in workflow time. Below Figure from synthesis of the Robin and Co-Scientist papers: “AI devises hypotheses and ways to test them,” Nature News and Views, by Oliver Elemento Subscribe now — Biomni published in Science 9 July An infographic by NotebookLM below helps give you a sense of what—a diversity of important tasks— accomplished by Biomni Share —New workbench offerings by the AI companies including Anthropic’s Claude Science, Open AI, Google DeepMind and the competition with academic spinoffs — Spud Cells — Virtual Cell project , which it’s such a big deal, and the importance of perturbations —Alzheimer’s disease with OpenAI for finding mechanistic new targets for drug discovery — The Nature Editorial —Mid-Journey’s Ultrasound Imaging of the “Whole-Body” (and we’re not imaginative enough, the need to think big) —Mitigating the worries about humans as lab assistants and nefarious use to produce bioweapons ******************************************************************************* A big thanks to Ground Truths subscribers from every US state and 212 countries. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you’re not a subscriber, please join! If you found this…

Human mathematicians are being outcounterexampled

Mon, 20 Jul 2026 09:53:22 +0000

It’s been an interesting few weeks for counterexamples. This post is basically my perspective of what has been going on in the world of formalization, AI tools and, in particular, counterexamples. Unit distance Two months ago today (20th May 2026), ChatGPT disproved Erdős’ Unit Distance conjecture in discrete geometry. This is now old news but I had to start somewhere. The announcement was accompanied with testimonies by human mathematicians, many of whom I knew and a few of whom I trusted, saying that they believed the argument (they had been given early access to it and had checked it). The basic structure of the proof is that a profound theorem in number theory due to Golod and Shafarevich from the 1960s could be used to construct a counterexample to the conjecture. It is now 9 years since I had a mid-life crisis, realised I no longer trusted many human mathematicians when it comes to technical details, discovered Lean, and started to argue that interactive theorem provers should play an important role in the future of mathematics. So of course my first question was “is the counterexample formalized in Lean”. The answer was “no”. But under a week later (26th May 2026), I got an email from Fields Medallist Mike Freedman. Mike is now the Chief Science Officer for Logical Intelligence , a company cofounded by Turing Award winner and “godfather of AI” Yan LeCun. Mike informed me that their system had autoformalized the entire ChatGPT-generated paper in Lean and could I take a look. I looked, and my post-doc Thomas Browning looked too. And indeed this was what Logical Intelligence had done: they had formalized precisely the statement that the profound theorem of number theory implied the Erdős counterexample. Breakthrough LLM-generated mathematics being formalized in real time. Interesting data point. Of course there is an elephant in the room here though, the profound theorem of number theory which takes 100+ pages to prove (it needs huge chunks of global class field theory, a theory developed at the beginning of the 20th century and for which there are still no short proofs; it is proving difficult to compress). In 2025 I had run a Clay Summer School with Richard Hill on the formalization of class field theory, and one year later we have nearly done the local case (it is the current PhD project of my student Edison Xie); the global case remained open, and i…

AI-optimized zinc-finger proteins enhance miniature base editors

Mon, 20 Jul 2026 00:00:00 +0000

AI-guided protein design was experimentally tested as a way to improve the efficiency and targeting range of compact base editors.

Interactive World Simulator for Robot Policy Training and Evaluation

Fri, 17 Jul 2026 16:32:40 +0000

Traditional robot learning relies on collecting demonstrations and evaluating policies on real robots, which is slow, expensive, and hard to reproduce. We propose an Interactive World Simulator: a learned, action-conditioned video prediction model that predicts subsequent pixel frames given the current image and robot actions, without a physics engine. On a single RTX 4090, it runs for over 10 minutes at 15 FPS, producing stable and physically plausible video. This enables cheap data generation and scalable, reproducible policy evaluation. We trained the simulator on four tasks spanning different physical regimes: T pushing (rigid-body contact), rope routing (deformable-rigid interaction), mug grasping (fine-grained gripper dynamics), and pile sweeping (manipulating piles). All behaviors are learned solely from interaction data, with no hard-coded physics priors.

Two More Apps: Visualizing the Zeta Process and the Motions of the Heavens

Fri, 17 Jul 2026 02:23:45 +0000

I believe that the creation of visualization apps to illustrate mathematical or scientific concepts is a particularly favorable use case for modern coding agents, as many of the downside risks attached to other LLM use cases are limited: Not mission-critical . As such apps are not authorative sources of truth and only used for secondary purposes, a small positive error rate in the output can be acceptable. Stand-alone . As the applets are not destined to be incorporated into a larger codebase or literature, the technical debt incurred by delegating all the coding to an LLM agent is bounded. End product is deterministic (and sandboxed) . As the applets run on a deterministic language (Javascript), are sandboxed against file or internet access, and do not make any LLM calls at run-time, security and privacy concerns are minimal, and the applet can be maintained without continued premium LLM access or resource-intensive compute. Not replacing primary skills . While deskilling is the tradeoff one accepts when relying on these tools to accelerate output, I am perfectly willing to forego the opportunity to keep my Javascript skills at a high level, as this is a tertiary skill for me at best in my chosen profession. (I continue to manually program in Lean and in Python to keep in practice with programming in general.) Not competing with humans . To my knowledge, there is no existing human effort that is being duplicated by these applets (the activity in this direction appears to have peaked two decades ago). I would however caution against unrestricted LLM use when one or more of the above five favorable situations is not in effect. With these points in mind, I have used such an agent to create two further apps. The first app illustrates the “zeta process” that was introduced in my recent paper with Alexeev, Barreto, Li, Lichtman, Price, Shah, and Tang , though it was first discovered by an AI . For each , the zeta distribution is a random natural number with distribution It has long been known that this distribution has good number-theoretic properties: for instance, the number of times a given prime divides has a geometric distribution of mean . However, the new observation is that these random variables can be chained together into a single stochastic process, which we call the “zeta process”, which is an infinite divisibility chain. I used an agent to create a…

Accuracy of electrostatic interactions captured by AI protein structure prediction models

Fri, 17 Jul 2026 00:00:00 +0000

The work audits AI protein structures at the level of electrostatic interactions, beyond aggregate structure scores.

An enzyme-specific protein language model for catalytic property prediction

Fri, 17 Jul 2026 00:00:00 +0000

Enzymes drive cellular metabolism, yet predicting catalytic properties from amino acid sequences remains challenging. Existing protein language models (PLMs) provide powerful general-purpose representations but are often inefficient for high-throughput screening and insufficiently adapted to enzyme-specific tasks. Here, we propose EnzGFM, an enzyme-specific PLM based on a Mamba-Transformer hybrid architecture with hierarchical pre-training to capture enzyme-specific patterns. Across enzyme property prediction benchmarks, EnzGFM consistently outperforms Transformer-based PLMs with 2–5-fold acceleration, achieving relative improvements of 16.67% in kinetic parameter prediction, 15.69% in enzyme-reaction mapping, 13.19% in EC number classification, and 20.04% in mutation effect assessment. Building on EnzGFM, we develop EnzGFM-Agent, an enzyme-focused agentic pipeline. Experimental validation further suggests that EnzGFM-Agent can enrich beneficial variants within small candidate pools. Together, these results demonstrate that EnzGFM captures enzyme-specific sequence-function patterns, while EnzGFM-Agent translates these predictions into experimentally actionable candidates and can help reduce wet-lab screening burden for practical enzyme engineering. Enzyme function prediction from amino acid sequences remains a central challenge in computational biology, despite recent advances in protein language models. This manuscript introduces EnzGFM, an enzyme-specific hybrid model that improves both accuracy and efficiency across multiple prediction tasks and, together with the EnzGFM-Agent pipeline, demonstrates the ability to identify experimentally validated beneficial variants while reducing screening effort.

AI-assisted fragment-based drug discovery validated by NMR and X-ray crystallography

Fri, 17 Jul 2026 00:00:00 +0000

AI-assisted fragment prioritization was connected to NMR and X-ray evidence rather than evaluated only in silico.

Flow Matching for Reaction Pathway Generation

Fri, 17 Jul 2026 00:00:00 +0000

Abstract Elucidating reaction mechanisms requires efficient generation of transition states (TSs) and products. Existing diffusion and sequence-based models accelerate parts of this process over traditional string-based methods, but typically still require manual enumeration of either TSs or products, and stochastic diffusion dynamics can be inefficient and hard to control. We introduce MolGEN, a conditional flow-matching framework that uses deterministic optimal transport to map Gaussian priors to chemical distributions. For TS generation, MolGEN improves TS geometry and barrier-height prediction over diffusion models while enabling sub-second sampling. For reaction product generation, it achieves competitive top- k accuracy while preserving mass and electron balance. Using the same backbone for TS and product sampling, MolGEN enables template-free generative exploration of reaction networks without the repeated quantum-chemistry searches required by prior methods. For the γ -ketohydroperoxide decomposition network, it produces more valid TSs than string-based methods using only 12 quantum-chemistry evaluations instead of 1156, and identifies a lower-barrier pathway.

Our approach to bioresilience

Thu, 16 Jul 2026 09:30:42 +0000

Our approach to bioresilience The global biosecurity landscape is rapidly evolving. Shifting natural ecosystems, global travel and the potential misuse of AI require greater vigilance — yet AI is also a critical tool for our response. We need frontier AI models, and the scientific advances they will enable, to respond to these challenges and help make society more resilient to events like future outbreaks. Today, Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience. Our work is twofold - to prevent threat actors from misusing our models, and to ensure that governments, scientists, biosecurity experts and our teams can harness these technologies to build a more resilient world. Over the past 12 months, we have advanced more than 15 partnerships with government bodies, biosecurity organizations, and research groups to prevent threat actors from misusing our models, detect new outbreaks quickly and respond quickly and effectively. Inside our bioresilience program We believe society must harness AI’s advancing capabilities to address infectious diseases and prepare for future outbreaks. Breakthroughs like Google DeepMind’s AlphaFold, which mapped the 3D structures of nearly all known proteins; Isomorphic Labs’ AI-powered Drug Design Engine (IsoDDE), which provides the real-world accuracy required to navigate novel biological systems with unprecedented precision; and AlphaGenome, which sheds light on genome function, are radically shifting the balance. Instead of just reacting to natural outbreaks or safety risks, we can now use these intelligent systems to help researchers design proactive defenses, accelerate the discovery of therapeutics, and safeguard the global health ecosystem with greater speed and precision. With this in mind, we are making our AI models and agents available to trusted partners to support progress across three key areas: prevention, detection and response. Here are some examples: Prevent: To ensure our models, like Gemini, are safe and useful to experts, we follow a four-step safety process: threat modeling, evaluations, mitigations and monitoring. We partner closely with in-house biologists, security experts and external partners to understand potential threats, test our models against them and build in robust safeguards. Additionally, we’re working on adapting our SynthID watermarking technology to biolog…

AlphaGEM integrates structural alignment and deep learning for genome-scale metabolic modeling

Thu, 16 Jul 2026 00:00:00 +0000

AlphaGEM uses structural information and deep learning to fill gaps in genome-scale metabolic models.

De novo design of orthogonal far-red, orange, and green fluorophore-binding proteins

Thu, 16 Jul 2026 00:00:00 +0000

Fluorescent proteins and small-molecule dyes offer complementary advantages for biological imaging: Proteins are amenable to genetic tagging, whereas dyes provide superior brightness and photostability. To combine these strengths, we used de novo protein design to generate small, nanomolar-affinity, high-selectivity binders (NovoTags) for three cell-permeable dyes spanning the visible spectrum. We show that the NovoTag fluorescent lifetimes can be tuned and demonstrate their application in lifetime- and wavelength-based multiplexed fluorescence imaging. We also designed a two-chain version (NovoSplit) that functions as a chemically induced dimerization system with fluorescent readout in living cells or as a minimally perturbing proximity probe in fixed cells. Our approach combines the advantages of fluorescent proteins and small-molecule dyes, thus expanding the toolkit for cellular imaging.

PeptiVerse: a unified platform for multi-property prediction of therapeutic peptides

Thu, 16 Jul 2026 00:00:00 +0000

Therapeutic peptides combine the advantages of small molecules and antibodies, offering target flexibility and low immunogenicity, yet their successful translation requires careful evaluation of multiple developability properties beyond binding alone. As chemically modified peptides become increasingly common in drug design, no unified platform currently supports systematic property assessment across both canonical sequences and SMILES-based representations. Leveraging the generalizability of large foundational models trained on protein and chemical data, we introduce PeptiVerse, a universal therapeutic peptide property prediction platform. PeptiVerse accepts either amino acid sequences or chemically modified peptide SMILES, delivers state-of-the-art performance across diverse property prediction tasks, and provides both a web interface and open-source implementation for rapid, accessible, and scalable peptide developability analysis. By unifying property prediction across representations, PeptiVerse directly supports early-stage peptide therapeutic development campaigns and property-aware generative design workflows. Therapeutic peptides are an increasingly important drug modality, combining the flexibility of small molecules with the specificity and low immunogenicity of antibodies, but their development requires simultaneous optimization of multiple physicochemical and pharmacological properties. Here, the authors present PeptiVerse, a unified platform that leverages large foundation models to predict diverse peptide developability properties from both amino acid sequences and SMILES representations, enabling accessible, scalable analysis for peptide drug design.

AI for Science conference talk recordings now available

Wed, 15 Jul 2026 14:33:33 +0000

On 31 March, the Alan Turing Institute hosted the AI for Science conference at the Royal Society, exploring how AI is changing the nature of scientific discovery. Recordings of the talks are now available on YouTube. The lineup includes Anna Scaife on foundation models for data-intensive astronomy, Aron Walsh on materials on demand, Scott Hosking on AI for climate and national resilience, Lorenzo Zanisi on putting the Sun in a bottle with AI, and Miles Cranmer on the extrapolation problem.

Conjecture Machines: AI agents and the new validation bottleneck in science

Wed, 15 Jul 2026 13:36:28 +0000

Conjecture Machines: AI agents and the new validation bottleneck in science Over the past year, agents have transformed what it means to code. Scientific research may be next — from proposing novel hypotheses, to designing experiments, to discovering algorithms that improve on the best that humans have designed. This raises urgent questions for policymakers and science funders, the biggest being how to validate the coming wave of AI-generated ideas. New approaches are also needed to ensure that scientists can access agents, that datasets are agent-ready, and that peer review is kept afloat. We sat down with 10 researchers and engineers from Google DeepMind to try to figure out what comes next. Microbiologist José Penadés and his team at Imperial College London took most of a decade to work out how a family of superbugs spreads antibiotic resistance. The result was unpublished, known only inside his lab. Then, in 2024, he described the problem to Co-Scientist, an AI agent from Google DeepMind. Within two days, Co-Scientist returned five potential explanations, ranked in priority. Number 1 was the same hypothesis Penadés’s team had spent so long working to prove: that some superbugs acquire tails from viruses and use them as “keys” to jump between host species. Stunned, his first thought was that his computer had been compromised, so he emailed Google to check. Confirmed: no peeks taken. If he could have gone back in time, that insight would have saved his team years. An AI agent such as Co-Scientist is a large language model (LLM)- based system given a goal and the tools to pursue it. Unlike a query-answering chatbot, an agent can plan how to achieve the goal you give it, breaking it down into steps, running multiple subagents and processes in parallel, and detecting and correcting errors as it goes. It can also engage with the wider world, for example by calling online databases and tools, or writing code to operate robots. The arrival of AI agents is timely. Researchers face a rapidly growing “burden of knowledge”, with more to learn before they can meaningfully contribute. The questions that count in drug discovery, climate modelling, materials design, and biology are becoming too complex and interdisciplinary for human teams to tackle at pace. Science depends in part on a social infrastructure that has gradually evolved: labs, teams, institutions, peer r…

On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning

Wed, 15 Jul 2026 00:00:00 +0000

SemiEpi connects a physics model, in-situ sensing and immediate feedback to control molecular-beam epitaxy.

Visualizing the Gilbreath Expectation Sequence

Tue, 14 Jul 2026 17:16:14 +0000

Terry Tao demonstrates how coding agents facilitate converting code-generated figures into interactive applets, using Figure 1 from his recent paper on the Gilbreath conjecture with Chase and Hunter. The figure plots exact and Monte Carlo simulated values of a sequence called the "Gilbreath expectation sequence," which arises from a Gilbreath array: an inverted pyramid with top entries as independent exponential random variables and subsequent entries as absolute differences of the two above. The sequence represents expectations of left diagonal entries. The paper provides theoretical values for some cases, with simulations agreeing well. Michael Ross later extended theoretical values, maintaining the fit. The asymptotic behavior remains mysterious; the sequence is non-monotonic and not proven bounded. The paper establishes an inequality showing the sequence cannot decay faster than a certain rate.

Building Shared Foundations for Machine-Learned Weather Prediction

Tue, 14 Jul 2026 10:00:00 +0000

Machine learning (ML) is rapidly moving from the margins of research into the centre of weather forecasting. Only a few years ago, ML models were largely experimental tools, tested alongside traditional numerical weather prediction (NWP) systems. Today, ML is becoming an increasingly important component of operational forecasting systems, complementing established numerical weather prediction. The potential is significant. Machine learning offers the possibility of producing forecasts faster, timelier and potentially at lower computational cost. It opens the door to new capabilities, such as rapidly generating large, reliable ensembles or learning patterns directly from vast volumes of diverse data. Yet moving from promising research to reliable operational forecasting systems is a far more complex challenge than developing a single ML model. It requires the development of new data-driven methodologies in weather forecasting, including datasets, infrastructure and workflows, presenting European national weather services with a transformative challenge that cannot be tackled alone. This is where the ECMWF Machine Learning Project (MLP) comes in. An international collaboration The ECMWF Machine Learning Project (MLP) is a coordinated international collaboration, led by MET Norway and MeteoSwiss, to develop and integrate ML models into the full operational forecasting system. Launched in 2024 under the umbrella of the EUMETNET E-AI programme, the project is a joint effort between ECMWF and 17 national meteorological services. To achieve the integration of ML weather prediction into the operational environment of each participating national meteorological service, the project is not centred on a single model or algorithm. Instead, it focuses on the joint exploration of the foundations needed for regional to global operational ML forecasting. Teams across Europe and beyond are contributing expertise in areas ranging from dataset preparation to model training, ensemble and initial state generation, and operational deployment. The goal is to understand how ML can function within the full forecasting chain, delivering forecasts to users. Rather than each organisation developing these capabilities independently, the MLP brings together expertise from across Europe and beyond, allowing participating meteorological services to share knowledge, establish common standar…

Protein Design’s AI Revolution: Inside David Baker’s “Communal Brain”

Tue, 14 Jul 2026 07:00:00 +0000

“I have this idea of a communal brain.” David Baker, PhD, told me as I sat in his office at the University of Washington (UW) surrounded by colorful and complex figurines of protein structures. It was the one-year anniversary of his Nobel Prize in Chemistry win. Just outside his doors, a lab of more than 100 researchers was united by the shared ambition to design proteins from scratch (or de novo) for powerful applications across pharmaceuticals, vaccines, biosensors, and more. This “communal brain” housed at the UW Institute for Protein Design (IPD), where Baker led as director, was hard at work developing deep learning methods that could achieve atomic precision. A small protein composed of 100 amino acids had an astronomical 20¹⁰⁰ possible sequences. Yet, only a vanishingly tiny fraction could fold into stable, functional structures. Misplacing a residue by an angstrom could mean the difference between a drug binding tightly to its target or complete failure. For the antibody drug market worth hundreds of billions of dollars, Nathaniel Bennett, PhD, former postdoctoral researcher in the Baker lab, says AI-guided antibody design that bypasses the need for time-consuming experimental screens has long been a “holy grail” for a breadth of indications, including cancer and autoimmune disease. Last November, Bennett and colleagues published a Nature paper demonstrating that full length de novo antibodies could bind user-specified epitopes. AI models could now construct antibody loops, the key region involved in binding that has been historically challenging to design due to its flexible nature. Despite this technological leap, AI-designed proteins that were manufacturable, remained stable in the body, and avoided unwanted side effects, were still a step away. The gap fueled an industry debate over whether generating de novo medicines was even possible. When I asked Baker to separate the hype from reality, he didn’t hesitate. “The reality is that we can now design proteins on a computer,” Baker explained in our video interview. “The hype is that for therapeutics, there’s a lot more than the basic activity of a protein binding or catalyzing a reaction. Whether de novo proteins will revolutionize medicine will require improving our understanding of the biology.” Nobel guests Bennett is continuing molecular design research as a co-founder at Xaira Therapeutics. Th…

A Paper Diagram Visualizer

Tue, 14 Jul 2026 05:34:36 +0000

I am finding the newly revealed capability to code old applet ideas into reality to be very tempting to sink more time into, though I am certainly encountering the common “vibe coding” experience that the process can produce something that superficially resembles a finished product well before a satisfactory level of testing and review has been completed; indeed, it is the review process which is now the most time-consuming, to the point where I think any further advances in coding agent capability will have little impact on the new bottlenecks in the design process. In any event, I spent a few hours working to realize a proposal I had made back in 2023 to automatically create diagrams to visually illustrate the logical flow of a given mathematical paper. At the time, Freddie Manners, extrapolating from the half-decent capability of the then-newly released ChatGPT 3.5 at this task, presciently predicted that “by the time a dedicated tool had been completed, the next general purpose engine would be better than it”. With that in mind, I decided to focus not on the generation of the diagram – which now can be done at various levels of quality by any number of large language models – but on its presentation. The result is the following app , which can take a certain formatted JSON file of dependencies between theorem objects and produce an interactive graph which can be explored, edited and also exported (somewhat lossily) into other standard formats such as SVG, TikZ, quiver, or Mermaid. Here is a screenshot of a diagramming of the celebrated proof by Wang and Zahl of the three-dimensional Kakeya conjecture: Using an LLM, I generated diagrams for eight papers for demonstration purposes, including for instance a diagram for Wiles’s proof of Fermat’s last theorem , or of Szemeredi’s proof of his famous theorem on arithmetic progressions (which sports a notoriously convoluted such diagram in the original paper ), as well as a few papers of my own . If there are other requests to diagram particular papers, I can try to use an LLM to generate more examples; but my intention is for users of the app to create their own such diagrams, either by manually constructing them, or by directing their own AI tools to build the diagram in the required format (which is a JSON, with the precise specification given here ). I mentioned in the previous post that for these sorts of…

Generative Model for Information Metamaterial Design

Tue, 14 Jul 2026 00:00:00 +0000

A generative system performs inverse design for information metamaterials, linking learned representations to physical objects.

Many AI analysts, one dataset

Tue, 14 Jul 2026 00:00:00 +0000

Across three datasets, compliant AI analyses still produced widely dispersed conclusions sensitive to model and persona choices.

UniFFBench: Experimental Benchmarking of Universal Machine-Learning Force Fields

Tue, 14 Jul 2026 00:00:00 +0000

Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evaluation has been limited to computational benchmarks that may not reflect real-world performance. Here we introduce UniFFBench, a comprehensive evaluation framework featuring the MinX dataset-a diverse collection of 1,500+ mineral systems spanning 85 elements, extreme thermodynamic conditions (0-5,000 K, 0-1,000 GPa) and structural complexity, including partial occupancy and disorder. This diversity, combined with experimental reference values for validation, enables assessment of UMLFF generalization across chemical space and conditions substantially beyond typical training scenarios. Our systematic evaluation of six state-of-the-art UMLFFs reveals a substantial 'reality gap': models achieving impressive performance on computational benchmarks often fail when confronted with experimental complexity. Even the best-performing models exhibit higher density prediction error than the threshold required for practical applications. We observe disconnects between simulation stability and mechanical property accuracy, with prediction errors correlating with training data representation rather than the modeling method.

Verifying Rust cryptography in SymCrypt, from standards to code

Mon, 13 Jul 2026 16:00:00 +0000

How Rust, Lean, Aeneas, and AI agents are helping scale formal verification for production cryptographic algorithms At a glance SymCrypt develops new verified cryptography using Rust, Aeneas, and Lean to provide higher security assurance. We prove that their code safely and correctly implements standard algorithms, notably for post-quantum cryptography. We are releasing verified code, specs, properties, and proofs initially for SHA-3 and ML-KEM. Aeneas allows verifying a large subset of Rust code and provides efficient automation in Lean to support the proof effort. Agents allow scaling automation by writing proofs that are independently-verifiable. Introduction and motivation for formal verification Cryptographic code sits at the foundation of modern computing. It protects operating systems, cloud services, firmware, messaging systems, and the protocols that connect them. Small mistakes can have outsized consequences: a single arithmetic slip, missing bounds check, or incorrect state transition can undermine the security of an otherwise sound design. Testing and auditing remain essential, but they are not enough on their own. Cryptographic implementations are often optimized, constant-time, architecture-specific, and deliberately low level. The code that ships rarely looks like the clean algorithm in a standard: it contains reductions, bit manipulations, SIMD intrinsics, carefully shaped loops, and portability layers for many environments. Formal verification addresses this gap by deploying machine-checked proofs instead of relying on testing alone. Rather than merely checking that the code usually behaves correctly, verification implements a precise mathematical specification for all inputs that satisfy the stated preconditions. In June last year, Microsoft announced we would formally verify new algorithms written in Rust in SymCrypt , the cryptographic provider used across products and services including Windows and Azure. New cryptographic implementations are being written in safe Rust, then verified in the Lean (opens in new tab) formal proof framework using the Aeneas (opens in new tab) toolchain. This applies in particular to post-quantum cryptography, which require fast secure implementations of complex algorithms. This combination gives us two layers of assurance: Rust rules out broad classes of memory-safety bugs, while Lean proofs establish funct…

The secret to human 'brilliance' that AI just can't match

Mon, 13 Jul 2026 14:56:19 +0000

Stanford GSB professor Douglas Guilbeault argues that human decision-making often relies on 'satisficing'—gathering just enough information to make a satisfactory prediction, rather than seeking optimal solutions. Experimental games show this strategy also applies to learning social rules, offering new insights into social learning and highlighting a key difference between humans and large language models (LLMs). LLMs assume that problems are solved by absorbing vast data and performing complex probability calculations, but humans have limited neurons and often lack sufficient data, so they use satisficing in uncertain situations. This finding underscores the subtle interaction between mind, behavior, and social context that AI may not replicate.

Understanding protein language model scaling for fitness prediction

Mon, 13 Jul 2026 00:00:00 +0000

Protein language models, as well as models that incorporate structural information or homologous sequences, estimate the sequence likelihood p(sequence), which reflects the protein fitness landscape and is commonly used in mutation effect prediction and protein design. It is widely believed in deep learning field that larger models perform better across tasks. However, for fitness prediction, language model performance declines beyond a certain size, raising concerns about their scalability. Here we showed that model size, training dataset and stochastic elements can bias the predicted p(sequence) away from real fitness. Model performance on fitness prediction depends on how well p(sequence) matches evolutionary patterns in homologs, which is best achieved at a moderate p(sequence) level for most proteins. At extreme predicted wild-type sequence likelihoods, models predict uniformly low or high likelihoods for nearly all mutations, failing to reflect the real fitness landscape. Notably, larger models tend to predict proteins with higher p(sequence), which may exceed the moderate range and thus reduce performance. Our findings clarify the scaling behavior of protein models on fitness prediction and provide practical guidelines for their application and future development.

Large language models do not replace chemists in a closed-loop catalysis experiment

Mon, 13 Jul 2026 00:00:00 +0000

In a 25-dimensional, 528-run closed-loop experiment, expert chemists and LLMs were compared directly, producing a useful mixed result for AI-scientist claims.

Medicine is Moving From Calendars to Clocks

Sun, 12 Jul 2026 17:25:03 +0000

This week Tony Wyss-Coray and I published an invited review paper in Nature Medicine entitled “Biological Aging Clocks in Health and Disease.” The concepts and content of the paper may be difficult to understand for people not working in life science, but they are groundbreaking and have exciting implications for the future of medicine. So this edition of Ground Truths is intended to make all of the progress comprehensible for you and to hopefully see the coming inflection point in medicine. I’ll divide this into 4 key sections: (1) the chronological illusion of aging; (2) aging is synchronous in our body—the value of organ clocks, (3) cell clocks and the master regulators of aging; and (4) impact for routine medical practice. Share The Chronological Illusion: Aging Isn’t Linear It is well recognized that each of us age at a different pace, exemplified by person aged 90 who has never been sick and physically appears to be decades younger (what we’ve called the Wellderly or Super Agers ). But the overall assumption has been that all of us undergo a gradual, steady, linear process of aging, predictable wear and tear throughout lifespan, as assessed via the calendar. That “chronological illusion” (the dotted line below) has now been debunked. Multiple studies that used large-scale proteomics (proteins or other omics/biological metrics and imaging) and have found 3 major waves of aging that occur around ages 34, 60 and 78 years, as shown below. Simplified redraw of Figure 1 from the review which cited 7 studies, made with ChatGPT Subscribe now Aging Is Asynchronous in Our Body It also turns out our organs age at a different pace. We might not have known that without the ability to do high-throughput proteomics with AI analytics. By that I mean being able to assess up to 11,000 proteins from a tube of blood and use AI to define which proteins are tracking which organ and the immune system. Perhaps the best way to prove this asynchronous process within our body is from the world’s oldest longitudinally followed birth cohort— the UK’s National Survey of Health and Development (NSHD). All of the 1,800 participants were born in the same week in 1946. At age 63, proteomics were assessed for 6 organs and the immune system. In the Figure below, you can see there is about a 10-year spread of organ and immune system pace of aging among the cohort. Conventional, top left,…

Old and new apps, via modern coding agents

Sat, 11 Jul 2026 22:48:30 +0000

I have been interested in machine-assisted ways to do and teach mathematics from as far back as 1999, when I started coding several applets in Java 1.0 , both for my complex analysis and linear algebra courses, as well as to visualize various mathematical objects I was interested in (such as honeycombs or Besicovitch sets). This was moderately successful; but the applets were time-consuming to program. Eventually, the standards for web pages stopped supporting this version of Java, and the applets became non-functional. However, in the last few days I have begun the process of migrating much of my old web page and blog data to a more maintainable repository , using modern AI assistance. As an experiment, I asked the agent to port my old applets to a modern supported language (we landed on Javascript), and it managed to do so in a matter of hours, with all of my old applets now functional again , with even a few graphical upgrades (for instance, the Besicovitch set applet is now colorized, in contrast to my original monochrome version). I am particularly pleased to see the honeycomb applet that I wrote with Allen Knutson in 1999 come back to life , as this was a particularly tricky one to code by hand: Notoriously, LLM-based coding agents can create various blatant or subtle bugs in their code; but in the porting of these two dozen or so applets, I could only find one minor bug (the handling of a drag event in one of the complex analysis applets had unwanted behavior when dragging outside of the main box), and in fact the agent identified two bugs in the original code that I was not aware of, so it ended up being a net wash as far as code quality was concerned. In any event, as these applets are meant to be secondary visual aids rather than critical components of a mathematical argument, the downside risk of such bugs is relatively low. The process was painless enough that I decided to also try coding some new apps, in addition to porting the old ones. Back in 1999 I had an ambitious idea for a visualization tool for special relativity; this was before the release of the software tool Inkscape, but the idea I had in mind was basically “Inkscape, but in Minkowski space”. I had even started writing Java code for this app , but the code complexity became too much for me, and I abandoned the project. However, after a couple hours of “vibe coding” with an AI agen…

Machine-learning-assisted development of a fast mechanochemical reaction

Sat, 11 Jul 2026 00:00:00 +0000

Herein, we combine Bayesian Optimization and mechanochemistry to develop a rapid, solvent-free protocol for the Johnson–Corey–Chaykovsky (JCC) cyclopropanation and epoxidation. By prioritizing efficiency and sustainability, Machine Learning quickly identified a new set of reaction conditions for this venerable reaction that diverge from established paradigms (explosive NaH/DMSO system), revealing that these transformations can proceed efficiently under air-equilibrated, mild conditions using an inexpensive and safe base (KOH). The method is broadly applicable, scalable, and tolerant to diverse functional groups and enabled the preparation of a wide variety of three-membered homo- and heterocycles. Overall, this work pioneers the synergy between Machine Learning and mechanochemistry to design industrially relevant transformations that prioritize safety and sustainability.

Is Pre-training Bitter Enough? Task Construction as the Control Surface in Continued Pretraining

Fri, 10 Jul 2026 08:18:29 +0000

This paper examines task construction as a control surface in continued pretraining. Standard pretraining fixes the self-supervised objective before training, with downstream feedback only available after the entire run, making the control loop coarse. The authors propose value-based pretraining (V-pretraining), which separates the learner from a lightweight task designer. The learner updates only via self-supervised loss on unlabeled data, while the designer learns to construct self-supervised tasks—such as target distributions in language modeling or view selection in vision—using a small set of verifiable downstream examples, thereby making the control loop more direct and tighter.

Aurora 1.5: Extending open foundation models for weather and Earth-system applications

Thu, 09 Jul 2026 16:46:22 +0000

At a glance - Aurora 1.5 is a major extension of Microsoft’s Aurora Earth System foundation model that adds 22 more weather variables relevant to energy, agriculture, transport, and climate risk, along with hourly temporal resolution and probabilistic ensemble forecasting. - Released as open source on GitHub with model checkpoints on Hugging Face, Aurora 1.5 enables researchers and developers to use, evaluate, and build on the model. - Aurora 1.5 connects open research to Microsoft Weather services, linking the model with data, infrastructure, managed access, and operational use for weather and Earth-system applications. Aurora 1.5 is a major update to the open Aurora Earth-system foundation model, adding 22 new weather variables for a broader view of atmospheric conditions, hourly forecasts, and probabilistic ensemble forecasting. Developed by Microsoft Weather as an extension of the original model from Microsoft Research AI for Science, Aurora 1.5 shows how frontier research can move into broader use: open for researchers and developers to evaluate and extend, and designed to support customers where additional data, infrastructure, and operational assurance is needed. As climate and weather-related risks continue to affect communities, infrastructure, and economies worldwide, advances in Earth-system forecasting can help improve preparedness and decision-making. What is Aurora? Aurora is a foundation model for the Earth system developed by Microsoft Research AI for Science, first introduced in 2024 and published in Nature (opens in new tab) in 2025. It showed that a single model could be adapted to medium-range weather, ocean waves, atmospheric chemistry, and emerging climate applications, including high-resolution weather forecasting through fine-tuning. Its growing use has reinforced the value of an open, collaborative model that is easier to adapt, evaluate, and put to use. This next phase of Aurora (opens in new tab) builds on that foundation by making the model openly available for the global community to adapt, extend, and build on. What is new in Aurora 1.5? Aurora 1.5 advances the broader effort to make open weather foundation models practical and scalable for organizations that rely on atmospheric and Earth-system intelligence. Alongside new variables and higher temporal resolution, Aurora 1.5 adds one of the most requested capabilities from user…

Interview with Thi Kieu Khanh Ho: Time-series anomaly detection

Thu, 09 Jul 2026 09:01:32 +0000

In the latest interview with AAAI/SIGAI Doctoral Consortium participants, Thi Kieu Khanh Ho, a PhD student at McGill University and Mila – Québec AI Institute, discusses her research on time-series anomaly detection. Her work focuses on teaching AI systems to detect unusual events in complex real-world data streams without relying on large amounts of labeled examples, which are often scarce, noisy, or contaminated. She has developed EEG-CGS, a contrastive and generative self-supervised framework that detects anomalous channels in EEG recordings for seizure detection, performing well without any labeled seizures during training. She also created TSAD-C, a graph- and diffusion-based framework for multivariate time-series anomaly detection that addresses the challenge of contaminated training data. Additionally, she has contributed two surveys, one on self-supervised learning for anomaly detection and one on graph-based time-series anomaly detection.

Aurora 1.5: Fine-Tuning a Foundation Model for Medium-Range Ensemble Weather Prediction

Thu, 09 Jul 2026 07:00:00 +0000

Aurora 1.5: Fine-Tuning a Foundation Model for Medium-Range Ensemble Weather Prediction - Jonathan Weyn , - Zekun Ni , - Amit Misra , - Will Fein , - Haiyu Dong , - Wessel P. Bruinsma , - Richard E. Turner , - Matt Corey , - Kit Thambiratnam , - Kevin White , - Kenji Takeda We present Aurora 1.5, a fine-tuned variant of the Aurora atmospheric foundation model [Bodnar et al., 2025] optimized for skillful medium-range ensemble weather prediction. Building on Aurora’s pretraining across diverse heterogeneous atmospheric data, we introduce a three-stage fine-tuning pipeline that (i) expands the single-level variable set and randomizes lead-time embeddings to enable a native one-hour temporal resolution, (ii) injects Gaussian noise into AdaptiveLayerNorm (AdaLN) modules to generate stochastic forward passes and optimizes a Continuous Ranked Probability Score (CRPS) objective in place of a deterministic loss, and (iii) performs auto-regressive fine-tuning on operational ECMWF analyses with multi-step rollouts. Aurora 1.5 ENS outperforms the ECMWF ENS operational ensemble on 88.9% of upper-air and single-level target variables in the medium range (days 1–10). Reliability diagnostics including rank histograms indicate that Aurora 1.5 ENS achieves this result with a slight over-dispersion, in contrast to the under-dispersion typical of dynamical models. Compared to Aurora, Aurora 1.5 also better predicts extreme events, reducing tropical cyclone track errors by 16% and the mean absolute error on top-5th percentile heat waves by 58%. Aurora 1.5 demonstrates that foundation model fine-is a viable, cost-effective path toward reliable probabilistic AI weather prediction.

NicheTrans: spatial-aware cross-omics translation

Thu, 09 Jul 2026 00:00:00 +0000

NicheTrans uses spatial context to translate between omics modalities and preserve tissue heterogeneity.

Autonomous biomedical research with an artificial intelligence agent

Thu, 09 Jul 2026 00:00:00 +0000

Biomni integrates tool retrieval, code execution and experimental protocols across biomedical tasks in a single agent environment.

How AI Learns to Smell with Alex Wiltschko

Wed, 08 Jul 2026 19:36:00 +0000

In this episode, Alex Wiltschko, founder and CEO of Osmo, discusses his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. He explains how graph neural networks and advanced embedding spaces allow AI to capture the multi-dimensional structure of scents, grouping them into perceptual neighborhoods, and creating a machine learning representation that predicts how molecules smell. Wiltschko describes how Osmo built the largest proprietary olfactory dataset from scratch to train a fleet of predictive models. Olfactory intelligence could eventually power applications beyond fragrance, including disease detection, emotion sensing, and consumer devices.

Flint: A visualization language for the AI era

Wed, 08 Jul 2026 16:00:00 +0000

Microsoft Research AI for Science introduces Flint, a visualization intermediate language for AI-driven chart creation. Flint enables AI agents to reliably generate expressive, visually polished charts from simple, human-editable specifications. It leverages semantic data types to guide the compiler in choosing appropriate scales, baselines, formatting, and color schemes, and automatically manages layout to adapt to data changes. A single Flint specification can compile to Vega-Lite, Apache ECharts, or Chart.js without rewriting, simplifying cross-library deployment. The open-source project includes the flint-chart library and the flint-chart-mcp server, enabling agents to create, validate, and render charts directly in chat or coding environments. This addresses the trade-off where short specs relying on defaults often produce uninspiring charts, while detailed specs are verbose and error-prone, especially for LLMs and agents.

Genome-Wide CRISPR Screen Reveals New Psoriasis Targets

Wed, 08 Jul 2026 15:02:40 +0000

Biohub researchers performed what they believe is the first genome-wide CRISPR study of primary human adult skin cells, then used an AI model to mine the results for overlooked drug targets for psoriasis. They found an unlikely candidate: the receptor for oxytocin, a hormone involved in childbirth, and sometimes called the “love hormone” for its additional role in social bonding. As reported in Nature Communications, when the team formulated topical gels containing compounds that would inhibit the oxytocin receptor, as well as another promising target, both gels reduced psoriasis inflammation in mice as effectively as an injected drug used by many patients. “A CRISPR screen at this scale, guided by AI, gave us answers the field has never had access to before, as well as a surprisingly effective potential treatment,” said Shana O. Kelley, president of bioengineering and head of Biohub in Chicago, where scientists are decoding the inflammatory processes that drive a wide range of diseases. Kelley co-led the study with Abdalla M. Abdrabou, lead scientist for Biohub’s Functional Immunogenomics group. “We think of this as a blueprint,” added Abdrabou. “The specific targets we found in psoriasis are exciting, but the method itself is what we are most proud of. This is a generalizable way to find treatments that those of us studying disease wouldn’t even know to look for.” Psoriasis affects roughly 125 million people worldwide. For mild cases, topical therapies, such as steroid creams, can often manage symptoms effectively. But for the many patients with moderate-to-severe disease, systemic therapies become necessary — and all come with trade-offs. “Biologics” that target immune signaling proteins must be injected, can cost tens of thousands of dollars per year, and carry risks from broad immune suppression. Recently approved therapies in pill form may reduce the burden of injections, but the drugs still circulate throughout the body, affecting immune pathways beyond the skin. What has been missing is an approach that acts locally in the skin cells where psoriasis begins, targeting disease pathways specific enough to not broadly perturb immune function. Getting there requires understanding the biology of keratinocytes — the outermost skin cells that are the primary site of psoriatic disease — at a level of detail the field has lacked, because these cells have been…

Universal cell embedding provides a foundation model for cell biology

Wed, 08 Jul 2026 00:00:00 +0000

A cross-species foundation model creates a shared cell representation that supports zero-shot transfer across biological datasets.

Managing Autonomous Materials Labs with Multi-Agent AI

Wed, 08 Jul 2026 00:00:00 +0000

Abstract Self-driving lab systems (aka, autonomous experimentation) accelerate research - letting scientists learn faster, spend less resources, and fail smarter in well defined, narrow studies. The next-generation materials lab combines self-driving systems to tackle broader challenges - orchestrating complex research campaigns while optimizing lab resources. We propose that agent-based and agentic artificial intelligence will be an integral part of next-generation lab management and discuss potential implementation scenarios. Additionally, digital and physical sandboxes will allow scientists to evaluate diverse and dynamic research and lab management strategies. Beyond the immediate benefit to lab optimization, such sandboxes will enable realistic computational studies of the philosophy of science (i.e., science of science) to achieve higher level scientific efficiencies.

NEP89: A Universal Neural Potential Across 89 Elements

Wed, 08 Jul 2026 00:00:00 +0000

NEP89 unifies inorganic and organic systems while targeting simulation speeds orders of magnitude beyond first-principles methods.

How to design a cancer vaccine (and vastly improve them): Alex Rubinsteyn & Ben Vincent

Mon, 06 Jul 2026 05:05:21 +0000

Introduction Timestamps Transcript Apple Podcasts Spotify Introduction Back in early May, I flew to North Carolina to interview Alex Rubinsteyn (left) and Benjamin Vincent (right). Together, they run the Personalized Immunotherapy Research Lab (PIRL) at UNC-Chapel Hill. Alex and Benjamin are both extraordinary people, demonstrating a degree of translational ambition, scientific insight, clarity of thought, and just genuine kindness that one rarely sees. Each time I stumble across a person like this, I try my best to get them in front of a camera. This is the first interview I’ve ever done where I traveled specifically for a guest(s), and I’m very, very glad I did it. We’re in an interesting moment with cancer vaccines. Between Sid Sijbrandij’s ‘founder mode’ journey on his cancer (which involved cancer vaccines), and the dog cancer vaccine story , a lot of eyes are being directed here. As luck would have it, Alex and Benjamin have spent a fairly high fraction of their career in this exact field, and have contributed to some of the most foundational research in it: MHCflurry 2.0, OpenVax , LENS , and plenty of other work. In fact, Alex actually helped run a neoantigen cancer vaccine trial a few years back! So obviously, they’d make for great conversation here. We talked for three hours about this area, eventually stretching beyond cancer vaccines and discussing the grander field of personalized immunotherapy. This is comfortably the longest episode I’ve ever recorded, and I suspect we could have gone for an hour longer. Finally: this is a complicated subject, and we really get into the details. Because of it, it may be worth skimming a companion cancer vaccine essay I released a month back. Many subjects from the article are discussed + expanded upon in the podcast! Timestamps: [00:00:26] Introduction [00:01:26] What cancer vaccines are, and the “easy living drug” dream [00:04:34] TAAs vs neoantigens — and why the field switched [00:08:18] MHC, HLA, and neoantigens, defined [00:12:57] The 100,000 MHCs per cell and the information-theoretic problem [00:15:28] Why a cancer can’t just drop its MHC [00:20:18] Immunopeptidomics: why predicting antigens isn’t knowing them [00:28:57] How many targets you actually need, and the HLA-loss problem [00:30:22] Can a tumor mutate its own MHC? [00:31:34] What are tumor-associated antigens and cancer-testis antigens?[00:34:…

Plausible but Not Valid: A Psychometric Audit of LLMs as Synthetic Survey Respondents

Mon, 06 Jul 2026 00:00:00 +0000

Large language models (LLMs) are increasingly used as synthetic survey respondents, but their individual answers may look plausible while failing to preserve the joint distribution, latent structure, reliability, mediation pathways, and demographic effects of real human data. This study introduces a Lithuanian organizational-psychology dataset (n=263 employees; 68 items, 12 subscales) and evaluates 37 LLMs from OpenAI, Anthropic, Google, and twelve open-weight families using a Psychometric Similarity Score (PSS) anchored against five non-LLM baselines and a human-human ceiling. Results show LLMs reproduce qualitative directions, but a Gaussian-copula baseline outperforms all LLMs; LLMs are more similar to each other (mean inter-LLM PSS 0.73) than to humans, and memorization does not drive performance. Counterfactual swaps reveal education effects (|d|=0.56) dominate gender (0.12) and role (0.18). All LLMs show strong acquiescence (+0.84 SD), synthetic-trained regressors lose validity on held-out humans (mean R² -0.18 vs 0.28), and models fabricate indirect effects on 3/10 placebo mediation paths. LLM samples are not a drop-in replacement for human survey data.

Croonian Medal Prize Lecture: Advances in computational protein design

Fri, 03 Jul 2026 09:43:51 +0000

Croonian Prize Lecture: Advances in computational protein design Join us for the Croonian Prize Lecture delivered by Dr David Baker. The Croonian Medal and Lecture 2026 is awarded to Dr David Baker for pioneering the design of completely new proteins as well as playing key roles in the development of protein structure prediction and protein engineering tools. Proteins mediate the critical processes of life and beautifully solve the challenges faced during the evolution of modern organisms. The focus of Dr David Baker's lab is the design of a new generation of proteins that address current-day problems not faced during evolution. In contrast to traditional protein engineering efforts which modify naturally occurring proteins, they design new proteins from scratch to optimally solve these problems. Dr Baker's lab develop and use deep learning methods to design these new proteins with new functions, produce synthetic genes encoding the designs, and characterize them experimentally. In this talk, he will describe the design of proteins to address current challenges in health, technology, and sustainability. Dr David Baker is a Nobel laureate, Professor of Biochemistry, Howard Hughes Medical Institute (HHMI) Investigator, and Director of the Institute for Protein Design at the University of Washington. He is also an adjunct professor of genome sciences, bioengineering, chemical engineering, computer science, and physics at the University of Washington. His lab develops software for protein design and uses it to create molecules that help solve challenges in medicine, technology, and sustainability. Recent work includes the development of machine learning methods for generating functional proteins. David Baker received his PhD in biochemistry at the University of California, Berkeley, and conducted postdoctoral research in biophysics at UCSF. He has published more than 650 scientific papers, been awarded over 100 patents, and co-founded 21 biotechnology companies. More than 100 of his trainees have gone on to independent faculty positions. He is the recipient of numerous awards, including the 2021 Breakthrough Prize and the 2024 Nobel Prize in Chemistry “for computational protein design”. A member of the US National Academy of Sciences and the American Academy of Arts & Sciences, Dr Baker was included in TIME’s inaugural list of the 100 Most Influential People in…

SkillOpt: Agent skills as trainable parameters

Tue, 30 Jun 2026 16:50:02 +0000

SkillOpt treats an agent's skill file as a trainable parameter outside a frozen target model, converting skill writing from one-shot prompting into a controlled optimization process. Across six benchmarks, seven target models, and three execution modes, SkillOpt achieves best or tied-best performance in all 52 evaluation cells, improving performance without updating model weights. The method keeps skills compact and auditable through bounded text edits, validation gating, rejected-edit feedback, and slow/meta updates, preventing uncontrolled prompt drift. Optimized skills transfer across model scales, agent harnesses, and related tasks, suggesting they capture reusable workflow knowledge rather than benchmark-specific instructions. This addresses the problem of manual skill editing that lacks guarantees of improvement.

From Frog Calls to Global Conservation: Scaling Citizen Science with AI

Tue, 30 Jun 2026 10:15:41 +0000

Image credit: Mantis Design / Adobe Stock The Growing Challenge of Biodiversity Data Citizen science projects are generating more data than ever before. Global biodiversity databases now contain millions of species observations , creating an incredible resource for ecological research. While collecting the data is not a problem, researchers still need to analyze the sounds hidden within thousands or even millions of recordings. Many contain background noise. Others capture multiple species calling at the same time. Still others need a trained ear to hear the difference between two similar-sound species. Sorting through all of that information manually can take a huge amount of time. This is a challenge Julia Tan is trying to tackle. In a previous collaboration with ESP, Julia evaluated our flagship model, NatureLM-audio, for use in processing and analyzing hundreds of thousands of frog recordings, and found strong performance and potential time-savings, even without fine-tuning. In a paper presented at the AI for Non-Human Communication workshop at NeurIPS 2025, Julia shows how an AI pipeline can help researchers extract useful information from noisy citizen science recordings, supporting biodiversity monitoring at scale. What motivated you to develop this pipeline for FrogID, and what problem were you trying to solve? The core motivation behind developing this pipeline for FrogID was the growing bottleneck created by manual validation. Citizen science initiatives like FrogID produce a large volume of valuable acoustic data, but expert review remains time-intensive, which limits the speed and effectiveness of species monitoring and delays timely management action. At the same time, while there is now more ecological data available than ever before, much of it is noisy, weakly labelled, or only coarsely annotated, making it difficult to use in conventional machine learning pipelines. We were also motivated by the rapid progress taking place in other areas of AI, particularly in fields such as human speech and bird call recognition. However, many other taxa, including frogs, have not been represented to the same extent in these advances. This raised an important question for us: how can we take the scale of citizen science data and combine it with progress in adjacent AI domains to support a more specialised ecological application? More broadly, we wanted to…

Temporal Multi-Signal Fusion for Token-Level Hallucination Detection

Tue, 30 Jun 2026 00:00:00 +0000

Token-level hallucination detectors that score each token independently often fail when the model is confidently wrong. This paper treats hallucination as a temporally extended span and frames detection as sequence labeling. Each token is scored from a 33-dimensional feature stream that fuses text statistics, Natural Language Inference (NLI) entailment, and language model surprisal, with no access to model internals. A Bidirectional Gated Recurrent Unit (BiGRU) achieves an AUC of 0.840 on RAGTruth (10 seeds), an 11-point gain over an independent logistic-regression baseline (p = 0.002, Wilcoxon signed-rank). A controlled decomposition attributes most of the gain to temporal order rather than model capacity: evidence propagates from confident positions to ambiguous neighbors. The same 0.845 ceiling recurs across recurrent, state-space (Mamba), and attention architectures, identifying the feature set as the bottleneck. Since it reads only generated text and external signals, the detector works on closed-source models and maintains performance on text from unseen language models, with under 4% AUC loss.

DiScoFormer: One transformer for density and score, across distributions

Mon, 29 Jun 2026 08:00:00 +0000

Many problems in machine learning and the sciences come down to the same task: you have a collection of data points and want to recover the distribution they came from—which values are common, and which are rare. Pinning down that distribution means estimating two quantities: the distribution’s density and, more useful as dimensionality grows, its score. The density is the smooth version of a histogram—high where points cluster and low where they’re scarce. The score – the gradient of the log-density – points in the direction the density rises fastest: move a point along the score and it heads toward a more probable region. Diffusion-based generative models (the technology behind AI image generators like Stable Diffusion and DALL-E) start from random noise and repeatedly follow the score, turning that noise into a realistic image. The same score drives Bayesian sampling and the particle simulations used to model systems such as plasma. Extracting the density and score from a finite sample is challenging, and today's tools force a trade-off between generalizability and accuracy. One classical approach, kernel density estimation (KDE), computes the density at any location from the data points around it: the closer and more numerous they are, the higher the density. It needs no training and applies to any distribution, but its accuracy falls off sharply as dimensionality grows. Alternatively, neural score-matching models trained to predict the score stay accurate even in high dimensions, but each needs to learn the distribution and must be retrained from scratch for another. We introduce a new solution called the DiScoFormer (Density and Score Transformer)—one model that, given a set of data points, estimates both the density and the score of the distribution in a single forward pass without retraining. Training a transformer for density and score estimation DiScoFormer maps an entire sample to the density and score of the distribution behind it using stacked layers of transformer blocks. The model utilizes cross-attention, which allows it to evaluate density and score at any point—not just where you have data. Score and density share a mathematical relationship: score is the gradient of the logarithm of density. We leverage this by having a shared backbone with two output heads, one for the density and one for the score. This coupling does more than save para…

An electronic nose that detects spoiled chicken, and wolves make a spectacular comeback in Europe

Thu, 18 Jun 2026 18:00:00 +0000

First up on the podcast, wrangling wolves in Europe . After near extermination in much of the continent, wolf numbers have surged up to about 20,000 individuals. Contributing Correspondent Gretchen Vogel joins podcast host Sarah Crespi to discuss the conflicts that have risen as the wolf population grows. Next on the show, Ph.D. student Carla Bassil talks about designing an e-nose that can hone in on important food smells such as chicken that has gone bad or the presence of allergens including peanuts. Finally, in a sponsored segment from the Science /AAAS Custom Publishing Office, Erika Berg, director and senior editor of custom publishing, interviews professors Eimear Kenny and Alex Charney about how genomic medicine, artificial intelligence, and large-scale sequencing are transforming the future of patient care. This segment is sponsored by the Icahn School of Medicine at Mount Sinai . This week’s episode was produced with help from Podigy . About the Science Podcast Image credit: Lorenzo Shoubridge Learn more about your ad choices. Visit megaphone.fm/adchoices

MolmoMotion: Language-guided 3D motion forecasting

Wed, 17 Jun 2026 08:00:00 +0000

Machines have become remarkably good at perceiving motion. Given a video, modern models can track how objects and points move through a scene with exceptionally high confidence. But perception is inherently retrospective: it explains motion that has already happened. Many of the systems and applications we want to build need tolook forward instead. A robot reaching for a cup has to anticipate how the cup will move before it touches it. A video generator has to know what realistic motion comes next if it's going to produce physically plausible frames. Predicting motion is harder than observing it, but it's also far more useful in many scenarios. This idea was the motivation behind MolmoMotion, a new motion forecasting model we're releasing today. Given a video frame, 3D points marked on an object, and written instructions describing the intended action (e.g., “Move and rotate the wooden bowl with fruit on the table”), MolmoMotion predicts where those points will move over the next few seconds in 3D space—achieving substantially stronger performance than existing forecasting methods. Given an RGB observation, a set of query points on an object, and an action description, MolmoMotion predicts the object's future 3D point trajectory. These predicted trajectories can then drive downstream applications such as robotics planning and trajectory-conditioned video generation. Alongside the model, we're publishing MolmoMotion-1M, the largest collection of 3D point trajectories paired with action descriptions, drawn from 1.16M videos. We're also releasing PointMotionBench, a human-validated benchmark designed to measure object-centric 3D motion forecasting accuracy, containing 2.7K video clips. We find that motion forecasters like MolmoMotion can be useful across a range of downstream tasks, from robot planning to controllable video generation. We're releasing the model weights, the MolmoMotion-1M dataset, and our PointMotionBench benchmark openly for the community to study, improve, and customize. MolmoMotion: Under the hood MolmoMotion represents motion in a deliberate, highly efficient way: as object-attached 3D points in world space, which capture motion without the cost of rendering full video. We chose it because we needed a general motion representation with three properties: Class-agnostic: not tied to templates for human bodies, hands, rigid objects, or any ot…

LifeSciBench: evaluating AI on real life-science research tasks

Wed, 17 Jun 2026 00:00:00 +0000

LifeSciBench evaluates models on research tasks designed with life scientists rather than relying on generic knowledge questions.

Our 2025 Annual Report: A Year of Momentum for Interspecies Understanding

Tue, 16 Jun 2026 14:50:59 +0000

Our 2025 Annual Report The past few years have seen a steady drumbeat of discoveries that illustrate the complex communications and culture of other animals, many of them accelerated by significant advances in technology. We are proud to be part of this incredible progress in understanding our fellow species. Our 2025 Annual Report highlights the advances we’ve made with our partners over the past year in the burgeoning new field of Animal Language Processing . Across our three core program pillars – Research to Decode, Building the Field, and Preparing Society for Impact – we expanded the tools, partnerships, and conversations needed to advance interspecies understanding. Dive into the Full Report Research to Decode In 2025, we focused on two core areas: 1) developing foundation models for animal communication. and 2) partnering with biologists to point these tools at specific Decode projects. These projects generate new datasets and insights, which improve our general models over time. We open-sourced our flagship foundation model, NatureLM-audio , the first large audio language model designed specifically for analyzing animal sounds. We published 14 new papers , expanded collaborations with researchers studying species ranging from killer whales (with Raincoast Conservation Foundation) to zebra finches (with McGill University) to carrion crows (with Universidad de León), and saw our work recognized at leading conferences including ICLR, NeurIPS, ICASSP, and DCASE. Video: Synchronized drone footage and underwater acoustic recordings from the Raincoast Conservation Foundation . It is illegal to fly drones over marine mammals if not under permit. These video clips were taken under SARA permit XMMS-2-2022-Amendment 2 (at a minimum 30m altitude) Building the Field In 2025, Earth Species Project co-organized the AI for Non-Human Animal Communication workshop at NeurIPS in San Diego, alongside representatives from Google DeepMind, ENES Lab Saint-Etienne, Naturalis Biodiversity Center. The workshop marked the first time this topic was formally introduced at a major AI conference. Our team co-organized a challenge for BioDCASE focused on Multi-Channel Alignment , which tackles the difficult problem of resynchronizing and aligning audio from multiple recording devices. We also co-organized the LINGUAE-animals Seminar with Phillippe Schlenker, Lucie Pavaux, Institu…

Aviv Regev on AI, rich datasets and the path to a 'virtual cell'

Tue, 16 Jun 2026 07:00:00 +0000

Aviv Regev is a computational biologist who leads Genentech Research and Early Development. Previously a professor at the Massachusetts Institute of Technology and a leader at the Broad Institute of M.I.T. and Harvard University, her research has advanced single-cell genomics, systems biology and large-scale efforts to map human cells. [This interview was edited for length and clarity.] How would you describe the current state of American science? On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. We are at an exciting inflection point in the history of health care. The combination of artificial intelligence, data, large-scale lab experiments and the ability to measure human biology are driving unprecedented insights and innovation in biology, drug discovery and medicine. We now have the experimental capabilities to gather very large, high-resolution, rich datasets and build deeply informative resources to drive biomedicine forward. Those rich datasets are ideally deeply integrated with AI, which can help analyze the data and fill in the gaps in what it is possible to capture experimentally and can also guide the iterative generation of additional data to gradually get to better and better models. For example, single-cell genomics allows us to understand the molecular composition and classification of individual cells, which we can then use to build a reference such as the Human Cell Atlas. As we gather more and more data about cells—the basic unit of life—we can use that to build a “virtual cell” model of how cells behave, and understand how they might behave differently in the context of disease or therapy. At the same time, we can use AI to power every stage of drug discovery, from the early stages as I just described of understanding the biology to developing the ideal medicines to target the pathways underlying disease and assessing their safety. What needs to change in American science? Basic research into foundational scientific problems is critical for innovation, and sufficient stability is important for open-ended innovation. Scientists are less likely to take risks if the environment is unstable, and science thrives in setting…

A New Path to Preventing Cancer

Sat, 13 Jun 2026 14:03:17 +0000

In the journal Cell , a team of >80 researchers from 4 continents reported on the discovery of a 14-protein blood test which paved the way for predicting and preventing lung cancer more than 5 years before it would be diagnosed. Current efforts in cancer are largely directed to therapy and early detection, with only a very limited foray to prevention. In a recent Ground Truths , I reviewed the emerging potential for preventive cancer vaccines in people with a known pathogenic mutation (such as Lynch syndrome or BRCA). In contrast, this extraordinary new report used machine learning of high-throughput proteomics, with validation of the 14-protein signature in 8 different cohorts, and in a study of people with lung cancer who never smoked . Beyond that, there was extensive work to understand the role of air pollution (via particulate matter, PM), wild-type and mutant mice, lung organoids, single-cell biology, and adjacent healthy tissue to the tumor microenvironment. The study was covered on the front page of the NY Times (←gift link) in an easy to understand way, although it glossed over its unique features, many important details, and the implications. The infographic below that I made with the help of NotebookLM is also reductionist but conveys the main thrust of the work. In this post, I’ll take you through the findings and the ramifications that it has for preventing cancer in the future. It’s a massive amount of work, so I will not get into the abundant technical details, but instead hit the high points. Subscribe now Let’s first go back to the CANTOS trial that set this up. Background on an Old Randomized Trial Back in 2017, there was publication of CANTOS , a large randomized clinical trial that assessed an anti-inflammatory medication (canakinumab, an interleukin-1β antibody) in more than 10,000 participants with prior heart attack for reduction of subsequent cardiovascular events. While the results were not compelling for this cardiovascular indication (small benefit and a risk of fatal infections), there was an unexpected outcome of r eduction of lung cancer and fatal lung cancer during 5 year follow-up , both with a dose-response (Figure below for lung cancer incidence). At the highest dose of the drug, there was nearly an 80% reduction of fatal lung cancer. But the number needed to treat (NNT) with the antibody to prevent 1 person’s lung cancer w…

XtalPi Enters $400m Partnership for Small Molecule Therapy for GPCR Target

Thu, 11 Jun 2026 07:00:00 +0000

XtalPi has entered a strategic partnership valued at more than $400m with an international biopharmaceutical company to develop oral small-molecule therapeutics targeting a G protein-coupled receptor (GPCR). The collaboration follows a successful pilot phase, where XtalPi’s integrated quantum physics and AI algorithms achieved improved hit rates, highlighting the platform’s capability to address the complex metabolic target. Discover B2B Marketing That Performs Combine business intelligence and editorial excellence to reach engaged professionals across 36 leading media platforms. According to the agreement, the partner biopharmaceutical company will provide an upfront payment and fund the early research and development efforts of XtalPi. XtalPi also stands to receive further payments based on preclinical, clinical and commercial milestones, as well as future royalties. The company stated that the targeted GPCR presents significant challenges because of its extreme conformational plasticity, making its ligand-binding pockets difficult for small molecules to engage selectively. To address these limitations, XtalPi’s research and development team employed multiscale enhanced sampling simulations to map the receptor’s functional conformational landscape. They then used a dynamic, multi-conformational screening strategy. Applying advanced quantum physics models and AI algorithms, XtalPi conducted virtual screening of several compounds and utilised its XFEP (Free Energy Perturbation) platform for accurate binding affinity prediction. As the collaboration progresses, XtalPi will use its structure-based drug design platform, integrating quantum physics, generative AI, and large-scale automated chemical synthesis enabled by a multi-agent system. XtalPi chairman Dr Shuhao Wen said: “This collaboration underscores our deep commitment to supporting top-tier biopharmaceutical companies with robust, scalable AI and robotics capabilities. “At XtalPi, we design our platform to act as the essential infrastructure for innovation — helping our partners reliably translate complex biological challenges into strong pipeline assets. “We are excited to combine our technological strengths with our partner’s clinical vision to advance accessible, highly effective oral therapies in the metabolic space.”

HarmProfile: Characterizing Harmful Distributions in Frontier LLMs

Thu, 11 Jun 2026 00:00:00 +0000

HarmProfile is a content-centric benchmark dataset that collects model misbehavior across diverse harm categories and model families, defining the resulting harmful-output distribution as a model-level risk profile. It contains over 80,000 validated artifacts from 23 frontier LLMs across 13 model families, organized into 15 harm categories and 57 subcategories. Using this corpus, the authors find that frontier LLMs reliably produce harmful content at scale, yet exhibit distinct risk profiles; both harmfulness and diversity grow with model capability, suggesting that frontier LLMs may appear safe yet harbor increasingly dangerous knowledge beneath the alignment surface. The source code is available at GitHub.

Creating new simulations of black holes with Codex

Thu, 11 Jun 2026 00:00:00 +0000

Astrophysicist Chi-kwan Chan used Codex to derive, implement and test candidate algorithms for Event Horizon Telescope simulations.

AlphaFold v3.0.3 Release Notes

Tue, 09 Jun 2026 12:26:50 +0000

Version 3.0.3 changes the AlphaFold 3 code license from CC BY-NC-SA 4.0 to Apache 2.0. Moreover, it provides bug fixes and a performance improvement for large inputs. We thank everyone who reported issues, proposed new features, and submitted PRs! Change log Below is a selection of new features, performance improvements and bug fixes. Full change log: v3.0.2...v3.0.3 New features AlphaFold 3 is now compatible with Python 3.14. The AlphaFold 3 code is now relicensed from CC BY-NC-SA 4.0 to Apache 2.0. Performance improvements Confidences JSON serialization is up to 30x faster, for a 5,000-residue input the serialization time drops 43 -> 1.3 s. mmCIF parsing is 4% faster. Bug fixes A new flag was introduced to not drop O1 atom from non-linking saccharides/glycans. Thanks @magnusbauer and @Jonas-Wilhelm . Added support for dynamic version (inferred from the commit history) for dev and releases. Thanks @ccoulombe . Pass all flags correctly to Jackhmmer/Nhmmer. Thanks @chenbq18 . Fix glycan O1 leaving-atom cleanup for standalone glycans. Thanks @Jonas-Wilhelm and @magnusbauer . Do not drop chain descriptions for RNA and protein chains. Thanks @ntnn19 . Fixes of typing issues revealed by Pyrefly. Documentation Fixed typos in docstrings, thanks @mooreneural . We thank the following people for their pull requests and/or great suggestions: @ccoulombe , @Jonas-Wilhelm , @magnusbauer , @mooreneural , @ntnn19 .

NVIDIA/physicsnemo v2.1.1 released

Mon, 08 Jun 2026 23:37:58 +0000

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods - Release v2.1.1 · NVIDIA/physicsnemo

insitro Presents Data at ADA 86th Scientific Sessions Showing AI-Discovered MASH Candidate Has Anti-Fibrotic Signal Beyond Liver-Fat Reduction

Mon, 08 Jun 2026 19:01:11 +0000

Share - Findings from insitro’s lead therapeutic CTRO-1013 program demonstrate that liver-targeted silencing of IRS1 lowered circulatory biomarkers associated with fibrosis progression and hepatocyte injury in an industry-standard fibrogenic preclinical mouse model of MASH - Data presented today at ADA provide converging evidence from human genetics and preclinical models supporting IRS1’s effect on liver fat and liver fibrosis - The findings extend insitro’s AI-driven discovery of IRS1 – the strongest of 480 genetic signals for liver fat – and support the development of CTRO-1013 toward first-in-human trials this year NEW ORLEANS – June 8, 2026 – insitro, the physical AI company unlocking causal human biology, today presented new preclinical data from its metabolic dysfunction-associated steatohepatitis (MASH) therapeutic development program during a podium presentation at the American Diabetes Association (ADA) 86th Scientific Sessions. The data demonstrate that liver-targeted silencing of IRS1 reduced fibrosis progression and injury-associated circulatory biomarkers in an industry-standard fibrogenic preclinical mouse model, with effects that appear partly independent of reductions in liver fat. The findings support the continued advancement of CTRO-1013, the company’s liver-targeted IRS1 candidate, through IND-enabling studies toward first-in-human trials this year. The presentation, “Hepatic IRS1 as a Therapeutic Target in MASLD: AI/ML Driven Genetic Discovery and In Vivo Therapeutic Validation,” was delivered by Santhosh Satapati, Ph.D., Senior Director of Metabolic Diseases at insitro. Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common chronic liver disease marked by excess fat accumulation in the liver. MASH is the progressive form of MASLD in which fat accumulation is accompanied by inflammation, liver injury, and fibrosis. Fibrosis – the scarring that can progress to cirrhosis, liver failure, and liver cancer – is one of the strongest predictors of poor outcomes for patients with MASH. Yet finding high-confidence targets has been difficult because the most relevant liver phenotypes, fibrosis among them, are hard to measure at the scale needed for human genetic discovery. Human genetic analyses revealed an IRS1-associated fibrosis signal that persists even after adjusting for liver fat, suggesting a fibrotic mechanism inde…

Paving the way for agents in biology

Mon, 08 Jun 2026 13:20:00 +0000

Science Jun 8, 2026 Written by Laura Luebbert. Based on research by Ferdous Nasri, Sarah Gurev, Patrick Varilly, Krithik Ramesh, Nuala A. O’Leary, Jonah Cool, Bernhard Y. Renard, Pardis Sabeti, and Laura Luebbert. In this post, Laura Luebbert argues that we need to make biological data infrastructure more agent-friendly. As a case study, she and her team tasked scientific research agents (Claude, Biomni Open Source (Biomni OSS)1, Edison Analysis,2 GPT) to retrieve the sequence data from NCBI Virus, a database virologists use for tasks such as surveillance and diagnostic assay development. Even the strongest models did not consistently achieve the level of accuracy required for reliable dataset construction. But accuracy rose to nearly 100% once she and her team added gget virus, a deterministic retrieval layer. The broader lesson for scientific agents is that deterministic retrieval tools are (currently) crucial to making agent workflows more reliable, and biological databases will need to be designed with agents in mind as scaled users. Using AI agents to navigate biological data infrastructure is like driving through an old city that was designed before cars: the infrastructure may be beautiful and even thoughtful, but it’s full of narrow, winding streets that are difficult for modern vehicles to navigate (idiosyncratic file formats, scattered databases, and one-off retrieval scripts).3 You can retrofit the city with traffic signs, parking lots, and the occasional widened road, but the basic layout remains hard to navigate because it was designed for a different mode of conveyance. Software infrastructure, by contrast, was basically made for the needs of cars (agents): paved roads, clear lanes, standardized signals, and systems designed for fast travel from start to finish (version control, well-documented APIs, and package managers). As a result, coding agents have advanced much more quickly than biological agents. Software commonly provides structured digital workflows and reliable interfaces, whereas the computational biology infrastructure needed for data retrieval and validation is often brittle, heterogeneous, and process-dependent. The tools with which we navigate them are necessarily bespoke and tuned to defined domains or…

Making Claude a chemist

Fri, 05 Jun 2026 20:57:00 +0000

Science Jun 5, 2026 We’re working with world-class synthetic, computational, and analytical chemists to make Claude better at chemistry. In this post, we share our first work as part of this effort, in which Anthropic chemist, David Kamber, examines how Claude performs on a chemist’s most common analytical input, an NMR spectrum. When working with molecules, chemists move between hand-drawn structures on a whiteboard, instrument readouts, database query strings, and the technical notations of patents and publications. Each of these representations encodes the same underlying chemistry, but each demands a different kind of fluency. A sketch of caffeine, for example, allows a chemist to spot its resemblance to adenosine, the body’s drowsiness signal, and predict that it keeps us alert by blocking the receptor. However, that same sketch cannot help a chemist tell it apart from other near-identical looking molecules. Understanding what molecule a chemist is working with is critical. Chemistry undergirds everything from the foods and medicine we ingest to our lotions, paints, and plastics. Reroute a handful of bonds among the same atoms, and glucose becomes fructose, molecules sharing a formula but processed through entirely different metabolic pathways. Flip a molecule into its mirror image, and a sedative becomes a teratogen, as happened in the thalidomide disaster.1 Chemists’ everyday work depends on reading these signals correctly across whichever representation befits a given task. Translating between these representations (chasing down a structure from a figure, reconciling an instrument readout against a proposed product, querying a database in the right notation) is time consuming and impossible to keep up with at scale—CAS, the largest chemistry registry, catalogs over 290 million disclosed substances and grows by roughly 15,000 new ones every day. AI is well-positioned to take on this research burden, yet it still remains largely aspirational in the context of chemistry. Machine-learning tools have been positioned for years as transformative for retrosynthesis—the process of working backward from a target molecule to simpler precursors to plan how to build it—reaction prediction, and property estimation, but the data those tools…

CZ Biohub releases “ESM Atlas” with 1.1 billion ESMFold2-predicted protein structures, claiming to surpass AlphaFold

Thu, 28 May 2026 07:00:00 +0000

A claim has emerged that a new protein structure prediction artificial intelligence (AI) model surpasses the performance of Google DeepMind’s AlphaFold, which was developed by DeepMind and even led to the 2024 Nobel Prize in Chemistry. Experts are divided between seeing it as an important new resource for protein structure prediction and expressing doubts about its performance. According to a report in the international journal Nature on the 27th (local time), the U.S. biomedical research institute Chan Zuckerberg Biohub (CZ Biohub) released a database called “ESM Atlas,” which compiles the results of its protein structure prediction AI model “ESMFold2.” CZ Biohub is a research institute founded by physician Priscilla Chan, the wife of Meta (formerly Facebook) founder Mark Zuckerberg. According to CZ Biohub, ESMFold2 outperforms other models, including AlphaFold3, the latest protein structure prediction model from Google DeepMind. The report states that the number of protein structure data predicted by ESMFold2 exceeds that of the “AlphaFold database,” which contains AlphaFold’s predictions, by 800 million. Alex Reeves, chief scientific officer at CZ Biohub and lead author of the ESM Atlas study, said, “ESM Atlas reveals even the most unknown regions of the protein universe,” adding that “it will provide a powerful foundation for new biological discoveries.” ESMFold2 is based on a “protein language model” that the team released in 2024. A protein language model learns amino acid sequences of proteins as if they were a language. The researchers trained the model on billions of protein data obtained from living organisms. This included “metagenomes,” which are the total genomes of microorganisms living in various environments such as soil and oceans. Metagenomic data are not included in the AlphaFold database. According to the team, ESMFold2 predicts protein complex structures, such as how antibody molecules bind to their target antigens, more accurately than existing models. ESMFold2 also designed new antibodies and proteins that bind strongly to proteins involved in cancer and immune diseases. The team produced these antibodies and proteins in the laboratory and confirmed that they functioned as predicted by the model. Using ESMFold2, the researchers built an atlas containing 1.1 billion predicted protein structures and 6.8 billion protein sequences. Reeves…

OpenAI's math breakthrough played to AI's strengths

Thu, 28 May 2026 07:00:00 +0000

Last week, OpenAI announced that an internal AI model had disproved the Erdős unit distance conjecture, a famous problem in discrete geometry that had stumped human mathematicians for the last 80 years. OpenAI gave several mathematicians early access to the result and published their reactions. Tim Gowers — who won the Fields Medal, the most prestigious prize in mathematics — wrote that “there is no doubt that the solution to the unit-distance problem is a milestone in AI mathematics.” University of Toronto professor Daniel Litt wrote that “this is the first example of a result produced autonomously by an AI that I find exciting in itself, as opposed to as a leading indicator.” It’s arguably the first time that an AI system has found a proof resolving a major open conjecture. That’s impressive, but I don’t view it as a radical break from the previous trajectory of AI progress in mathematics. Three years ago, LLMs struggled to solve arithmetic problems. It was only last year that LLMs started acing high school mathematics competitions. When I attended the Joint Mathematics Meetings — the largest annual mathematics conference in the world — in January, I learned that AI systems were starting to contribute to mathematical research, but only in constrained settings. It took significant human interpretation to turn an AI output into a publishable theorem. OpenAI’s new result is the next step in this progression. The AI model cleverly applied existing ideas drawn from several subfields of mathematics to create a full proof. But it didn’t pioneer any genuinely new techniques. The result has since been cleaned up and extended by human mathematicians. This points to a medium-term future where human mathematicians and AI models complement each other: AIs have a broader knowledge of past work than any human alive and much more willingness to grind through tedious proof strategies that aren’t likely to work. But humans can still think more deeply about any one problem and ask more interesting questions. That might not last. AI systems have been improving at math so rapidly that it’s unclear what role — if any — human mathematicians will play a decade from now. The unit distance problem Paul Erdős was one of the most prolific mathematicians in history. He wrote over 1,500 papers in his lifetime, the most ever. One of his greatest talents was coming up with problems that…

Biohub Releases a World Model of Protein Biology

Wed, 27 May 2026 11:21:49 +0000

REDWOOD CITY, Calif., May 27, 2026 – Biohub today announced the release of a world model of protein biology: a scientific engine for prediction, design, and discovery that can map proteins across the tree of life, predict their structures, and design new protein binders that function in laboratory experiments. Proteins are the machinery of life. Nearly every function of the human body depends on them. They are among the most important targets in medicine, yet designing functional, stable proteins that work as intended in the body is an immense scientific and technical challenge. Today, Biohub is making available to researchers everywhere an open discovery engine for protein structure prediction, design, and biological discovery built around three releases: ESMC, ESMFold2, and ESM Atlas: - The core scientific hypothesis of ESM is that training a language model across the sequences of all life will cause it to internalize the fundamental properties that govern protein biology — the rules underlying how proteins fold, interact, and function across all of life. At its foundation is ESMC, a state-of-the-art language model that represents proteins, trained on approximately 2.8 billion sequences drawn from across all of life. - ESMFold2 is the design engine built to transform ESMC’s sequence representations into atomically-resolved 3D structure of biomolecular complexes. In experiments described in a preprint, researchers used ESMFold2 to design protein binders against five targets central to cancer and immunology — a computational search completed in days, rather than several months or years. The lab-validated binders exhibited high affinity, specificity, and stability — properties critical for clinical utility — and showed minimal similarity to sequences in public databases, suggesting the model is producing de novo solutions, rather than retrieving known binders. - ESM Atlas makes ESMC’s representations navigable across 6.8 billion protein sequences and 1.1 billion predicted structures — the largest application of AI to protein biology to date. It organizes proteins by relationships the model has learned, surfacing connections that existing databases have not captured, including evolutionary links between gene-editing enzymes spread across distant branches of life. Much of that biology has never been annotated. For researchers working on diseases where the biol…

De novo Generation for Molecular Structure Elucidation from Mass Spectrometry

Tue, 26 May 2026 07:00:00 +0000

De novo Generation for Molecular Structure Elucidation from Mass Spectrometry - Runzhong Wang | MIT - Microsoft Research New England Generative Modeling & Sampling Seminar Recent advances in generative AI are enabling new approaches to scientific discovery in chemistry and biology. In this talk, we present DiffMS and FRIGID, two generative AI frameworks for de novo molecular structure elucidation from tandem mass spectrometry (MS/MS). DiffMS introduces a formula-constrained graph diffusion model that generates molecular structures directly from experimental spectra using transformer-based spectral encoding and large-scale pretraining on fingerprint–structure pairs. Building on this foundation, FRIGID develops a scalable diffusion language model trained on hundreds of millions of molecular structures and introduces inference-time scaling through cycle-consistent refinement with forward fragmentation models such as ICEBERG, enabling targeted correction of spectrum-inconsistent molecular fragments. Together, these works demonstrate how diffusion models, large-scale pretraining, and inference-time reasoning can advance generative AI for scientific discovery and molecular identification. Speaker bio Runzhong Wang is a Postdoc working with Prof. Connor Coley at MIT. Prior to that, he received his B.S. and Ph.D. from the Department of Computer Science and Engineering, Shanghai Jiao Tong University. His research is at the intersection of machine learning, optimization, and computational metabolomics. He has published more than 30 papers on machine learning and AI for Science topics. Series: MSR New England Generative Modeling & Sampling Seminar - - - - - - - - - - Rare event analysis via stochastic optimal control - Yuanqi Du & Carles Domingo-Enrich - Constrained Generative AI for Materials Inverse Design - Mouyang Cheng - - Designing Dynamic Measure Transport for Sampling - Aimee Maurais - - Physics and information theory of generative diffusion - Luca Ambrogioni - - Matching features, not tokens: Energy-based fine-tuning of language models - Mujin Kwun, - Carles Domingo-Enrich - - - Generative Models for Molecular Dynamics Across Timescales - Michael Plainer, - Winfried Ripken, - Gregor Lied - - Q-learning with Flow-Matching Policies - Qiyang (Colin) Li - - - A non-Markovian approach to diffusion-based sampling - Lorenz Richter

ESI-Bench Catches Frontier AI Failing 3D Space: Seeing and Acting Diverge

Fri, 22 May 2026 07:00:00 +0000

A Stanford-led research team that includes Fei-Fei Li published a benchmark on May 18, 2026, that documents a specific, measurable failure in every frontier multimodal AI model tested: when forced to actively move through a 3D environment rather than interpret a pre-composed image, state-of-the-art systems consistently choose the wrong actions, skip viewpoints that would correct their mistakes, and commit to wrong answers with high confidence. The paper names this failure "action blindness." For robotics buyers, simulation engineers, and any developer whose product requires an AI agent to reason about physical space, the finding supplies a test that existing leaderboards do not. ESI-Bench Redefines What Spatial Intelligence Requires The benchmark, titled "ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop," covers 10 task categories and 29 subcategories built inside the OmniGibson simulator on top of Stanford's BEHAVIOR-1K activity dataset. Its approximately 3,000 tasks are organized around what developmental psychologist Elizabeth Spelke at Harvard has identified as core knowledge systems — the domain-specific cognitive systems human infants use to reason about objects, agents, numbers, space, and form. The conceptual shift the benchmark makes is precise and consequential. Prior spatial-intelligence benchmarks assumed oracle observations: the model was handed the right view of the scene before being asked a question. ESI-Bench removes that assumption. The agent must first decide which ability to deploy — perception, locomotion, or manipulation — and in what sequence, in order to accumulate the evidence needed to answer. As described on the project page, the agent must actively uncover what is unseen: occluded structure, dynamics, containment, and functionality that no single static image can reveal. The task set reflects that design. Agents are asked to compare the liquid-holding capacity of two containers by manipulating them, predict whether a deformable object will conform to a surface, judge whether a tower will balance under given mass and geometry distributions, and distinguish a mirror reflection from the real scene by repositioning. Each task has a correct answer that is structurally inaccessible without choosing an action first. Frontier Models See But Do Not Know When to Move Experiments on state-of-the-art mu…

Marcus on AI: Checking the math behind OpenAI's latest headline

Thu, 21 May 2026 07:00:00 +0000

OpenAI scored a big result yesterday, with respect to an Erdos problem: Clearly impressive. But as with so much else, it should be viewed with skepticism. In an email to me this morning, Cal Newport made a number of good points that he said I could share, summarizing both what was found and some limitations: OpenAI used a new reasoning model (not yet released) to help identify a counterexample that disproved a conjecture from discrete geometry first proposed by Paul Erdos 80 years ago. The model was tuned for so-called chain-of-thought reasoning where the model endlessly “thinks out loud” about whatever it is trying to solve, an approach that lets you approximate something like memory and dynamic computation using LLMs, which are otherwise static and feed-forward. Professional mathematicians identified the counterexample from within a long transcript of the model’s reasoning, and then extracted the key parts and rewrote it as a more succinct proof in a more standard style. How was the model able to solve something that human mathematicians had failed to do? In a companion article released by OpenAI, the mathematician Thomas Bloom, who reviewed the full model output, identified the factors that came together to make this counterexample ripe for LLM-aided discovery. He noted that though the conjecture is old, those who have worked on it have largely shared Erdos’s original belief that it was true and therefore focused on trying to solve it. What the LLM-based tool did instead was to systematically apply and extend existing techniques in search of evidence that the conjecture was false. Here’s Bloom: “[the AI’s] success here echoes previous achievements: it often produces the most surprising results by persevering down the paths that a human may have dismissed as not worth their time to explore, combining superhuman levels of patience with familiarity with a vast array of technical machinery.” A few observations of my own: (1) Non-mathematicians might not be familiar with the degree to which LLM-technology has been combined with existing computer-aided math tools in recent years to seek new math results through the systematic and patient exploration of techniques and corners of problem spaces that are too exhausting to interest most human mathematicians. The real technical headline of the new OpenAI result, therefore, is that chain-of-thought reasoning was abl…

Formalizing Fermat workshop

Fri, 15 May 2026 12:23:02 +0000

I’m organizing a workshop in London on July 6th to 10th (2026) whose goal is to work on my EPSRC-funded project formalizing Fermat’s Last theorem in Lean. The initial aim of the project was to reduce FLT to theorems known in the 1980s, i.e. “formalize the Wiles/Taylor–Wiles papers” (although actually we are formalizing a more modern proof whose ideas go back to a strategy proposed by Khare). I have just finished giving a series of 11 lectures on the proposed proof route as part of the EPSRC Taught Course Centre which Imperial College London is part of; pdfs of the lectures are available here https://github.com/ImperialCollegeLondon/FLT/tree/main/2026_EPSRC_TCC_course . However, with autoformalization becoming more and more powerful, the tech company Logos Research (who are funding the workshop) has proposed experimenting with getting AI to formalize the prerequisites which I need (and perhaps also main proof ideas themselves). Whilst autoformalization has got a lot better recently (see e.g. the second half of my talk at the recent Exeter workshop ) it still does not seem to be capable of reliably autonomously formalizing definitions correctly, or theorem statements idiomatically. My vision for the workshop is that a combination of human experts in number theory/lean plus experts in autoformalization could have a fun time experimenting with what actually can be achieved here. For those who think “autoformalization is solved/will shortly be solved”, you could think of this workshop as a challenge to prove this. What is happening right now is that people are choosing targets for which autoformalization will work well and then saying “look we did this impressive-looking thing”. Here we’re doing something different — we’re choosing the target first, and then asking whether autoformalization is appropriate for it. The target is huge, and the answer will surely be “autoformalization is good at this part, but not very good at that part”. Given recent advances, I think it will be a very interesting project to find out exactly which parts of the (gigantic) proof of Fermat’s Last Theorem are currently suitable for autoformalization. My guess is that complex-definition-heavy parts will be bad, and parts of the proof where all definitions are already in place in mathlib and the proofs are well-documented in the human literature could well be good. But let’s see. The dea…

Farewell to External AI Models

Mon, 11 May 2026 10:00:00 +0000

Machine learning (ML) models have become an increasingly important component of the global weather forecasting landscape. Over the past few years at ECMWF, several externally developed models have been run alongside the Integrated Forecasting System (IFS) in an experimental setting, allowing their performance to be assessed against established numerical methods. The upgrade of the IFS to Cycle 50r1 has provided a test of how robust these models are to changes in their underlying data. In this blog post, we discuss the impact of the IFS upgrade on the performance of the ML models run at ECMWF, and why this has prompted the decision to stop running external models in real time. The IFS and ML models The IFS is the global numerical weather prediction (NWP) model run operationally by ECMWF. It receives regular updates, with each upgrade incorporating new science, improving its performance. The IFS is also the backbone of the ERA5 reanalysis dataset, which is used to train ML models for global weather forecasting. ERA5 is based on IFS Cycle 41r2 – the operational cycle in 2016. ML models for global weather forecasting are generally trained on approximately 40 years of ERA5 data and, for some, fine-tuned on the latest version of the IFS. This is the case for the Artificial Intelligence Forecasting System (AIFS), ECMWF’s ML-based forecasting model that has been operational since February 2025. More precisely, AIFS v1.1 is fine-tuned on seven years of operational analysis spanning January 2016 to January 2023. A verification experiment Since 2023, ECMWF has run several external ML models in experimental mode. Forecasts from a selection of these models are routinely made available on openCharts, providing the weather community with quick and easy access to ML-based predictions. This effort predates the creation of the AIFS and has helped build a better understanding of the structure and performance of these new types of forecasts, which helped initiate the uptake of ML in global weather forecasting. At the beginning of 2026, the set of external ML models running daily at ECMWF comprised the following: Pangu-Weather (Huawei), GraphCast (Google DeepMind), Aurora (Microsoft Research), and FourCastNet (Nvidia). These models were run twice daily (at 0 and 12 UTC) and were initialised with the IFS operational analysis. Prior to the 50r1 upgrade, we conducted a verificatio…

Demis Hassabis on AlphaFold as the First Rung of AI-Driven Science

Mon, 11 May 2026 07:00:00 +0000

Demis Hassabis, CEO of Google DeepMind and 2024 Nobel laureate in Chemistry, has spent the first months of 2026 articulating a specific and measurable thesis: AlphaFold, the protein-structure model that now serves more than three million researchers across 190 countries, is the first rung in an AI-powered scientific revolution, not the last. His clearest statement of this position came in a Semafor interview published 21 January 2026, where he described the commercial boom in generative AI as potentially slowing, not accelerating, progress toward deeper scientific breakthroughs. The Paradox at the Heart of AI Progress In his January 2026 Semafor interview, Hassabis offered an assessment that sits at odds with the prevailing industry narrative. "The paradox of AI progress," he said, is that the commercial success of generative AI may actually stretch out the timeline to whatever comes after it. Shortages in high-bandwidth memory and reduced open research sharing have created friction in scaling, which he acknowledged as potentially useful: "It may be a good thing that it's not as fast. There's a whole bunch of other things that we need to think through with this technology." He noted that commercial pressure has made it harder to share research openly, describing it as "a shame on the one hand, but understandable." That framing is deliberate. Where competitors have staked out exponential-scaling positions, Hassabis is positioning DeepMind's approach around multimodal training data and reasoning rather than raw compute volume. "Text alone would not get you to the endgame faster," he told Semafor, explaining DeepMind's bet that reasoning would emerge through richer, multi-modal inputs. The implication is that DeepMind's path diverges from the brute-force scaling playbook, relying instead on architecture choices and domain-specific training. In a separate Fortune interview in February 2026, Hassabis described his longer-term ambition as building toward "radical abundance," a state in which AI has bottled the scientific method and can autonomously run the hypothesis-experiment-publication cycle. "In 10, 15 years' time, we'll be in a kind of new golden era of discovery that is a kind of new renaissance," he told Fortune editor-in-chief Alyson Shontell. AlphaFold to Isomorphic: From Protein Maps to Drug Candidates Hassabis has consistently described AlphaFold as t…

A Recent Experience with ChatGPT 5.5 Pro

Fri, 08 May 2026 15:40:04 +0000

We are all having to keep revising upwards our assessments of the mathematical capabilities of large language models. I have just made a fairly large revision as a result of ChatGPT 5.5 Pro, to which I am fortunate to have been given access, producing a piece of PhD-level research in an hour or so, with no serious mathematical input from me. The background is that, as has been widely reported, LLMs are now capable of solving research-level problems, and have managed to solve several of the Erdős problems listed on Thomas Bloom’s wonderful website . Initially it was possible to laugh this off: many of the “solutions” consisted in the LLM noticing that the problem had an answer sitting there in the literature already, or could be very easily deduced from known results. But little by little the laughter has become quieter. The message I am getting from what other mathematicians more involved in this enterprise have been saying is that LLMs have got to the point where if a problem has an easy argument that for one reason or another human mathematicians have missed (that reason sometimes, but not always, being that the problem has not received all that much attention), then there is a good chance that the LLMs will spot it. Conversely, for problems where one’s initial reaction is to be impressed that an LLM has come up with a clever argument, it often turns out on closer inspection that there are precedents for those arguments, so it is still just about possible to comfort oneself that LLMs are merely putting together existing knowledge rather than having truly original ideas. How much of a comfort that is I will not discuss here, other than to note that quite a lot of perfectly good human mathematics consists in putting together existing knowledge and proof techniques. I decided to try something a little bit different. At least in combinatorics, there are quite a lot of papers that investigate some relatively new combinatorial parameter that leads naturally to several questions. Because of the sheer number of questions one can ask, the authors of such papers will not necessarily have the time to spend a week or two thinking about each one, so there is a decent probability that at least some of them will not be all that hard. This makes such papers very valuable as sources of problems for mathematicians who are doing research for the first time and who will be h…

A survey of downstream applications of evolutionary scale modeling protein language models

Thu, 07 May 2026 07:00:00 +0000

The advent of the evolutionary scale modeling (ESM) series of protein language models (PLMs) is a significant innovation in the convergence of large language models (LLMs) with protein representation. These models, trained on large amounts of unlabeled protein sequence data, learn the intricate patterns of mutation and conservation that have sculpted protein families through evolutionary history. ESM has become a widely used foundation model family for protein representation and downstream biological tasks.

AI Co-Mathematician: Accelerating Mathematicians with Agentic AI

Thu, 07 May 2026 00:00:00 +0000

Google DeepMind presents an agentic system designed to collaborate with research mathematicians on open-ended problems.

From What to Who? AI Learns to Recognize Individual Animals

Tue, 05 May 2026 16:08:07 +0000

Image credit: Quang / Adobe Stock Individual ID in Chatty Species In the wild, zebra finches don’t sing in isolation. They move through dynamic social groups, calling and responding to one another across space and time. For ecologists, understanding this communication requires knowing who is communicating with whom , and how information flows through a group. Tracking communication at the level of individuals, however, has long been out of reach, especially in highly social, “chatty” animals that operate in large groups. In natural soundscapes, following specific birds across time requires painstaking manual annotation, and even then, it’s difficult to scale beyond small datasets or controlled settings. Recording of wild zebra finches by Hugo Loning This challenge brought together two different perspectives. Dr. Hugo Loning , an ecologist studying wild zebra finches, and Dr. Aneesh Chauhan , an AI and robotics researcher exploring bioacoustic monitoring. Together, they began exploring how AI can help identify individual birds in the wild. To do this, they applied BirdAVES , a self-supervised bioacoustic model developed by Earth Species Project. This approach aims to make it possible to recognize individual zebra finches from field recordings, turning hours of audio into “who-sang-when” timelines and enabling new questions about social behavior, movement, and information sharing. Their paper was accepted at the first AI for Non-Human Animals Workshop at NeurIPS 2025. We spoke with Hugo and Aneesh about how the collaboration came together, what it takes to study communication in the wild, and how tools like BirdAVES are helping bridge the gap between ecology and AI. ⮕ How did this collaboration between ecology and machine learning come together in the first place? [Hugo] I first met Aneesh after attending one of his talks on a collaborative project on local biodiversity monitoring with colleagues in my group. For that project, Aneesh had used classical bioacoustic monitoring methods and BirdNet to identify which species were present and to measure broad biodiversity metrics in a local organic community-run garden and in a University farm in Brazil, and was now wondering whether his results made sense. I approached Aneesh after the talk thinking I could potentially help validate his results as I am a reasonably knowledgeable birder in our region. At that point…

Multimodal Knowledge Transfer to LLMs via Preference Optimization for Biomedical Applications

Mon, 04 May 2026 00:00:00 +0000

Wang et al. introduce MINT, a framework that transfers knowledge from multimodal biomedical models to unimodal large decoder models through preference optimization. Applied to rare-disease prediction and tissue-type classification, MINT improves task performance while preserving general reasoning ability and offers a practical strategy for extending scarce multimodal biomedical knowledge to more flexible foundation models.

Evaluating Claude’s bioinformatics research capabilities with BioMysteryBench

Wed, 29 Apr 2026 20:26:00 +0000

Science Apr 29, 2026 In this post, Brianna, a researcher on the discovery team, shares results from a recent bioinformatics benchmarking effort. Almost as soon as large language models could hold a conversation, people started asking how they’d stack up against human experts. Could models pass the bar exam? Could they answer medical licensing questions, or solve Olympiad math problems? Such benchmarks—self-contained sets of human-vetted problems designed to evaluate a capability of a model—have now become a source of competition across AI developers, reported in model release system cards and tracked on many online leaderboards. Competition aside, benchmarks help us tackle an important question: whether models are capable and reliable enough to support, or even produce, professional-level work. Scientists are using models to write code for analysis pipelines, propose hypotheses, and draw conclusions from data with the long-term aim of accelerating innovation and discovery. But exactly how proficient is AI in science right now, and how quickly are Claude and other models improving? To answer this, the research community has built several benchmarks. MMLU-Pro tests expert-level knowledge and reasoning questions. GPQA poses graduate-level, "Google-proof" questions in biology, physics, and chemistry. LAB-Bench tests biology-specific knowledge work—reading the literature, interpreting figures, reasoning about protocols. Although these benchmarks were developed in the “chatbot” era, they’ve persisted into the agent and tool-use era, joined by even more difficult scientific reasoning evals like FrontierScience and Humanity's Last Exam, because knowledge and reasoning remain a vital measure of scientific capability. Still, many real-world scientific tasks demand more than that. They require reading papers, querying databases, running experiments, coding and analysis. Now that models can do many of these things, benchmarks have evolved to reflect these workflows. BLADE tasks a model with a dataset and an open-ended task, and checks if the model takes similar analysis steps to a human scientist. BixBench uses biological datasets, and grades models on whether their conclusions line up with scientists’. In SciGym, the model is dropped into a sim…

Heard on Campus: Tanya Berger-Wolf on AI for Nature

Wed, 29 Apr 2026 07:00:00 +0000

Tanya Berger-Wolf, professor of computer science engineering; of electrical and computer engineering; and of evolution, ecology and organismal biology at Ohio State University, gave a lecture titled “AI for Nature: From Science to Action" as part of the Distinguished Lectures in Life Sciences series presented by the Huck Institutes of the Life Sciences.

Proof scarcity to proof abundance: the impedance mismatch

Tue, 28 Apr 2026 00:00:00 +0000

Terence Tao argues that generating and verifying proofs is still incomplete until results are digested and connected to the field.

Deep Learning Predicts Nuclear Lamina-Associated Chromatin

Mon, 27 Apr 2026 00:00:00 +0000

LADDER is a deep learning framework designed for predicting tethering of chromatin to the nuclear lamina via DNA sequence and associated genomic properties. By combining it with genome-wide random in silico deletions, the authors revealed an intrinsic genome lamina susceptibility landscape. LADDER can learn general features of lamina organization from one species and use them to extrapolate predictions to other mammalian species. Leveraging this cross-species capability, the authors examined allele-specific differential lamina tethering associated with mouse escapee genes.

‘The job description is changing’: mathematician Terence Tao on the rise of AI

Mon, 27 Apr 2026 00:00:00 +0000

In a Nature interview, Tao separates current research usefulness from hype and explains the growing role of formal verification and human judgment.

Anima Anandkumar Is Accelerating Scientific Discovery with AI

Fri, 24 Apr 2026 14:55:52 +0000

Scientific progress is often limited not by a lack of new ideas, but by the cost and complexity of testing them. New solutions are needed to make that testing easier—and researchers like Anima Anandkumar are leading the way. She has conducted cutting-edge research across academia and industry for over a decade, pioneering new AI algorithms that simulate physical systems with unprecedented speed and accuracy—in some cases, over a million times faster than traditional methods. By empowering AI to model these systems, her research has unlocked advances across science and engineering, from high-resolution weather forecasting to designing novel medical devices. “What fascinates me is how to bridge the gap between theory and practice, because I started at a time when deep learning wasn't there—you had to start from first-principles design methods,” says Anandkumar, who explains that her approach to designing algorithms builds on fundamental principles found in maths and physics. Anandkumar is the Bren Professor of computing and mathematical sciences at Caltech, where she leads the Anima AI + Science Lab. She has also worked as a principal scientist at Amazon Web Services, designing machine learning-based solutions for Amazon cloud and a senior director of AI research at Nvidia. Informed by other scientific domains, particularly physics, she says her focus has always been on making algorithms “more principled, hardware efficient, and robust.” Starting from this first-principles approach, Anandkumar and her collaborators developed “neural operators”: a kind of universal AI framework that can learn to simulate physical processes across multiple scales, from molecular interactions to climate patterns. Unlike large language models such as ChatGPT, AI models built with this framework can incorporate the laws of physics to test the plausibility of their predictions. And unlike traditional methods of simulating physical processes, which require immense computational resources to perform millions of calculations from scratch for each new prediction, these models are able to “learn shortcuts” from the data they’re trained on, Anandkumar explains—allowing them to simulate processes with equal or greater accuracy than methods that rely on raw computation, but at a much faster pace. Models designed in this way are particularly powerful because they “have the flexibility to le…

A four-hour conversation with Carina Hong on AI for Math, Lean, proof and intuition

Mon, 20 Apr 2026 00:00:00 +0000

Jùn Zhang and Carina Hong discuss translating mathematics into Lean, formal proof, mathematical intuition and building an AI-for-math company.

UCLA Computer Scientists, Mathematician Terence Tao Awarded Laude Moonshots Grant to Build AI System as Research Partner

Wed, 15 Apr 2026 07:00:00 +0000

UCLA Computer Scientists, Mathematician Terence Tao Awarded Laude Moonshots Grant to Build AI System as Research Partner Project aims to unlock progress in physics, cryptography and other challenging fields The multidisciplinary UCLA team will lead the Accelerating the Queen of Sciences project to develop an AI system capable of thinking like a mathematician and accelerating scientific breakthroughs. A team of six UCLA faculty members spanning artificial intelligence, computer science and mathematics has received a $250,000 seed grant from the Laude Institute as part of its inaugural Moonshots initiative, which supports university-led efforts to solve some of humanity’s hardest, undiscovered problems. The UCLA team was one of just eight selected from 125 proposals by more than 600 researchers across 47 institutions in the U.S. and Canada, as announced by the Laude Institute today. Led by Amit Sahai, a professor of computer science at the UCLA Samueli School of Engineering, other UCLA computer scientists include professor Raghu Meka, department chair Wei Wang, and associate professors Kai-Wei Chang and Nanyun (Violet) Peng. Terence Tao, a UCLA mathematics professor and director of special projects at the Institute for Pure and Applied Mathematics, is part of the team as well. “I’m most excited by our goal of creating AI capable of wonder-based discovery in mathematics — an AI system that not only solves problems rigorously, but also asks what might be true and explores the boundary between possible and impossible,” Amit Sahai said. Titled “Accelerating the Queen of Sciences,” the project seeks to develop an AI system for rigorous and creative mathematical thinking, ultimately serving as a research partner for scientists across every quantitative field. Rather than training AI to solve known problems, the team is focused on teaching AI to behave like a mathematician and ensure claims are automatically verified, turning ‘sounding right’ into ‘being right.’ “I’m most excited by our goal of creating AI capable of wonder-based discovery in mathematics — an AI system that not only solves problems rigorously, but also asks what might be true and explores the boundary between possible and impossible,” said Sahai, who holds the Symantec Term Chair in Computer Science. Today’s leading AI models can solve competition-level math problems and generate fluent explanations…

ChemGraph-XANES: An Agentic Framework for XANES Simulation and Curation

Wed, 15 Apr 2026 00:00:00 +0000

Computational X-ray absorption near-edge structure (XANES) is widely used to interpret local coordination environments, oxidation states, and electronic structure, but large computational campaigns are often limited by workflow complexity. We present ChemGraph-XANES, a large language model (LLM)-based agentic framework that combines documentation-grounded parameter retrieval via retrieval-augmented generation (RAG), schema-constrained tool execution, deterministic FDMNES input generation, Parsl-backed execution, and provenance-aware data curation. Scripted and natural-language interfaces share a common scientific backend for structure handling, parameterization, execution, spectral extraction, and optional post-processing. We evaluate three workflow modes: documentation-grounded parameter propagation, structure-file-based execution, and composition-based execution from a chemistry-level request. Repeated trials yielded end-to-end completion in 10/10 composition-based runs, 10/10 structure-file-based runs, and 9/10 documentation-grounded RAG runs. In every RAG run, the energy-grid specification retrieved from the FDMNES manual was correctly propagated, with the single end-to-end failure occurring downstream during multi-structure handling. In a separate task-parallel demonstration, the framework retrieved 21 TiO$_2$ structures from the Materials Project and submitted one FDMNES calculation per structure. All calculations completed successfully, with Parsl distributing the independent tasks across the user-configured worker pool. Together, these results show that ChemGraph-XANES provides a constrained and reproducible orchestration layer for computational spectroscopy, supporting consistent execution of representative tasks, documentation-linked parameter selection, and task-parallel generation of structure-linked XANES collections.

Evaluating Post-hoc Explanations of the Transformer-based Genome Language Model DNABERT-2

Wed, 15 Apr 2026 00:00:00 +0000

Explaining deep neural network predictions on genome sequences enables biological insight and hypothesis generation-often of greater interest than predictive performance alone. While explanations of convolutional neural networks (CNNs) have been shown to capture relevant patterns in genome sequences, it is unclear whether this transfers to more expressive Transformer-based genome language models (gLMs). To answer this question, we adapt AttnLRP, an extension of layer-wise relevance propagation to the attention mechanism, and apply it to the state-of-the-art gLM DNABERT-2. Thereby, we propose strategies to transfer explanations from token and nucleotide level. We evaluate the adaption of AttnLRP on genomic datasets using multiple metrics. Further, we provide an extensive comparison between the explanations of DNABERT-2 and a baseline CNN. Our results demonstrate that AttnLRP yields reliable explanations corresponding to known biological patterns. Hence, like CNNs, gLMs can also help derive biological insights. This work contributes to the explainability of gLMs and addresses the comparability of relevance attributions across different architectures.

Vivek Natarajan of Google DeepMind to Speak at RAAIS 2026

Mon, 13 Apr 2026 07:00:00 +0000

The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. In the run up to our 10th annual event on June 12th 2026 in London, we’re running a series of speaker profiles to shed more light on what you can expect to learn on the day! Vivek Natarajan is a Research Lead at Google DeepMind leading research at the intersection of AI, science, and medicine. He spoke at RAAIS in 2023 on the potential of large language models in medicine, and the progress since then has been remarkable. His work centers on a question that is rapidly becoming one of the most important in applied AI: what does it take to build systems that are useful in expert domains like healthcare and scientific discovery? In medicine especially, performance means reasoning under uncertainty, handling complex interactions, and meeting a far higher bar for trust and reliability. From medical benchmarks to clinical capability Vivek is the lead researcher behind Med-PaLM (Nature, 2023) and Med-PaLM 2 (Nature Medicine, 2025), the first AI systems to achieve passing and expert-level scores respectively on US Medical Licensing Examination questions. Med-PaLM 2 scored up to 86.5% on the MedQA dataset, an improvement of over 19 percentage points on its predecessor, and produced answers that physicians rated as comparable or preferred to those from human doctors. Medicine is one of the clearest examples of a domain where surface-level language ability is not enough. A model has to retrieve specialist knowledge, reason carefully, and communicate in a way that reflects the stakes of the setting. Med-PaLM helped shift the conversation from whether language models could be adapted to medicine at all, to how they should be evaluated, where they might be useful, and what standards they need to meet. Project AMIE and the move toward real clinical interaction Vivek co-leads Project AMIE (Articulate Medical Intelligence Explorer), a research program aiming to build and democratize medical superintelligence. AMIE is not a question-answering system: it is a conversational diagnostic agent that gathers symptoms, asks follow-up questions, reasons across specialties, and now interprets visual medical information through its multimodal capabilities. In March 2026, the team published results from a prospective clinical feasibili…

Anemoi: A European framework for operational AI in weather and climate

Tue, 07 Apr 2026 12:00:00 +0000

Artificial intelligence (AI) is rapidly transforming weather and climate science across Europe. What is changing is not just how models are built, but how information is produced, delivered and used by national meteorological services, public authorities and downstream users. We are shaping this transformation with our Member and Co‑operating States, building on decades of shared investment in data, infrastructure and operational expertise. Anemoi plays a key role in this transformation. Developed by ECMWF together with several national meteorological services across Europe, Anemoi is a joint European framework enabling the development, training and operation of large‑scale AI models for weather and climate. Rather than focusing on individual models, Anemoi provides shared foundations – datasets, workflows and software infrastructure – that allow AI to move from research into pre‑operational and operational use across national meteorological services and at ECMWF. From data to decisions: why Anemoi matters Anemoi supports rapidly evolving machine‑learning (ML) techniques while maintaining the reliability, traceability and robustness required for operational services. Achieving this at scale requires much more than neural network architectures alone. It requires: - AI‑ready datasets built with deep Earth‑system domain knowledge - Robust, reusable workflows that run efficiently on distributed European HPC systems - Integrated tooling for training, inference, verification and monitoring Anemoi was created to address this shared need. Its focus on end‑to‑end workflows allows new AI capabilities to be developed, tested and deployed collaboratively, without each user having to rebuild the full software stack. This supports innovation while preserving scientific rigour and operational consistency across Europe. What is Anemoi? Anemoi is an end‑to‑end software framework for AI weather and climate applications. It covers the full lifecycle from data preparation to model training and large‑scale inference. The framework is modular, Python‑based and designed to run efficiently on modern high-performance computing (HPC) architectures, including EuroHPC systems. The framework brings together three tightly integrated components. 1. Anemoi datasets Anemoi produces and manages AI‑ready datasets optimised for ML workloads. These datasets differ fundamentally from traditiona…

Animal Language Processing: An AI Convergence In Animal Communication

Thu, 02 Apr 2026 17:22:01 +0000

Image credit: Elianne Dipp / Pexels Over the last decade, work on animal communication has reached a convergence as the growing availability of large-scale behavioral and bioacoustic data merges with relatively recent advances in machine learning. As a result, we’re starting to see the study of animal communication shift from narrowly defined, species-specific, and hypothesis-limited analyses toward data-driven discovery at scale. We refer to this convergence as Animal Language Processing (ALP), an approach that uses AI to study non-human communication at scale. The term was first used in a presentation given in Japanese by our senior AI researcher Masato Hagiwara in 2024, and it offers a useful framework for describing and organizing the shift we’re observing in the field. How Animal Language Processing Emerged For decades, ethologists, field biologists, and bioacousticians have built the foundations that make studying animal signals both possible and worthwhile. They’ve conducted long-term field studies that link vocal behavior to social context, ecology, and individual life histories. They’ve conducted playback experiments that test how animals respond to specific calls, sequences, or contexts. These experiments are vital for grounding any AI-based predictions in real behavior. We’ve also seen methodological innovations in recording, tagging, and annotation, from autonomous recorders to animal-borne sensors. This work has resulted in incredible breakthroughs, like discovering that elephants call out to each other using distinct names and that female beluga whales have a “come back!” call to coax their curious calves back from wandering too far. That’s all to say that this emergent field builds on decades of accumulated knowledge, methods, and data, and expands what is possible with modern AI tools. What’s Changed: Data, Models, and Scale Several shifts have made a fundamentally different approach possible: 1. Data volume and diversity First, advances in sensing technologies have led to an unprecedented accumulation of behavioral and acoustic data across many species and environments. Tools like passive acoustic monitoring, animal-borne tags, and long-term video recording now capture communication that was previously too hard to capture and annotate, including continuous, overlapping, and context-rich interactions. 2. Bigger questions are being asked acro…

Mathematical Methods and Human Thought in the Age of AI

Sun, 29 Mar 2026 00:00:00 +0000

Tanya Klowden and Terence Tao examine how AI and formalization may change the purpose, practice and social role of mathematics.

Severe storms in the age of machine learning

Fri, 27 Mar 2026 09:00:00 +0000

Machine learning (ML) weather models are evolving rapidly, and a key question is: how well do these systems represent the fast-evolving dynamics that define severe mid-latitude storms? We explored this question using Storm Amy, the first named storm of the 2025–26 season for the western group of the European storm-naming initiative. Amy brought disruptive winds across northwestern Europe in early October 2025. We compared the performance of ECMWF’s forecasting systems: the physics-based Integrated Forecasting System Control forecast (IFS-CF, Cycle 49r1), the ML-based Artificial Intelligence Forecasting System deterministic forecast (AIFS Single), and the control member of the AIFS Ensemble forecast (AIFS ENS CF). To put the results in context, we also looked at Storm Éowyn, a more classical winter storm. Capturing Storm Amy’s evolution Storm Amy developed rapidly from the interaction of the remnants of tropical cyclones Imelda and Humberto, southwest of the North Atlantic storm track (Figure 1). By 3 October, Amy’s centre had crossed the northern fringes of Scotland as a deepening cyclone, before stalling north of Shetland and turning towards Norway. All three forecasting systems captured the storm track, pressure pattern, and depth of the low reasonably well for these short lead times. In particular, ML-based systems continue to perform well on these larger-scale flow features. The main differences appear in how well each system captures localised wind maxima linked, in part, to Amy’s strong frontal gradients. Figure 1: Animation of mean sea-level pressure (MSLP) and 10 m wind speed for Storm Amy, based on forecasts initialised on 2 October 2025 at 00 UTC. The analysis is shown in the top-right panel; the bottom row displays IFS Control (IFS CF), AIFS Single, and AIFS Ensemble Control forecast (AIFS ENS CF). Wind and pressure verification Focusing on the passage of Amy over the region in Figure 2a, all models captured the storm’s overall intensity, but some struggled with the timing of its rapid deepening. The AIFS Single initially underestimated the depth due in part to land influence on 3 October, 18 UTC (see also Figure 3c). Although the forecast for midnight improved, the system was still a few hectopascals shallower than the IFS analysis. Later, the AIFS Single recovered rapidly and captured the depth of the low remarkably well during the storm’s matu…

Vibe physics: The AI grad student

Mon, 23 Mar 2026 23:00:00 +0000

Science Mar 23, 2026 Can AI do theoretical physics? In this guest post, professor of physics Matthew Schwartz decided to find out by supervising Claude through a real research calculation, start to finish, without ever touching a file himself. His account of what happened is below. - I guided Claude Opus 4.5 through a real theoretical physics calculation, encapsulating the complexity of code and computations behind text prompts. - The result was a technically rigorous, impactful high-energy theoretical physics paper in two weeks instead of the usual year. - Over 110 separate drafts, 36M tokens, and 40+ hours of local CPU compute, Claude proved fast, indefatigable, and eager to please. - Claude is impressively capable, but also sloppy enough that I found domain expertise essential for evaluating its accuracy. - AI is not doing end-to-end science yet. But this project proves that I could create a set of prompts that can get Claude to do frontier science. This wasn’t true three months ago. - This may be the most important paper I’ve ever written—not for the physics, but for the method. There is no going back. I’m Matthew Schwartz, a professor of physics at Harvard and a principal investigator in the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI). My area of expertise is quantum field theory, which asks what matter is, how particles interact, and why the Universe has the rules it does. One might say I wrote the book on the subject. I’ve been working with modern machine learning tools for over a decade. My first modern ML paper, from 2016, was an early application of deep learning to particle physics. In a Nature Reviews Physics piece in 2022, I compared the timescale of AI and human evolution, arguing that transferring understanding between biological and artificial intelligence would become a fundamental challenge. Since then, I’ve been trying to push AI towards more symbolic work (manipulating mathematical expressions rather than numerical data) and the core questions in theoretical physics. There has been a lot of recent hype about AI scientists doing end-to-end research autonomously. In August 2024, Sakana AI released their AI Scientist, a system designed to automate the entire research lifecycle—from…

Long-running Claude for scientific computing

Mon, 23 Mar 2026 23:00:00 +0000

Science Mar 23, 2026 In this post, Siddharth Mishra-Sharma, a researcher on the Discovery team, explains how to apply multi-day agentic coding workflows—test oracles, persistent memory, and orchestration patterns—to scientific computing tasks even outside of one’s domain. Most scientists currently using AI agents work in a conversational loop, managing each step of the process on a tight leash. As models have become significantly better at long-horizon tasks over the last year or so, a new way of working emerged: rather than getting involved with every detail, we can specify the high-level objective and set a team of agents loose to work autonomously. This makes it possible to complete projects in mere hours that might otherwise take us days, weeks, or even months. Certain types of scientific tasks fit well within this model, e.g., reimplementing a numerical solver, converting legacy scientific software written in an old Fortran dialect to a modern language, and debugging a large codebase against a reference implementation. These are tasks where the work is well-scoped, the success criteria are clear, and human oversight can be occasional rather than continuous. Anthropic’s C compiler project demonstrated a version of this, where Claude worked across roughly 2,000 sessions to build a C compiler capable of compiling the Linux kernel. This post describes how to set up a similar pattern for scientific computing tasks using Claude Code, with a typical academic lab in mind. As a concrete example, I will walk through using Claude Opus 4.6 to implement a differentiable version of a cosmological Boltzmann solver. This is numerical code that predicts the statistical properties of the afterglow of the Big Bang—the Cosmic Microwave Background, or CMB. It does this by evolving coupled equations for photons, baryons, neutrinos, and dark matter through the early universe. Boltzmann solvers like CLASS and CAMB are core pieces of scientific infrastructure in cosmology, allowing us to constrain cosmological models using data from surveys like Planck and the Simons Observatory. A differentiable version—one that can propagate gradients through the full solver—enables the use of gradient-based inference methods, dramatically speeding up parameter estima…

insitro Expands ALS Drug Development Collaboration with BMS

Mon, 23 Mar 2026 12:01:52 +0000

Share Amyotrophic Lateral Sclerosis (ALS) presents one of the most formidable challenges in medical science. For too long, the search for effective treatments has been hampered by a temptation to revisit familiar hypotheses and established biological pathways. While understandable given the slow progress and frequent setbacks, this approach rarely advances the science. At the heart of insitro’s collaboration with Bristol Myers Squibb (BMS) is the deliberate decision to boldly seek novel biology. We generated billions of multimodal data points from human-derived cellular systems and used machine learning and AI to guide our understanding of ALS biology. Our unbiased approach revealed novel biological targets – points of intervention that have been previously invisible. Today, we are excited to announce an expansion of our collaboration with BMS toward the development of new therapeutic approaches for the treatment of ALS. BMS nominated two additional ALS targets – ALS-2 and ALS-3 – under our collaboration. insitro will advance its own oligonucleotide program for ALS-1 while simultaneously progressing a small molecule for BMS. This expansion underscores the value of taking a fundamentally different approach in looking for treatments in a disease that has challenged every conventional approach. The Virtual HumanTM: Letting the Data Lead ALS is heterogeneous, its biology is complex, and many of the most intuitive hypotheses have already been tested, mostly unsuccessfully. This is precisely where insitro’s Virtual Human™ Causal Discovery Platform becomes uniquely enabling. Our platform is built on the premise that to truly understand the drivers of complex disease, one must measure it across multiple biological domains and modalities. This allows us to employ machine learning to disentangle intricate biology and uncover patterns that are invisible to conventional human inspection. To demonstrate the power of this premise, we worked with over 200 distinct ALS cell lines – both engineered models and patient-derived lines, spanning familial (inherited) and sporadic disease. We developed protocols to generate motor neurons robustly and rapidly enough to support large-scale experimentation while maintaining disease-relevant biology. Crucially, we established an imaging and screening infrastructure, including our POSH technology for pooled optical screening, capable o…

insitro and Bristol Myers Squibb Expand ALS Collaboration with Nomination of New Targets

Mon, 23 Mar 2026 12:01:15 +0000

Collaboration expands to include two additional therapeutic targets for the treatment of ALS discovered via insitro’s AI-driven Virtual Human™ platform Joint effort focuses on identifying key biological drivers to deliver disease-modifying interventions for ALS patients SOUTH SAN FRANCISCO, Calif.--(BUSINESS WIRE)--insitro, the AI therapeutics company built on causal biology, today announced the expansion of its strategic collaboration with Bristol Myers Squibb (NYSE: BMY) to advance a broadened portfolio of therapeutic programs for amyotrophic lateral sclerosis (ALS). The collaboration is focused on accelerating and delivering disease-modifying interventions designed to address the underlying biological drivers of ALS. BMS has nominated two additional targets, ALS-2 and ALS-3, which were identified through insitro’s Virtual Human™ platform. These join the initial target, ALS-1, nominated by Bristol Myers Squibb in December 2024. The companies will leverage multiple therapeutic modalities to address the nominated targets. insitro will advance its own oligonucleotide program for ALS-1 while simultaneously progressing a small molecule program for BMS for ALS-1. This multimodality development strategy is designed to maximize the opportunities to impact patients as quickly and effectively as possible. insitro received a $10 million milestone payment in connection with the selection of the two additional targets. "We are driven by a sense of urgency to translate our biological insights into meaningful clinical outcomes for the ALS community," said Daphne Koller, Ph.D., founder and CEO of insitro. "Our platform allows us to build a data-driven map of the impact of ALS on motor neurons and identify novel drivers of neurodegeneration. By expanding our collaboration with Bristol Myers Squibb, we are broadening our approach to tackling this devastating disease, with a set of compelling targets that address its fundamental mechanisms, with the goal of delivering disease-modifying therapies to the many patients who cannot wait.” Leveraging insitro’s Virtual Human™ causal biology discovery platform, the company has identified a series of high-impact targets that play a central role in the biological mechanisms underlying ALS. By integrating massive-scale, human-derived cell data with machine learning, the Virtual Human™ allows for the mapping of disease drivers with unp…

Interview: James Zou — EMJ

Tue, 17 Mar 2026 07:00:00 +0000

James Zou | Associate Professor of Biomedical Data Science, Stanford University, California, USA Citation: EMJ. 2026;11[1]:28-31. https://doi.org/10.33590/emj/OR9BWSEN When did you first become interested in applying AI to clinical research? I’ve been working at the intersection of AI and biomedicine for about 15 years now, since my PhD. My initial work was more focused on using AI to enable biomedical discoveries. When I came to Stanford, California, USA, in 2016, that’s when I started doing more work on AI for clinical research. Your work spans clinical prediction, genomics, and large-scale AI models. When you look at the field today, what’s changed the most since you first started working in healthcare AI? I think the biggest change we’ve seen is the shift from viewing AI as a tool to viewing AI as more of an agent or co-scientist. In the past, when I first started in the field, we would begin with a specific problem in medicine or healthcare and then apply or develop an AI tool to tackle that problem. But in the last 2 years, we’ve seen the emergence of much more autonomous AI driven by language models and AI agents. Because these agents are more autonomous, they can start to come up with their own problems. They don’t have to wait for us to define the problem. They can generate hypotheses and even create their own tools. From a clinician’s perspective, for example, in dermatology, when diagnosing melanoma, if AI provides a conclusion, a clinician still has to review it. Do you think that will always be necessary, or could we step aside in the near future? Currently, it’s still necessary for human experts and clinicians to provide final judgment and oversight over these AI systems. Not necessarily for every step, but for the most important decisions. For example, the final diagnosis or treatment decision. I think that oversight is still necessary. Some studies suggest AI can make fewer mistakes than clinicians. So why do we still need to revise AI decisions? AI is still a relatively new and emerging technology. The algorithms might work very well on the specific data and hospitals they were trained on, and they could be very reliable there, but if they’re applied in the wild, in new clinics or with different patient populations, there’s still uncertainty about how the models will perform. They might work very well and be robust, but there’s also a chanc…

How to make the most of your masked language model for protein engineering

Sun, 15 Mar 2026 00:00:00 +0000

Despite the recent release of numerous protein language models, relatively little work has addressed how to best sample from them to optimize desired biological properties. This paper fills that gap by proposing a flexible and effective sampling method for masked language models (MLMs), and by systematically evaluating models and methods both in silico and in vitro in the context of antibody therapeutics campaigns. The authors propose sampling with stochastic beam search, exploiting the fact that MLMs can efficiently evaluate the pseudo-perplexity of the entire 1-edit neighborhood of a sequence. Reframing generation as whole-sequence evaluation enables flexible guidance with multiple optimization objectives. Their extensive in vitro head-to-head evaluation for antibody engineering reveals that the choice of sampling method can have a substantial impact, motivating further research into this under-explored area.

OpenAI and Ginkgo Bioworks put GPT-5 to work designing lab experiments

Fri, 13 Mar 2026 07:00:00 +0000

OpenAI’s GPT can summarize research papers and make predictions—but can it do science? Can it generate hypotheses, design experiments, interpret results and iterate? Last summer researchers at OpenAI and Ginkgo Bioworks, a company that designs and installs autonomous, robot-run labs, decided to find out. Though artificial intelligence systems have posted high scores in math, physics and computer science, biology is harder to measure, says Joy Jiao, who leads life sciences research at OpenAI. “For something like ‘design the optimal experiment,’ there’s no right answer. It’s what we call a hard-hard problem: it’s hard to generate a solution, and it’s also really hard to verify.” That led the team to have AI design experiments using superfolder green fluorescent protein (sfGFP), an engineered jellyfish protein that is a common benchmark because it provides a fast, unambiguous signal: it glows green. While OpenAI’s GPT-5 provided the experimental designs, Ginkgo Bioworks provided what its co-founder and CEO Jason Kelly calls the “Waymo” of biology: an automated lab system where researchers set objective and the AI does the driving. The autonomous robotic lab can rapidly process experiments and operate without constant human oversight. On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. The team focused its experiment on cell-free protein synthesis (CFPS), a technique for producing proteins without living cells. Traditional biomanufacturing relies on genetically modifying living cells to produce medicines like insulin. CFPS makes proteins outside of cells by running the cell’s own protein-making machinery in a controlled mixture. “It is one of the fastest ways to make proteins,” says Reshma Shetty, chief operating officer and co-founder of Ginkgo Bioworks. “You don’t need to clone your DNA, put it into the cell and wait for the cell to grow up.” Improving CFPS could have significant implications for medicine, food and agricultural products. From OpenAI’s San Francisco, Calif., headquarters, GPT-5 designed experiments and sent them across the country to Ginkgo Bioworks’ robotic systems in Boston. As it iterated, GPT-5 analyzed incoming data and…

"AI Has Overwhelmed Human Exam Questions": Terence Tao and Mark Chen in Conversation - Large Language Models No Longer 'Math Underachievers'

Wed, 11 Mar 2026 07:00:00 +0000

"AI Has Overwhelmed Human Exam Questions," A Conversation Between Terence Tao and Mark Chen: Large Language Models Are No Longer "Math Underachievers" Recently, at a seminar hosted by the Institute for Pure and Applied Mathematics (IPAM), Fields Medalist Terence Tao and Mark Chen, the Chief Researcher at OpenAI, participated in a "fireside chat." The two discussed the leap - forward progress of AI in the past year and how it will fundamentally change the way of mathematical research. During the conversation, Terence Tao and Mark Chen discussed the key changes in AI capabilities. A year ago, Terence Tao once commented that GPT's performance in mathematics was "like a very inefficient graduate student." Today, AI has won gold - level results in the IMO competition, and the benchmark tests written by humans are being rapidly "broken." It can be said that in the field of mathematics, large models have basically shed the label of "poor student." In Terence Tao's view, mathematics is a place where experiments are inexpensive and where trial - and - error is inexpensive. "If you are an engineer and a bridge collapses, that's an expensive mistake; if you are a surgeon and cut the wrong organ, that's an expensive mistake." As AI begins to quickly solve some Erdős problems that have long been ignored, the mathematical community is also re - thinking the division of research labor, human - machine collaboration, and changes in the education system. When calculation and verification can be outsourced to machines, the form of mathematical research may be quietly changing. The following is the highlights of the "fireside chat" between Terence Tao and Mark Chen: Question: What was your evaluation of AI's performance in mathematics a year ago? What changes have occurred in the past year? Terence Tao: The changes are very significant. AI itself is progressing, but more importantly, it has begun to integrate into our daily research. The current in - depth research and literature search are far more effective than traditional methods, and code generation is also quite reliable. Terence Tao, a Fields Medalist, a Chinese - American mathematician, and a professor at the University of California, Los Angeles As a pure mathematician, I don't rely heavily on AI, but it has indeed changed my habit of handling problems. For example, if I want to verify a certain conjecture, I'll let…

10 Years of AlphaGo

Mon, 09 Mar 2026 00:00:00 +0000

Demis Hassabis traces how AlphaGo's search and learning ideas fed into AlphaFold, AlphaProof, AlphaEvolve and the broader scientific-AI program.

Terence Tao on generative AI in mathematics: Erdős solutions are 'cheap wins'

Tue, 24 Feb 2026 08:00:00 +0000

Much of the excitement around the news has stemmed from the adjudicator of these AI-written proofs: Terence Tao, a UCLA professor who is widely considered to be the world’s greatest living mathematician, reported The Atlantic. When Tao was interviewed about what AI can offer mathematics, he was more tempered. The AI-generated Erdős solutions are impressive, he said, but not overwhelmingly so: The bots have functionally landed some “cheap wins,” Tao said. See more about UCLA in today’s LAist, New York Times and other outlets.

The AI-augmented scientist

Tue, 24 Feb 2026 00:00:00 +0000

Climate scientist Zeke Hausfather documents a live AI-assisted modeling workflow, including a plausible wrong answer caught by domain expertise.

SEISMO: Explanation-Aware, Trajectory-Conditioned LLM Agents for Sample-Efficient Molecular Optimisation

Sun, 15 Feb 2026 00:00:00 +0000

Optimizing molecules to achieve desired properties is a central bottleneck across the chemical sciences, particularly in the pharmaceutical industry, where it underlies the discovery of new drugs. Since molecular property evaluation often relies on costly and rate-limited oracles, such as experimental assays, molecular optimization must be highly sample-efficient. To address this, we introduce SEISMO, an LLM agent for inference-time molecular optimisation that turns information routinely available alongside the oracle score, but discarded by existing methods, into an explicit guidance signal. Rather than treating the oracle as a scalar black box, SEISMO conditions each proposal on a natural-language task description, the full optimization trajectory, and machine-readable feedback derived from post-hoc explainability methods and sub-score decompositions. Across a wide range of drug-discovery-relevant tasks, this consistently improves sample efficiency over existing optimisers as well as zero-shot LLM generation, with gains growing as explanatory feedback is enriched. In practice, medicinal chemists can inspect the agent's reasoning and intervene to steer generation in natural language, keeping them central to molecular optimisation projects.

Gemini Deep Think: Redefining the Future of Scientific Research

Wed, 11 Feb 2026 08:00:00 +0000

Accelerating Mathematical and Scientific Discovery with Gemini Deep Think Under direction from expert mathematicians and scientists, Gemini Deep Think is solving professional research problems across mathematics, physics, and computer science In the summer of 2025, an advanced version of Gemini Deep Think achieved Gold-medal standard at the International Mathematics Olympiad (IMO) and later, an updated version, obtained similar results at the International Collegiate Programming Contest. These results demonstrated the model could reason through some of the most challenging math and programming problems designed for students. Since then, Gemini Deep Think mode has moved into science, engineering and enterprise workflows to tackle more complex, open-ended challenges. In the last week, our teams published two papers (1, 2) detailing a cross-disciplinary effort to solve professional research problems using Gemini Deep Think mode. These results stem from deep collaboration between mathematicians, physicists, and computer scientists. The Frontier of Pure Mathematics Unlike IMO problems, research-level mathematics requires advanced techniques from vast literature. While foundation models have large knowledge bases, data scarcity often leads to superficial understanding and hallucinations in advanced subjects. To solve this, we built a math research agent (internally codenamed Aletheia), powered by Gemini Deep Think mode. It features a natural language verifier to identify flaws in candidate solutions and enable an iterative process of generating and revising solutions. Crucially, this agent can admit failure to solve a problem, a key feature that improved the efficiency for researchers. Additionally, the research agent uses Google Search and web browsing to navigate complex research, preventing spurious citations and computational inaccuracies when synthesizing published literature. Since achieving IMO Gold-medal standard in July 2025, Gemini Deep Think has progressed rapidly, scoring up to 90% on the IMO-ProofBench Advanced test as inference-time compute scales. We demonstrated that the scaling law continues to hold as we progress beyond Olympiad level into PhD-level exercises (per our internal FutureMath Basic benchmark). Notably, Aletheia demonstrated that higher reasoning quality can be achieved at a lower inference-time compute. For research-level math, Aleth…

Accelerating Mathematics

Mon, 09 Feb 2026 13:23:22 +0000

Let’s say that someone had a big pot of money, and wanted to use it to accelerate mathematical discovery. How might they go about doing this? The traditional approach Historically it has been governments who have been driving this agenda, with taxpayer-funded institutions such as the NSF (in the US), the EPSRC (in the UK), the ERC (in Europe) and many other national institutions (NSERC, ARC, DFG, JSPS…) typically funding individuals and small teams to work on cutting-edge problems at the boundary of mathematical understanding. These institutions also fund centres and institutes where mathematicians gather for workshops and exchange ideas. The question I want to raise here is: can new advances in technology somehow accelerate the process of growing mathematical knowledge and understanding in different ways to the traditional ones? And I want to argue that right now this is not as easy as it sounds. “Duh just let AI do it” The immediate and unhelpful answer from the AI bros (who may have no understanding of the nature of mathematics) is “this question is not even relevant, AGI will happen within the next few years and then math will be solved”. But this is not what is happening on the ground. Right now there seem to be two relevant technologies poised to help disrupt mathematics: the language model (LLM) and the interactive theorem prover (ITP). There is progress, but there is certainly no “killer app” yet. How are experts using these tools in 2026? Uses of LLMs in mathematics Professional mathematicians seem to be using these tools to do a variety of things. For example: High-level brainstorming for ideas; Literature search; Solving intermediate undergraduate/PhD level questions which arise on the way to harder problems. Amateurs and cranks aim higher, and are currently producing large documents which purport to contain breakthrough results, but which do not survive 5 minutes of expert scrutiny. I continually find it surprising that a non-expert can genuinely believe that they might contribute to research mathematics using only the tools which the experts also have access to. The slop being produced by LLMs is getting better, and I find it extremely depressing ploughing through this kind of stuff, because the AI wants to please, so it wants to prove your theorem for you, even if your theorem is false or not accessible given the tools that humanity currently…

First vaccine targeting SARS virus family enters human trials

Tue, 03 Feb 2026 00:53:27 +0000

A vaccine called GBP511 has begun clinical testing in Australia. Pioneered by UW Medicine researchers, it is intended to protect against COVID-19 and related coronaviruses — including some that haven’t yet jumped to humans. South Korean pharmaceutical company SK bioscience is bringing the vaccine to trial. “GBP511 is the first vaccine to reach human testing that is intended to protect against multiple strains of the virus that causes COVID-19 as well as related coronaviruses with the potential to spark dangerous outbreaks,” said Neil King , associate professor of biochemistry at UW Medicine and co-inventor of the nanoparticle platform underlying the vaccine. “This clinical trial is an important step toward broadly protective vaccines, and we are grateful for the team at SK bioscience for developing the vaccine and CEPI for their visionary support of it.” What are sarbecoviruses? Three major disease outbreaks in the past two decades — SARS, MERS, and COVID-19 — have been caused by coronaviruses. Sarbecoviruses are a subgenus of coronaviruses that includes SARS-CoV-2 (the virus that causes COVID-19), the original SARS-CoV virus, and numerous bat coronaviruses with pandemic potential. UW Medicine biochemists Neil King, PhD, (left) and David Veesler, PhD, (right) have been studying coronaviruses and vaccines since before the COVID-19 pandemic. How it works The vaccine’s core is a computer-designed protein particle — a precise molecular assembly that does not exist in nature. To turn this tiny object into a vaccine, scientists at UW Medicine attached four receptor-binding domains (RBDs) from different coronaviruses — two from SARS-CoV-2, one from SARS-CoV-1, and one from a bat coronavirus (BtKY72) — on the surface. “The beauty of this approach is that by presenting the immune system with multiple related antigens at once, we can train it to recognize features that are conserved across the entire sarbecovirus family,” said David Veesler , professor of biochemistry at UW Medicine and Howard Hughes Medical Institute Investigator, who led the preclinical studies. “That’s exactly what you need to prepare for unpredictable future threats.” In preclinical studies, GBP511 protected animals from related viruses they weren’t directly immunized against. About the trial The international Phase 1/2 trial , which began enrollment in January 2026, will evaluate safety and immu…

Mapping the Genetics of Brown Fat: insitro's Presentation at Keystone Symposium

Mon, 02 Feb 2026 22:39:38 +0000

Share How insitro unlocked population-scale human genetics in a tissue that has resisted measurement for decades – and what it reveals about peripheral approaches to metabolic disease The Furnace We Couldn’t See The human body contains a type of fat tissue engineered to burn energy rather than store it. Brown adipose tissue – dense with mitochondria, rich in uncoupling proteins – converts glucose and lipids into heat through non-shivering thermogenesis. For years, researchers have documented this tissue’s role in energy expenditure, its associations with favorable metabolic profiles, its potential as a therapeutic target for obesity and cardiometabolic disease. What they have not been able to do is map its genetic architecture. The genetic variation was there, distributed across the genomes of hundreds of thousands of biobank participants. The barrier was measurement itself. Brown fat, sparse and anatomically diffuse, concentrated in small depots around the neck and upper chest, resists the standardized quantification that genetic discovery requires. The gold standard for detection – PET-CT scans with radioactive tracers – involves radiation exposure and cannot scale to the many thousands of participants required for genome-wide association. Without scalable measurement, the genetic programs governing this tissue remained inaccessible to systematic investigation. Research presented this week by instiro at the Keystone Symposia on Obesity Therapeutics demonstrates that this constraint can be resolved – and that resolving it opens a therapeutic frontier that has been structurally closed to drug discovery. Why Human Genetics Matters Translating brown fat biology into therapies has proven difficult. Many targets derived from rodent studies have failed to produce meaningful weight loss in human trials, despite showing BAT activation in mice. FGF21 agonists, orexin antagonists, β-adrenergic receptor agonists – each effective in rodents, each disappointing in humans. This translation gap underscores the need for discovery anchored in human genetics from day one. “To power human genetics, you need tens of thousands of people – but brown fat measurements have historically required expensive approaches such as PET scans, which is hard to acquire at scale,” said Daphne Koller, Ph.D., founder and CEO of insitro. “AI enables these measurements to be derived from MRIs, u…

insitro Completes First AI-Enabled Human Genetics Study of Brown Adipose Tissue, Shares Differentiated Targets with Anti-Obesity Effects

Mon, 02 Feb 2026 21:29:56 +0000

Share AI-enabled brown adipose tissue (BAT) phenotype unlocks population-scale human genetic discovery BAT-01 knockdown drives 15% body-weight reduction in preclinical models through peripheral adipose beiging, preserving lean mass Key findings presented at the Keystone Symposia on Obesity Therapeutics SOUTH SAN FRANCISCO, Calif. – Feb 2, 2026 – insitro, the AI therapeutics company built on causal biology, today reported research demonstrating that artificial intelligence enables population-scale genetic analysis in tissues that have historically been challenging to study in large cohorts. In a first-of-its-kind genome-wide association study (GWAS) of brown adipose tissue (BAT) quantified using computer vision, insitro identified genetic loci associated with BAT biology and subsequently validated a prioritized target, BAT-01, the modulation of which produced significant weight loss in diet-induced obese mice while preserving lean mass potentially through a peripheral mechanism distinct from centrally acting appetite suppressants. The findings were presented by David Lloyd, Ph.D., Senior Vice President, Metabolic Disease and Translational Pharmacology, at the Keystone Symposia on Obesity Therapeutics. BAT has become increasingly implicated in metabolic health, but its anatomical diffuseness and functional heterogeneity have limited understanding of its genetic regulation in humans. “To power human genetics, you need tens of thousands of people – but brown fat measurements have historically required specialized approaches such as PET scans, which are hard to acquire at scale,” said Daphne Koller, Ph.D., founder and CEO of insitro. “AI enables these measurements to be derived from broadly available MRIs, unlocking a first-ever analysis of human genetics for BAT. Our results provide strong evidence for the important role that BAT plays across multiple metabolic health outcomes, and potentially reveal novel peripheral mechanisms for fat reduction, orthogonal to the central appetite pathways dominating current obesity therapies.” Leveraging its ClinML™ platform, insitro developed a machine learning-derived BAT imaging phenotype from UK Biobank Dixon MRI fat-signal fraction maps (n=69,598), using the delta between abdominal and supraclavicular adipose fat-signal fraction, to estimate brown fat content. Before launching the GWAS, the team established biological spe…

FrontierMath: Open Problems

Tue, 27 Jan 2026 00:00:00 +0000

Epoch AI released a pilot of 14 research-level open problems with programmatically checkable answers to test the frontier beyond existing benchmarks.

insitro to Acquire CombinAbleAI to Complete Its Full Stack, Modality-Agnostic AI Platform for Drug Discovery and Design

Mon, 12 Jan 2026 17:00:00 +0000

Share TherML™ integrates CombinAbleAI’s advanced, next generation biologics expertise with insitro’s existing small molecule and oligonucleotide capabilities, unifying therapeutic design across all major drug modalities in a single AI engine SOUTH SAN FRANCISCO, Calif. – Jan. 12, 2026 – insitro, the AI therapeutics company built on causal biology, today announced the acquisition of CombinAbleAI and the launch of insitro’s TherML™ (Therapeutic Machine Learning) platform. The acquisition, which is expected to close in late January 2026, will complete insitro’s full stack, modality-agnostic capability spanning small molecules, oligonucleotides, antibodies, and other complex biologics with precision – enabling biology to guide both modality selection and molecular design from target discovery through developability assessment. This integrated approach accelerates the generation of clinic-ready assets while reducing late-stage development risk. “Drug discovery has traditionally optimized molecules for potency before assessing developability – often discovering that highly potent candidates face manufacturing constraints,” said Philip Tagari, Chief Scientific Officer, insitro. “By integrating CombinAbleAI’s physics-informed, AI-driven design for complex biologic therapeutics with our causal biology platform, we treat potency and manufacturability as interdependent design criteria from the outset. This fundamentally changes the translation from biological insight to viable therapeutic – instead of optimizing sequentially and hoping for serendipitous alignment, we’re designing for both simultaneously.” “We are delighted to welcome the CombinAbleAI team in Israel as fellow insitrocytes, alongside our colleagues in the U.S., Poland and Malaysia,” added Daphne Koller, Ph.D., founder and CEO, insitro. “Their addition helps launch our integrated TherML platform, which is a critical part of our end-to-end industrialized AI-enabled system built for repeatable, scalable, predictable drug discovery.” TherML supports the AI-driven design and optimization of multiple therapeutic modalities within a single platform. For complex biologics, including multi-specific antibodies and T-cell engagers, the platform utilizes CombinAbleAI’s physics-informed, AI-driven optimization engine, which is pre-trained on over 100,000 molecular dynamics surrogates to predict protein structure and…

Introducing insitro's TherMLTM: Rapidly engineering the right therapeutic for the right target

Mon, 12 Jan 2026 17:00:00 +0000

Share The primary driver of clinical attrition in drug discovery is often not insufficient scientific rigor, it is misalignment between the biological target and the therapeutic intervention. Discovery organizations have historically structured themselves around technological capabilities, operating as either small molecule, biologics, or oligonucleotide specialists. This specialization creates an underappreciated constraint: modality selection becomes influenced by internal capabilities rather than the requirements of disease biology. The tool shapes the task, when the task should shape the tool. TherML™ (Therapeutics Machine Learning) is insitro’s integrated design engine, built to reverse this logic. Spanning small molecules, oligonucleotides, and complex biologics within a single platform, TherML transitions drug discovery from stochastic screening to predictive and adaptive engineering, simultaneously optimizing for both potency and developability in silico before committing substantial resources. TherML is the therapeutic design layer of insitro’s end-to-end discovery platform, rapidly translating biological insight into optimally designed interventions. Our discovery engine draws on genetic and phenotypic data from the world’s largest biobanks, complex imaging, and large-scale genetic perturbations to map how specific changes drive cellular phenotypes. These converge in the Virtual Human™, a unified computational representation of causal biology that allows us to understand not merely that a gene associates with disease, but how its perturbation propagates through biological systems to produce pathology. Where the Virtual Human addresses what mechanism to target, TherML addresses how best to intervene. At the heart of TherML is a closed-loop active learning system of iterative data generation via differentiated experimental capabilities and AI-driven design cycles. Computational predictions guide experimental design, selecting experiments that maximize information gain. Subsequent experimental results refine the models—each cycle sharpens the next. The platform integrates directly with insitro’s automated laboratories, enabling continuous improvement in predictive performance that compounds over time. In addition, rather than optimizing one trait at a time—potency, permeability, toxicity, etc. —TherML enables integrated optimization across two critic…

insitro to Acquire CombinAbleAI, Launches TherML Platform to Unify AI-Driven Therapeutic Design Across Modalities

Mon, 12 Jan 2026 08:00:00 +0000

insitro announced it will acquire CombinAbleAI and, alongside the deal, has launched TherML (Therapeutic Machine Learning), a platform it describes as a single, modality-agnostic AI engine for drug discovery and design. The deal is expected to close in late January 2026 and is intended to expand insitro’s capabilities beyond its existing small-molecule and oligonucleotide work into antibodies and other complex biologics, creating an end-to-end system that spans target discovery through developability assessment. The company said TherML is designed to let biology guide both modality selection and molecular design, aiming to generate clinic-ready assets faster while reducing late-stage risk. The combined platform is positioned to move developability considerations—such as stability and manufacturability—earlier in the design process rather than optimizing potency first and assessing manufacturability later. For complex biologics, including multi-specific antibodies and T-cell engagers, TherML will incorporate CombinAbleAI’s physics-informed optimization engine, which insitro said has been pre-trained on more than 100,000 molecular dynamics surrogates to help predict protein structure and flexibility. For small molecules, insitro said TherML leverages its Quantitative Adaptive Libraries to map chemical space and generate local training data, while for oligonucleotides it applies AI and automation to industrialize siRNA candidate design across targets. The platform also includes machine learning models for ADMET prediction, built on insitro’s internal datasets and industry collaboration data, to optimize drug-likeness and safety alongside binding performance. insitro said TherML is integrated with its automated laboratory infrastructure to rapidly generate experimental data and continuously improve model performance based on target- and modality-specific constraints. Following the acquisition, CombinAbleAI’s team will continue operating in Israel as a new insitro R&D center focused on large-molecule design. CombinAbleAI was created and incubated within AION Labs, an Israeli venture studio formed with participation from Merck KGaA, AstraZeneca, Pfizer, Teva, Amazon Web Services, the Israel Biotech Fund, Amiti Ventures, and the Israel Innovation Authority. KEY QUOTES: “Drug discovery has traditionally optimized molecules for potency before assessing developabilit…

NVIDIA ALCHEMI Toolkit Accelerates AI-Powered Chemistry and Materials Simulations

Fri, 19 Dec 2025 08:00:00 +0000

Machine learning interatomic potentials (MLIPs) are transforming the landscape of computational chemistry and materials science. MLIPs enable atomistic simulations that combine the fidelity of computationally expensive quantum chemistry with the scaling power of AI. Yet, developers working at this intersection face a persistent challenge: a lack of robust, Pythonic toolbox for GPU-accelerated atomistic simulation. For use cases such as running a large number of simultaneous, GPU-accelerated simulations, robust and well-supported tools are either missing in the current software ecosystem or are fragmented across several open source software tools. Over the past few years, available software for running atomistic simulations with MLIPs has been CPU-centric. Core operations such as neighbor identification, dispersion corrections, long-range interactions, and their associated gradient calculation have traditionally supported only CPU computation, which often struggles to deliver the speed that contemporary research demands. High-throughput simulations of small- to medium-sized atomic systems quickly become bottlenecked by inefficient GPU usage in hybrid workflows where the model is GPU-accelerated in PyTorch but the simulation tooling is serial and CPU-based. While developers have attempted to implement these operations directly in PyTorch over the years, the general-purpose design of PyTorch leaves performance on the table for the specialized spatial and force calculation operations required in atomistic simulation. This fundamental mismatch between PyTorch capabilities and the demands of atomistic modeling raises an important question: What’s needed to bridge this gap? NVIDIA ALCHEMI (AI Lab for Chemistry and Materials Innovation), announced at Supercomputing 2024, provides chemistry and materials science developers and researchers with domain-specialized toolkits and NVIDIA NIM microservices optimized on NVIDIA accelerated computing platforms. It is a collection of high-performance, batched and GPU-accelerated tools specifically for enabling atomistic simulations in chemistry and materials science research at the machine learning framework level. NVIDIA ALCHEMI delivers capabilities across three integrated layers: - ALCHEMI Toolkit-Ops: A repository of GPU-accelerated, batched common operations for AI-enabled atomistic simulation tasks, such as neighbor list…

UW Institute for Protein Design finds better treatments for cancer and beyond with AI-enabled work

Tue, 16 Dec 2025 23:10:42 +0000

That enormous potential is what attracted Christina Savvides, ’29, to the IPD. She’s working toward a UW medical degree and a doctorate in molecular engineering, thanks in part to the Oren Traub Endowed Fellowship, which supports students enrolled in the UW School of Medicine’s Medical Scientist Training Program. “I want to be what is called a physician scientist,”says Savvides, who chose the UW for its top-notch program and collaborative research environment. “A core principle is the idea of ‘bench to bedside,’” she explains, referring to a medicine’s journey from being developed in the lab to becoming available to patients. “I want to offer something to patients who have no viable treatment options.” The protein research she’s now working on could be that hope for people with rheumatoid arthritis. Savvides describes the current treatment options for this painful autoimmune disorder as a “sledgehammer” approach. These medications are not elective for all patients—and for those who do respond, the medicines can cause collateral damage to tissue and organs. Though patients may continue treatment despite the side effects, many are unhappy with their current options. Savvides’ goal is to find the key to target just the immune system “and avoid that destruction.” The IPD has already successfully created one medicine based on computer-generated proteins: a COVID-19 vaccine authorized in the United Kingdom and South Korea. Other IPD breakthroughs that might save or improve lives include new antivenoms for snakebites, a flu shot you may need only once in your lifetime (currently in clinical testing), antibiotics that combat drug-resistant bacteria, a better vaccine to prevent malaria—the number-one killer of young children worldwide—and more effective cancer treatments. “One of the challenges in recognizing cancer cells has been they don’t look very different from normal cells,” Baker says. “We’ve recently developed a technology that can measure key markers of cancer proteins. This gives us a whole new way of targeting cancer cells.” Getting a new medical treatment from a university lab to patients is a complex process, requiring clinical trials and approvals from regulators. Spinout companies play a role by testing and commercializing new medicines so they can get to doctors and the patients who’ll benefit from them. So far, Baker has launched 21 biotech companie…

insitro Validates AI-Enabled POSH Platform in Nature Communications, Bridging Critical Gap in Drug Discovery

Tue, 16 Dec 2025 21:09:58 +0000

Share New publication demonstrates that insitro’s POSH platform — integrating pooled CRISPR screening and self-supervised deep learning — successfully breaks the historic compromise between scale and depth to more rapidly identify novel therapeutic targets. SOUTH SAN FRANCISCO, Calif. — Dec. 16, 2025 — insitro, the AI therapeutics company built on causal biology, today announced the publication of research in Nature Communications validating its POSH (Pooled Optical Screening in Human cells) platform. The study details the development of CellPaint-POSH, a scalable approach that integrates pooled CRISPR screening, high-content imaging, and advanced machine learning to map gene function at scale without relying on predefined biomarkers or human-engineered hypotheses. Across multiple large-scale experiments, the study demonstrates that biologically meaningful gene networks emerge directly from morphology-based readouts and AI-derived embeddings. By capturing the subtle, holistic changes in cells rather than isolated metrics, the platform reveals cellular relationships and therapeutic targets that conventional, hypothesis-driven analyses systematically miss. “Biology doesn’t organize itself according to the features we’ve learned to measure,” said Daphne Koller, Ph.D., founder and CEO of insitro. “insitro’s POSH platform has demonstrated that self-supervised models trained on unbiased cellular morphology can reconstruct gene function and causal relationships without being told what to look for. We’re finally able to interrogate the genome at scale while preserving the phenotypic complexity where disease mechanisms actually live.” Drug discovery has historically been constrained by a fundamental compromise: researchers were forced to choose between screening thousands of genes using low-resolution metrics (scale) or interrogating a handful of genes with deep biological insight (depth). Prioritizing scale typically meant relying on coarse, low-dimensional readouts — such as simple cell viability or single-protein expression — that inherently obscure complex biology. These “looking under the streetlight” approaches often miss the subtle, multi-factorial changes that drive disease. Conversely, while high-content phenotypes offer rich insights, generating and interpreting them at scale has been logistically prohibitive, making it impractical to map the functional ar…

The future of intelligence

Tue, 16 Dec 2025 00:00:00 +0000

Hassabis explains why DeepMind combines scaling with algorithmic innovation and treats advanced AI as both a scientific instrument and a model of intelligence.

Evaluating Music Context Preservation: A Multi-facet Framework for Music Editing Systems

Mon, 15 Dec 2025 00:00:00 +0000

Music editing is vital in modern production, yet many systems overlook preserving musical facets that should remain unchanged. The authors define this as Music Context Preservation (MuseCP) and introduce MuseCPEval, the first evaluation framework covering four categories of music facets with fine-grained metrics. Objective validation and human studies confirm effectiveness, while case studies demonstrate utility as a testbed and diagnostic tool for diverse editing systems.

Formalization of Erdős Problems

Fri, 05 Dec 2025 15:06:26 +0000

[This is a guest post by Boris Alexeev . Now over to Boris.] I’m here to tell you about various exciting developments centering on Erdős problems, especially involving the formalization of old and new mathematics using artificial intelligence. Background As is well known, Paul Erdős was a prolific mathematician of the 20th century who posed an extraordinary number of conjectures. Around May 2023 , Thomas Bloom set up erdosproblems.com to collect these problems and keep track of progress on them. Over the past 2.5 years, this progress has accelerated as many people realized they could solve problems that were previously unknown to them. In August 2025, Thomas added a forum that became active very quickly, further accelerating developments. In May 2025, Google DeepMind launched the Formal Conjectures project, an open repository of formalized mathematics conjectures, including (but not at all limited to) Erdős problems. In August 2025, Thomas Bloom and Terence Tao proposed a crowdsourced project to link up erdosproblems.com to the Online Encyclopedia of Integer Sequences (OEIS). These are both part of a greater push to increase the number of mathematical databases , as well as links between them. At present, there are over 1100 problems on erdosproblems.com , of which approximately 40% have been solved. (Note that only ~100 problems are known to have monetary prizes associated with them.) Approximately 240 problems have statements formalized in Lean, and 17 have solutions formalized in Lean. Approximately 260 problems have been linked to sequences in the OEIS. Alexis Olson has implemented a progress graph displaying these statistics visually over time. Human formalization without AI At first, formalization was not a large part of the story with Erdős problems. One of the first developments was several years ago when Thomas Bloom and Bhavik Mehta formalized Bloom’s solution to Problem 47 about unit fractions (and its further applications to several related problems). I would like to highlight a couple of passages from their paper (emphasis mine): The formalisation began in January 2022 and concluded in July 2022 . At the beginning of the formalisation, the first author had no experience with Lean at all, and learnt Lean (or at least a sufficient subset of Lean) through the formalising process. and This formalisation is a first in several respects: it is the fir…

Can AI Look at Your Retina and Diagnose Alzheimer’s? Eric Topol Hopes So

Thu, 04 Dec 2025 08:00:00 +0000

For decades now, it’s been fairly well established that once you turn 40 you should start paying more attention to your body. That’s when women are supposed to start getting mammograms and men are supposed to start paying a bit more attention to their prostates. Over the next decade, you’ll start getting colonoscopies, and from then on out, it feels like a gradual march of doctor’s appointments and tests until your body collapses sometime in your seventies or eighties. But what if modern medicine has the timeline all wrong? What if we’re testing some middle-aged people unnecessarily for diseases they’ll most likely never get, while blindly ignoring twentysomethings who might be prone to colon cancer? Is there a way that, even as we age, we can stay healthy in a way that’s both meaningful and not reliant on taking 12 horse-sized pills every morning? Eric Topol certainly thinks so. The cardiologist, vice president of Scripps Research, and author of Super Agers is convinced that new innovations in AI-assisted medicine, bioengineering, and anti-inflammatory awareness could have potential to revolutionize the way people age. During WIRED’s Big Interview event in San Francisco on Thursday, Topol told features editor Sandra Upson that while he was working on Super Agers he learned that there’s a difference between lifespan and health span and that neither has much to do with genetics. Someone who’s “wellderly,” or over 65 and generally healthy, has pretty much the same genetic makeup as someone who’s elderly and facing major health challenges, like heart disease, cancer, or a neurodegenerative disorder. Instead, Topol said, there appears to be a correlation between having a healthy immune system and aging healthily. Lifestyle can influence how healthy you are, too, with Topol advocating for eating a diet low in ultra-processed foods, focusing on sleep quality over sleep quantity, and getting out in nature. He also recommended getting exercise, focusing both on aerobic work and resistance and balance training, which can help the body become more resilient as you age. If possible, Topol said, people should avoid environmental stressors, like air pollution, micro and nano plastics, and forever chemicals, all of which Topol said are pro-inflammatory. All of these, Topol noted, are not being addressed by President Trump and health secretary Robert F. Kennedy Jr., despi…

RFdiffusion3 now available

Wed, 03 Dec 2025 17:37:33 +0000

Today we are releasing RFdiffusion3 as open-source software. This state-of-the-art AI model for biodesign is capable of generating new proteins that interact with any type of molecule commonly found inside living cells. “We built this as a general model and are sharing it freely so the scientific community can create things we haven’t even imagined yet.” Lead developer Rohith Krishna, PhD RFdiffusion3 tackles research challenges that have stymied protein engineers for decades. Building proteins that bind to specific DNA sequences — key for gene regulation and genome editing — has required researchers to specify the exact position of every atom in the molecules they wish to create. The same has been true for enzymes, which must position atoms with extreme precision to make and break chemical bonds. Prior tools for molecular design attempted to simplify important chemical details, which limited what they could do. RFdiffusion3 takes a different approach. “The central innovation here is precision control. We can now tell the model exactly which chemical interactions matter most, and it will generate new protein structures that make those interactions possible.” Lead developer Jasper Butcher This opens new possibilities for designing enzymes to break down microplastics, creating synthetic transcription factors for gene therapy, or building biosensors for environmental monitoring. When prompted with a DNA molecule to target, RFdiffusion3 generates a complete binding protein with a never-before-seen structure and sequence. [Animation: Ian C. Haydon, UW Institute for Protein Design] Key improvements As detailed in a recent preprint , the team created RFdiffusion3 using the latest advances in efficient transformer architectures. The tool shares no code with its predecessors RFdiffusion or RFdiffusion2 . RFdiffusion3 delivers several key innovations: Efficiency and performance: RFdiffusion3 offers ten-fold faster performance over RFdiffusion2 developed earlier this year. In computational benchmarks, it matched or outperformed prior tools on a wide range of protein-protein binding, protein-DNA binding, protein-small molecule binding, and enzyme design tasks. Atom-level diffusion: The model treats individual atoms as the fundamental units being designed, applying a deep learning technique called diffusion to rapidly create new atomic arrangements. This produces intric…

RFdiffusion2: Efficient Enzymes from Prompts

Wed, 03 Dec 2025 17:36:38 +0000

Today we report the development of AI-generated protein catalysts that function efficiently immediately upon synthesis, bypassing the need for lab optimization that has historically bottlenecked enzyme development. While prior protein design software yielded enzymes with only trace levels of activity, RFdiffusion2 delivers catalytic proficiencies — a rigorous measure of how well an enzyme captures and converts a molecule — comparable to enzymes found in cells. “Deep learning has evolved from predicting protein structures to creating functional systems. This technology allows us to build enzymes around the specific chemistry we want to see happen, opening the door to advanced medicines and materials that nature has not had time to explore.” IPD director David Baker, PhD RFdiffusion2 is freely available on GitHub , allowing all scientists to use and refine this tool. Publications Computational design of metallohydrolases Published in : Nature [ PDF ] Authors : Donghyo Kim , Seth M. Woodbury , Woody Ahern , Doug Tischer, Alex Kang, Emily Joyce, Asim K. Bera, Nikita Hanikel, Saman Salike, Rohith Krishna, Jason Yim, Samuel J. Pellock, Anna Lauko, Indrek Kalvet, Donald Hilvert, David Baker Computational design of metallohydrolases Published in : Nature Methods [ PDF ] Authors : Woody Ahern , Jason Yim , Doug Tischer , Saman Salike , Seth M. Woodbury, Donghyo Kim, Indrek Kalvet, Yakov Kipnis , Brian Coventry, Han Raut Altae-Tran, Magnus S. Bauer , Regina Barzilay, Tommi S. Jaakkola, Rohith Krishna, David Baker Lead Authors Donghyo Kim, PhD Seth M. Woodbury Woody Ahern, PhD Jason Yim, PhD Doug Tischer, PhD Coverage Protein design takes big leap forward as model produces enzymes almost as effective as nature’s — Chemistry World A trio of AI methods tackles enzyme design — C&EN UW Nobel winner’s lab releases most powerful protein design tool yet — GeekWire Driving the chemistry of life Enzymes are the manufacturing machinery of life. Some press atoms together to create larger molecules; others act like nutcrackers, breaking molecules down into smaller components. For decades, scientists have sought to build custom enzymes for new chemical transformations, such as breaking down plastic pollution. However, efficient catalysts has proven exceptionally difficult to design. Like a nutcracker that must fit a specific shell perfectly to crack it, an enzyme must align with i…

Early Science Acceleration Experiments with GPT-5

Thu, 20 Nov 2025 00:00:00 +0000

Fourteen researchers report eleven case studies across mathematics, physics, astronomy, biology and materials, documenting both concrete advances and failure modes.

Google DeepMind and Fields Medalist Terence Tao Detail AI-Powered Mathematical Discoveries with AlphaEvolve

Fri, 07 Nov 2025 08:00:00 +0000

Google DeepMind’s AlphaEvolve AI is accelerating mathematical research at an unprecedented scale, according to a new paper published this week by collaborators including renowned mathematician Terence Tao. The research shows how the AI agent tackled 67 challenging problems, rediscovering top solutions and finding novel constructions for several long-standing challenges. The work demonstrates a powerful new method for human-AI collaboration in pure mathematics. It uses AI’s ability to search vast problem spaces to generate insights that complement human intuition, potentially speeding up the path to solving famously difficult conjectures. An Evolutionary Engine for Mathematical Discovery Operating differently from general-purpose chatbots that often struggle with logical rigor, AlphaEvolve uses a structured evolutionary framework. It functions as a “generic evolutionary coding agent,” employing large language models like Gemini to propose, test, and iteratively refine algorithmic solutions. This work builds on DeepMind’s initial unveiling of the tool in May 2025. According to the research paper, “…AlphaEvolve is a powerful new tool for mathematical discovery, capable of exploring vast search spaces to solve complex optimization problems at scale.” In a detailed blog post, Tao explained the AI’s core method involves evolving Python code that searches for a solution, rather than creating the mathematical object directly. This “search mode” allows a single, slow LLM call to trigger a massive, cheap computation as the generated search heuristic explores millions of possibilities on its own. A contrasting “generalizer mode” tasks the AI with finding formulas that work for any given number, aiming for broader applicability. Getting started on new lines of inquiry is remarkably efficient with this process. The researchers highlight that for many of the problems they explored, “…on average the usual preparation time for the setup of a problem using AlphaEvolve took only up to a few hours.” Such rapid setup allows mathematicians to systematically investigate large classes of problems that would otherwise require extensive, bespoke computational work. From Moving Sofas to Kakeya Sets: AI Tackles Open Problems While the system successfully rediscovered known solutions for most of the 67 problems, its most significant contributions came from finding novel approaches. Th…

Teaching AI to build antibodies from scratch

Wed, 05 Nov 2025 20:50:17 +0000

A new study led by the Baker Lab shows for the first time that functional, full-length antibodies can be developed entirely on computers — no animal immunizations or extensive screening required. This breakthrough could reshape the $200 billion antibody drug industry and open pathways to treatments for diseases currently considered undruggable. The work builds on a preprint we posted in March 2024 showing AI could generate functional nanobody fragments. Today’s publication marks a significant advance: it is now possible to design complete antibody variable regions containing both heavy and light chains — the molecular architectures used in most current antibody drugs. “AI antibody design will transform the biotechnology and pharmaceutical industries, enabling precise targeting and simpler drug development.” David Baker, PhD, senior author and director of the Institute for Protein Design We’re sharing the antibody design software RFantibody freely for academic, personal, and commercial use, enabling laboratories worldwide to apply this technology. The code is available on GitHub . Publications Atomically accurate de novo design of antibodies with RFdiffusion Published in : Nature [ PDF ] Authors : Nathaniel R. Bennett , Joseph L. Watson , Robert J. Ragotte , Andrew J. Borst , DéJenaé L. See , Connor Weidle , Riti Biswas , Yutong Yu , Ellen L. Shrock, Russell Ault, Philip J. Y. Leung, Buwei Huang, Inna Goreshnik, John Tam, Kenneth D. Carr, Benedikt Singer, Cameron Criswell, Basile I. M. Wicky, Dionne Vafeados, Mariana Garcia Sanchez, Ho Min Kim, Susana Vázquez Torres, Sidney Chan, Shirley M. Sun, Timothy T. Spear, Yi Sun, Keelan O’Reilly, John M. Maris, Nikolaos G. Sgourakis, Roman A. Melnyk, Chang C. Liu, David Baker Lead Authors Nate Bennett, PhD Joseph Watson, PhD Rob Ragotte, PhD Andrew Borst, PhD DéJenaé See Connor Weidle Riti Biswas Yutong Yu (UC Irvine) Coverage AI-designed antibodies promise big boost to drug development — Financial Times ‘Not the end of the story:’ Nobel laureate David Baker on what’s next for de novo design after RFantibody lands in Nature — Endpoints News AI-Designed Antibodies Achieve Atomic Precision to Enhance Drug Discovery — GEN Academic AIs make inroads in protein binder design — C&EN AI tuned for antibody design Antibodies are proteins that recognize and bind to specific sites on target molecules, triggering protective respo…

Formal or not formal? That is the question in AI for theorem proving.

Wed, 22 Oct 2025 14:41:23 +0000

So it’s an interesting time for computers-doing-mathematics. A couple of interesting things happened in the last few days, which have inspired me to write about the question more broadly. First there is the question on whether computers will ever prove interesting research-level mathematical theorems autonomously at all, which is really hard to answer right now, although clearly a lot of people are betting on “yes”. I personally am unsure: I’ve seen what can currently be done, and can’t quite extrapolate that far once you cut through the hype. I am however far more optimistic about AI tools becoming useful helpers to mathematics researchers, and this is one of the things which motivates my current work (trying to teach Lean a lot of research-level definitions, theorem statements and proofs ). But whether they beat us or just help us, one can still ask what an autonomous theorem-proving tool will look like. Let’s initially take a gigantic step down from research level mathematics and talk instead about problems which can be solved by smart schoolchildren, namely those on the International Mathematics Olympiad (IMO). I wrote recently about the attempts by AI on the 2025 IMO but all you need to know here is that there were four attempts which were made public: two of them were using language models to write proofs in English (which were then graded by humans); let’s call this the “informal approach”. The other two were using a language model/Lean hybrid set-up; let’s call this the “formal approach”. Lean is a computer proof checker or interactive theorem prover; it checks mathematical arguments at a superhuman level. In this hybrid setup, the language model writes proofs in Lean’s language which are then checked automatically by Lean; once Lean says the proofs are correct, humans only need to check that the statements have been translated correctly into Lean, although that is an important part of the process. Worries about the non-formal approach. What worries me mostly about the non-formal approach is the horror stories I am hearing from journal editors. Mathematics journals are being spammed with LLM-generated papers, which are becoming more and more plausible-looking, and sometimes the use of LLMs is not even revealed in the paper. I want to flag some differences between mathematics papers generated by LLMs and those generated by humans: 1. LLMs are sycopha…

David Baker on Hype vs. Reality in de Novo Protein Design

Mon, 20 Oct 2025 07:00:00 +0000

SEATTLE — In celebration of Nobel Prize announcement week, Fay Lin, PhD, Senior Editor, Technology at GEN, sat down with Nobel Laureate, David Baker, PhD, to unpack the hype vs. reality surrounding designing proteins from scratch (de novo) and explore what’s needed for the AI revolution to truly transform medicine. Baker, who is also the director of the Institute for Protein Design (IPD) at the University of Washington (UW), won a share of the 2024 Nobel Prize in Chemistry for pioneering AI methods to design novel proteins for powerful applications across pharmaceuticals, vaccines, nanomaterials, biosensors, and more. Seattle’s reputation as a science and technology powerhouse remains strong. Mary Brunkow, PhD, researcher at the Seattle-based Institute for Systems Biology (ISB), was recently awarded the 2025 Nobel Prize in Physiology or Medicine for uncovering the role of regulatory T cells (Tregs) in peripheral immune tolerance. *David Baker, PhD, gave the inaugural keynote at GEN‘s virtual event, “The State of AI in Drug Discovery 2024.” Make sure to register for the 2025 event streaming on October 29.

Pranam Chatterjee Designs Novel AI Frameworks for Biotechnology

Thu, 18 Sep 2025 07:00:00 +0000

Pranam Chatterjee Designs Novel AI Frameworks for Biotechnology In the News / September 18, 2025 When he enrolled at Dartmouth, Pranam Chatterjee, Assistant Professor and Africk-Lesley Distinguished Scholar of Innovation in Engineering with a joint appointment in Bioengineering (BE) and in Computer and Information Science (CIS), did not start off as an engineering student. He majored in religion. “I had a deep desire to understand the people around me,” says Chatterjee, who grew up in a household of scientists on the Alabama-Georgia border. But after transferring to MIT, where he returned to his science roots and studied biology and computer science, Chatterjee brought the unique perspective of the humanities with him. “Studying religion taught me to be a people-first engineer,” he says. “No matter how powerful your technology is, if it doesn’t fit into the way people see the world, it won’t have the impact you want.” From the Humanities to AI for Biotech Chatterjee quickly turned to artificial intelligence (AI) as his tool of choice in the engineering world, asking himself the question,”How can AI be used first and foremost to solve the most pressing problems facing humanity?” The pursuit of the answer to this question cleared a path that would lead him to the forefront of AI for biotechnology. “Biotechnology has the potential to solve so many of the world’s most pressing problems, from health and medicine to energy, agriculture, food security and the environment,” Chatterjee says. “When designed well, AI can make sense of biological complexity and design solutions at scale.” Today, Chatterjee is a member of the Center for Precision Engineering for Health, where his lab develops generative AI models to identify and design molecular therapeutics that have the potential to treat diseases considered “undruggable,” like pediatric cancers, which are caused by unstable, malfunctioning proteins. These diseases are hard to target and synthesize, making the endeavor challenging. But Chatterjee saw the problem and turned it into an opportunity that AI could help solve. Generative AI for Biology’s Edge Cases In his Penn Engineering lab, the workflow is strikingly direct. Publicly available biological data — often from patient samples or past clinical trials — feeds into advanced generative algorithms. These models don’t just predict properties of molecules; they crea…

HIP: Hessian Interatomic Potentials without derivatives

Mon, 15 Sep 2025 00:00:00 +0000

Molecular Hessians, the second derivatives of the potential energy, are fundamental to many workflows in computational chemistry. Usually, accurate Hessians are computationally expensive to calculate and scale poorly with system size, whether computed using quantum chemistry methods or machine-learning interatomic potentials (MLIPs). In this work, we introduce Hessian interatomic potentials (HIPs), a deep learning model that directly predicts Hessians without relying on automatic differentiation or finite differences. To do so, we construct SE(3)-equivariant, symmetric Hessians from irreducible representation (irrep) features up to degree $l$=2, computed by a graph neural network. HIP Hessians are one to two orders of magnitude faster, more accurate, more memory efficient, easier to train, and exhibit more favourable scaling with system size. We validate our predictions across a wide range of downstream tasks, demonstrating consistently superior performance in transition state search, geometry optimization, zero-point energy corrections, and vibrational analysis. We open-source the HIP code and model weights.

MIT Media Lab Alum Sam Rodriques Named to TIME100 AI 2025 List

Thu, 11 Sep 2025 07:00:00 +0000

Sam Rodriques, an alum of the Synthetic Neurobiology research group, has been recognized in the Innovators category of the TIME100 AI 2025 list. Dr. Rodriques co-founded the nonprofit FutureHouse in 2023, with the aim of giving scientists new tools to break through bottlenecks and improve their workflows. To that end, FutureHouse is developing AI agents that can formulate hypotheses, conduct experiments, and analyze data by themselves. In May, the organization announced that its agents had helped to discover a promising new treatment for dry age-related macular disease (AMD), a major cause of blindness; the paper, which is available as a pre-print, has not yet been peer-reviewed. “It’s like being a professor today: you have a bunch of graduate students working with you," Dr. Rodriques told TIME. "They go do some work, come back, you provide them updates. I think it will be like that, just much faster, where every scientist will have their own lab of agents.”

Andrew White on Building an AI Scientist to Automate Discovery

Wed, 10 Sep 2025 00:00:00 +0000

FutureHouse's head of science explains its agent stack, a drug-repurposing case, and why scientific publishing and evaluation must be redesigned for machine readers.

Sam Rodriques: The 100 Most Influential People in AI 2025 - Time Magazine

Tue, 26 Aug 2025 07:00:00 +0000

A theoretical physicist by training, Sam Rodriques recognized the possible uses of AI for scientific discovery. In 2023 he co-founded the nonprofit FutureHouse, with a goal to build AI scientists that can formulate hypotheses, conduct experiments, and analyze data by themselves. While AI science is an increasingly crowded field, FutureHouse’s audacious vision and scientific bonafides have earned it the backing of Eric Schmidt and other funders. This year FutureHouse released a set of AI tools for scientific discovery, which can search scientific literature and databases and help plan experiments. And in May, the organization said its AI agents made their first major discovery: a promising new treatment for dry age-related macular disease (AMD), a major cause of blindness, though the study has not been peer reviewed yet. While human scientists conducted the physical experiments in the lab and wrote the final manuscript, AI agents took care of everything else. Rodriques contends there are many more such discoveries on the way. It was while pursuing his PhD in neuroscience at the Massachusetts Institute of Technology, Rodriques says, that he realized advancements in the field were limited by humans’ sheer constraints in reading, processing, and cross-referencing massive amounts of scientific literature. AI, he argues, can perform that part of the scientific process more swiftly. “It’s like being a professor today: you have a bunch of graduate students working with you. They go do some work, come back, you provide them updates,” he says. “I think it will be like that, just much faster, where every scientist will have their own lab of agents.” Rodriques says that since May, FutureHouse’s tools have been looking into breast cancer and autism mutations. “In the next two to three years, we are just going to see an exponential increase in the rate at which those connections can be made,” he says. With the acceleration AI can offer, he adds, long-held scientific mysteries could be solved in short order.