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01Allen Institute for AI · AI for Science
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…
arXiv Quantitative Methods
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.
Nature Portfolio · Nature
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…
Hugging Face Daily Papers
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.
Hugging Face Daily Papers
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.
