Scientific Foundation Models

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 kn

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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 i

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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 fr

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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 t

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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 found

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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 f

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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

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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 demonstratio

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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 ext

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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

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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

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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 subsequentl

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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" exhi

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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

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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 te

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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

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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.

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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

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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

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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 te

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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

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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 alre

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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,

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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

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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 visu

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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 benc

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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 analyt

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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 P

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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 auton

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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 n

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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 tar

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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 draini

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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

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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-specif

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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 a

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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

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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.

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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 sen

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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]

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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

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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 stat

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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 Om

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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. W

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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-ce

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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

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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 ge

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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 extensiv

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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 b

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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 >=

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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 multimoda

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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 ha

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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

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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 la

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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

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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.

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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

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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 sub

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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-t

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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

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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 est

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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

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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, a

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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 th

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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 finetun

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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-

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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 mo

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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

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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

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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 re

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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

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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, a

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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 V

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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 thro

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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

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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 t

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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

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AI-guided protein design was experimentally tested as a way to improve the efficiency and targeting range of compact base editors.

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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 hi

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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-mat

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AlphaGEM uses structural information and deep learning to fill gaps in genome-scale metabolic models.

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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 s

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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 th

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A generative system performs inverse design for information metamaterials, linking learned representations to physical objects.

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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 performan

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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

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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 bu

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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. S

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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

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NicheTrans uses spatial context to translate between omics modalities and preserve tissue heterogeneity.

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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 smel

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A cross-species foundation model creates a shared cell representation that supports zero-shot transfer across biological datasets.

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NEP89 unifies inorganic and organic systems while targeting simulation speeds orders of magnitude beyond first-principles methods.

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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 burgeon

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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 spe

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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 publishe

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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 wid

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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 m

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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 Bri

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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 stra

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Demis Hassabis traces how AlphaGo's search and learning ideas fed into AlphaFold, AlphaProof, AlphaEvolve and the broader scientific-AI program.

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Hassabis explains why DeepMind combines scaling with algorithmic innovation and treats advanced AI as both a scientific instrument and a model of intelligence.

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