AI Agents for Scientific Discovery

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Overview

AI agents for scientific discovery aim to help researchers move from questions and hypotheses to analysis, experiments, and evidence-backed conclusions. Common approaches combine language models with literature retrieval, code execution, scientific simulators, laboratory instruments, and specialized agents in iterative hypothesis–test–revision loops. Work spans fields such as materials science, biology, physics, chemistry, and mathematics, with systems also developing reusable skills and memories to build on prior results. A central challenge is ensuring that promising outputs are genuinely novel, scientifically valid, reproducible, and grounded in evidence, especially in long workflows and physical experiments.

Latest papers

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292 papers57 papers in the last four weeks, up 111% on the four weeks before. 0.3% of all new papers.

Apr 14, 2026

Agentic Discovery with Active Hypothesis Exploration for Visual Recognition

We introduce HypoExplore, an agentic framework that formulates neural architecture discovery for visual recognition as a hypothesis-driven scientific inquiry. Given a human-specified high-level research direction, HypoExplore ideates, implements, evaluates, and improves neural architectures through evolutionary branching. New hypotheses are created using a large language model by selecting a parent hypothesis to build upon, guided by a dual strategy that balances exploiting validated principles with resolving uncertain ones. Our proposed framework maintains a Trajectory Tree that records the lineage of all proposed architectures, and a Hypothesis Memory Bank that actively tracks confidence scores acquired through experimental evidence. After each experiment, multiple feedback agents analyze the results from different perspectives and consolidate their findings into hypothesis confidence updates. Our framework is tested on discovering lightweight vision architectures on CIFAR-10, with the best achieving 94.11% accuracy evolved from a root node baseline that starts at 18.91%, and generalizes to CIFAR-100 and Tiny-ImageNet. We further demonstrate applicability to a specialized domain by conducting independent architecture discovery runs on MedMNIST, which yield a state-of-the-art performance. We show that hypothesis confidence scores grow increasingly predictive as evidence accumulates, and that the learned principles transfer across independent evolutionary lineages, suggesting that HypoExplore not only discovers stronger architectures, but can help build a genuine understanding of the design space.

Jaywon Koo, Jefferson Hernandez, Ruozhen He +3

Rice University

Neural Architecture SearchAI Agents for Scientific DiscoveryLarge Language Model-Guided NAS
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Apr 13, 2026

VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing

Scientific research based on multimodal clinical data (including medical imaging) requires coordinating clinical, radiological, programming, and biostatistical expertise, a fragmented process that bottlenecks discovery. We present VERITAS (Verifiable Epistemic Reasoning for Image-Derived Hypothesis Testing via Agentic Systems), a clinical co-scientist: a multi-agent system that autonomously tests natural-language hypotheses and produces a fully auditable evidence trail, tracing every conclusion through executable outputs from analysis plan to segmentation masks to statistical code to final verdict. Unlike prior AI-scientist systems, which mainly operate on tabular or text data, VERITAS grounds autonomous discovery directly in medical images. It decomposes the workflow into four phases handled by role-specialized agents, and introduces an epistemic evidence label framework that mechanically classifies outcomes as Supported, Refuted, Underpowered, or Invalid by jointly evaluating significance, effect direction, and study power. This distinction is critical in medical imaging, where non-significant results often reflect insufficient sample size rather than absent effects. We construct a tiered benchmark of 64 hypotheses spanning six complexity levels across cardiac and brain glioma MRI datasets. VERITAS reaches 81.4% verdict accuracy with frontier models and 71.2% with locally-hosted open-weight models (8-30B), outperforming all single-model baselines in both classes. It also produces the highest rate of independently verifiable statistical outputs (86.6%), so even its failures remain diagnosable through artifact inspection. Structured multi-agent decomposition thus substitutes for model scale while preserving the verifiability that scientific discovery demands. We release code, hypothesis bank, and evaluation pipeline at https://github.com/LucZot/veritas.

Lucas Stoffl, Benedikt Wiestler, Johannes C. Paetzold

Department of Radiology, Weill Cornell Medicine, New York, USA · Cornell Tech, New York, USA · TUM University Hospital, Munich, Germany +2

AI Agents for Scientific DiscoveryHealthcareMulti-Agent SystemsMedical Image BenchmarksScientific Claim Verification
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Apr 8, 2026

Prism: An Evolutionary Memory Substrate for Multi-Agent Open-Ended Discovery

We introduce \prism{} (Probabilistic Retrieval with Information-Stratified Memory), an evolutionary memory substrate for multi-agent AI systems engaged in open-ended discovery. \prism{} unifies four independently developed paradigms -- layered file-based persistence, vector-augmented semantic memory, graph-structured relational memory, and multi-agent evolutionary search -- under a single decision-theoretic framework with eight interconnected subsystems. We make five contributions: (1)~an entropy-gated stratification mechanism that assigns memories to a tri-partite hub (skills/notes/attempts) based on Shannon information content, with formal context-window utilization bounds; (2)~a causal memory graph G=(V,Er,Ec)\mathcal{G} = (V, E_r, E_c) with interventional edges and agent-attributed provenance; (3)~a Value-of-Information retrieval policy with self-evolving strategy selection; (4)~a heartbeat-driven consolidation controller with stagnation detection via optimal stopping theory; and (5)~a replicator-decay dynamics framework that interprets memory confidence as evolutionary fitness, proving convergence to an Evolutionary Stable Memory Set (ESMS). On the LOCOMO benchmark, \prism{} achieves 88.1 LLM-as-a-Judge score (31.2% over Mem0). On CORAL-style evolutionary optimization tasks, 4-agent \prism{} achieves 2.8×\times higher improvement rate than single-agent baselines.%

Suyash Mishra

AI Researcher · Basel, Switzerland

Agent Memory ManagementGraph-Based Agent MemoryShared-Memory Multi-Agent SystemsAI Agents for Scientific DiscoverySelf-Evolving Agents
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Apr 7, 2026

Artificial Intelligence and the Structure of Mathematics

Recent progress in artificial intelligence (AI) is unlocking transformative capabilities for mathematics. There is great hope that AI will help solve major open problems and autonomously discover new mathematical concepts. In this essay, we further consider how AI may open a grand perspective on mathematics by forging a new route, complementary to mathematicallogic, to understanding the global structure of formal proof****s. We begin by providing a sketch of the formal structure of mathematics in terms of universal proof and structural hypergraphs and discuss questions this raises about the foundational structure of mathematics. We then outline the main ingredients and provide a set of criteria to be satisfied for AI models capable of automated mathematical discovery. As we send AI agents to traverse Platonic mathematical worlds, we expect they will teach us about the nature of mathematics: both as a whole, and the small ribbons conducive to human understanding. Perhaps they will shed light on the old question: "Is mathematics discovered or invented?" Can we grok the terrain of these Platonic worlds?

Maissam Barkeshli, Michael R. Douglas, Michael H. Freedman

Meta Superintelligence Labs, Fundamental AI Research · Department of Physics and Joint Quantum Institute, University of Maryland, College Park · Center for Mathematical Sciences and Applications, Harvard University +1

AI Agents for Scientific DiscoveryMathematical Reasoning
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Apr 7, 2026

EpiBench: Benchmarking Multi-turn Research Workflows for Multimodal Agents

Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across papers to align experimental settings and support reproducible conclusions. This joint capability is not systematically assessed in existing benchmarks, which largely under-evaluate proactive search, multi-evidence integration and sustained evidence use over time. In this work, we introduce EpiBench, an episodic multi-turn multimodal benchmark that instantiates short research workflows. Given a research task, agents must navigate across papers over multiple turns, align evidence from figures and tables, and use the accumulated evidence in the memory to answer objective questions that require cross paper comparisons and multi-figure integration. EpiBench introduces a process-level evaluation framework for fine-grained testing and diagnosis of research agents. Our experiments show that even the leading model achieves an accuracy of only 29.23% on the hard split, indicating substantial room for improvement in multi-turn, multi-evidence research workflows, providing an evaluation platform for verifiable and reproducible research agents.

Xuan Dong, Huanyang Zheng, Tianhao Niu +7

Harbin Institute of Technology

AI Agent BenchmarksAI Agents for Scientific DiscoveryEvidence RetrievalMultimodal Reasoning
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Apr 5, 2026

SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources

Modern scientific ecosystems are rich in procedural knowledge across repositories, APIs, scripts, notebooks, documentation, databases, and papers, yet much of this knowledge remains fragmented across heterogeneous artifacts that agents cannot readily operationalize. This gap between abundant scientific know-how and usable agent capabilities is a key bottleneck for building effective scientific agents. We present SkillFoundry, a self-evolving framework that converts such resources into validated agent skills, reusable packages that encode task scope, inputs and outputs, execution steps, environment assumptions, provenance, and tests. SkillFoundry organizes a target domain as a domain knowledge tree, mines resources from high-value branches, extracts operational contracts, compiles them into executable skill packages, and then iteratively expands, repairs, merges, or prunes the resulting library through a closed-loop validation process. SkillFoundry produces a substantially novel and internally valid skill library, with 71.1% of mined skills differing from existing skill libraries such as SkillHub and SkillSMP. We demonstrate that these mined skills improve coding agent performance on five of the six MoSciBench datasets. We further show that SkillFoundry can design new task-specific skills on demand for concrete scientific objectives, and that the resulting skills substantially improve performance on two challenging genomics tasks: cell type annotation and the scDRS workflow. Together, these results show that automatically mined skills improve agent performance on benchmarks and domain-specific tasks, expand coverage beyond hand-crafted skill libraries, and provide a practical foundation for more capable scientific agents.

Shuaike Shen, Wenduo Cheng, Mingqian Ma +3

Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University · Machine Learning Department, School of Computer Science, Carnegie Mellon University

Agent Skill LearningAI Agents for Scientific DiscoveryLLM Agent Self-Improvement
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Apr 1, 2026

CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery

Scientific algorithm discovery is iterative: hypotheses are proposed, implemented, stress-tested, and revised. Current LLM-guided search systems accelerate proposal generation, but often under-represent scientific structure by optimizing code-only artifacts with weak correctness/originality gating. We present CliffSearch, an agentic evolutionary framework in which the core evolution operators (pair selection, crossover, mutation, and review) are implemented as LLM agents, and the loop is designed around three principles: (1) each node is a structured scientific artifact, instantiated in either theory+code or code_only mode, (2) reviewer judgments of correctness and originality are first-class selection gates alongside optimization of the benchmark metric of interest, and (3) mutation is split into exploration and correction pathways with distinct objectives. Exploration mutation imports ideas from adjacent scientific domains to increase novelty, while correction mutation performs targeted evidence-guided repair using reviewer signals over theory, code, benchmark results, and runtime errors. We illustrate the framework on three benchmark-grounded studies: transformer hyper-connection evolution, optimizer discovery on a fixed nanoGPT stack, and a smaller native-optimizer ablation. Across these settings, the same loop supports explicit metric direction, reproducible persistence, and reviewer-gated comparison of discoveries under controlled search conditions. The result is a discovery workflow that prioritizes scientific interpretability and correctness while optimizing task metrics under controlled novelty constraints, rather than maximizing candidate throughput alone. Full run artifacts, interactive visualizations, and exported best nodes for the reported studies are available at https://cliffsearch.ai .

Youssef Mroueh, Carlos Fonseca, Brian Belgodere +1

IBM Research

LLMs for Evolutionary OptimizationAutomated Algorithm DiscoveryAI Agents for Scientific DiscoveryLarge Language Model-Guided OptimizationEvolutionary Optimization
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Apr 1, 2026

BloClaw: An Omniscient, Multi-Modal Agentic Workspace for Next-Generation Scientific Discovery

The integration of Large Language Models (LLMs) into life sciences has catalyzed the development of "AI Scientists." However, translating these theoretical capabilities into deployment-ready research environments exposes profound infrastructural vulnerabilities. Current frameworks are bottlenecked by fragile JSON-based tool-calling protocols, easily disrupted execution sandboxes that lose graphical outputs, and rigid conversational interfaces inherently ill-suited for high-dimensional scientific data.We introduce BloClaw, a unified, multi-modal operating system designed for Artificial Intelligence for Science (AI4S). BloClaw reconstructs the Agent-Computer Interaction (ACI) paradigm through three architectural innovations: (1) An XML-Regex Dual-Track Routing Protocol that statistically eliminates serialization failures (0.2% error rate vs. 17.6% in JSON); (2) A Runtime State Interception Sandbox that utilizes Python monkey-patching to autonomously capture and compile dynamic data visualizations (Plotly/Matplotlib), circumventing browser CORS policies; and (3) A State-Driven Dynamic Viewport UI that morphs seamlessly between a minimalist command deck and an interactive spatial rendering engine. We comprehensively benchmark BloClaw across cheminformatics (RDKit), de novo 3D protein folding via ESMFold, molecular docking, and autonomous Retrieval-Augmented Generation (RAG), establishing a highly robust, self-evolving paradigm for computational research assistants. The open-source repository is available at https://github.com/qinheming/BloClaw.

Yao Qin, Yangyang Yan, Jinhua Pang +1

AI Innovation Department, Beijing 1st Biotech Group Co., Ltd. · Diplomatic Negotiation Simulation and Data Lab · First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, China

AI Agents for Scientific DiscoveryAI for ScienceMultimodal AgentsScientific Workflow Automation
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Mar 30, 2026

The Future of AI is Many, Not One

The way we're thinking about generative AI right now is fundamentally individual. We see this not just in how users interact with models but also in how models are built, how they're benchmarked, and how commercial and research strategies using AI are defined. We argue that we should abandon this approach if we're hoping for AI to support groundbreaking innovation and scientific discovery. Drawing on research and formal results in complex systems, organizational behavior, and philosophy of science, we show why we should expect deep intellectual breakthroughs to come from epistemically diverse groups of AI agents working together rather than singular superintelligent agents. Having a diverse team broadens the search for solutions, delays premature consensus, and allows for the pursuit of unconventional approaches. Developing diverse AI teams also addresses AI critics' concerns that current models are constrained by past data and lack the creative insight required for innovation. The upshot, we argue, is that the future of transformative transformer-based AI is fundamentally many, not one.

Daniel J. Singer, Luca Garzino Demo

University of Pennsylvania, Department of Philosophy and Wharton Legal Studies & Business Ethics · University of Pennsylvania, Department of Philosophy

AI Agents for Scientific DiscoveryMulti-Agent LLM SystemsMulti-Agent Systems
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Mar 30, 2026

Towards a Medical AI Scientist

Autonomous systems that generate scientific hypotheses, conduct experiments, and draft manuscripts have recently emerged as a promising paradigm for accelerating discovery. However, existing AI Scientists remain largely domain-agnostic, limiting their applicability to clinical medicine, where research is required to be grounded in medical evidence with specialized data modalities. In this work, we introduce Medical AI Scientist, the first autonomous research framework tailored to clinical autonomous research. It enables clinically grounded ideation by transforming extensively surveyed literature into actionable evidence through clinician-engineer co-reasoning mechanism, which improves the traceability of generated research ideas. It further facilitates evidence-grounded manuscript drafting guided by structured medical compositional conventions and ethical policies. The framework operates under 3 research modes, namely paper-based reproduction, literature-inspired innovation, and task-driven exploration, each corresponding to a distinct level of automated scientific inquiry with progressively increasing autonomy. Comprehensive evaluations by both large language models and human experts demonstrate that the ideas generated by the Medical AI Scientist are of substantially higher quality than those produced by commercial LLMs across 171 cases, 19 clinical tasks, and 6 data modalities. Meanwhile, our system achieves strong alignment between the proposed method and its implementation, while also demonstrating significantly higher success rates in executable experiments. Double-blind evaluations by human experts and the Stanford Agentic Reviewer suggest that the generated manuscripts approach MICCAI-level quality, while consistently surpassing those from ISBI and BIBM. The proposed Medical AI Scientist highlights the potential of leveraging AI for autonomous scientific discovery in healthcare.

Hongtao Wu, Boyun Zheng, Dingjie Song +5

The Chinese University of Hong Kong · Lehigh University · Microsoft Research +1

AI Agents for Scientific DiscoveryScientific Hypothesis GenerationEvidence-Grounded Generation
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Mar 30, 2026

Deep Research of Deep Research: From Transformer to Agent, From AI to AI for Science

With the advancement of large language models (LLMs) in their knowledge base and reasoning capabilities, their interactive modalities have evolved from pure text to multimodality and further to agentic tool use. Consequently, their applications have broadened from question answering to AI assistants and now to general-purpose agents. Deep research (DR) represents a prototypical vertical application for general-purpose agents, which represents an ideal approach for intelligent information processing and assisting humans in discovering and solving problems, with the goal of reaching or even surpassing the level of top human scientists. This paper provides a deep research of deep research. We articulate a clear and precise definition of deep research and unify perspectives from industry's deep research and academia's AI for Science (AI4S) within a developmental framework. We position LLMs and Stable Diffusion as the twin pillars of generative AI, and lay out a roadmap evolving from the Transformer to agents. We examine the progress of AI4S across various disciplines. We identify the predominant paradigms of human-AI interaction and prevailing system architectures, and discuss the major challenges and fundamental research issues that remain. AI supports scientific innovation, and science also can contribute to AI growth (Science for AI, S4AI). We hope this paper can help bridge the gap between the AI and AI4S communities.

Yipeng Yu

Deep Research AgentsAI Agents for Scientific DiscoveryAI for ScienceLarge Language Model-Guided Scientific Discovery
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Mar 29, 2026

TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science

Artificial intelligence (AI) has achieved breakthroughs comparable to traditional numerical models in data-driven weather forecasting, yet it remains essentially statistical fitting and struggles to uncover the physical causal mechanisms of the atmosphere. Physics-oriented mechanism research still heavily relies on domain knowledge and cumbersome engineering operations of human scientists, becoming a bottleneck restricting the efficiency of Earth system science exploration. Here, we propose TianJi - the first "AI meteorologist" system capable of autonomously driving complex numerical models to verify physical mechanisms. Powered by a large language model-driven multi-agent architecture, TianJi can autonomously conduct literature research and generate scientific hypotheses. We further decouple scientific research into cognitive planning and engineering execution: the meta-planner interprets hypotheses and devises experimental roadmaps, while a cohort of specialized worker agents collaboratively complete data preparation, model configuration, and multi-dimensional result analysis. In two classic atmospheric dynamic scenarios (squall-line cold pools and typhoon track deflections), TianJi accomplishes expert-level end-to-end experimental operations with zero human intervention, compressing the research cycle to a few hours. It also delivers detailed result analyses and autonomously judges and explains the validity of the hypotheses from outputs. TianJi reveals that the role of AI in Earth system science is transitioning from a "black-box predictor" to an "interpretable scientific collaborator", offering a new paradigm for high-throughput exploration of scientific mechanisms.

Kaikai Zhang, Xiang Wang, Haoluo Zhao +4

School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China · State Key Laboratory of Climate System Prediction and Risk Management (CPRM), Nanjing University of Information Science and Technology, Nanjing, 210044, China · College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong, China

AI Agents for Scientific DiscoveryAutonomous Scientific DiscoveryAutonomous Research Agents
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Mar 27, 2026

AIRA_2: Overcoming Bottlenecks in AI Research Agents

Existing research has identified three structural performance bottlenecks in AI research agents: (1) synchronous single-GPU execution constrains sample throughput, limiting the benefit of search; (2) a generalization gap where validation-based selection causes overfitting and performance to degrade over extended search horizons; and (3) the limited capability of fixed, single-turn LLM operators imposes a ceiling on search performance. We introduce AIRA2_2, which addresses these bottlenecks through three architectural choices: an asynchronous multi-GPU worker pool that increases experiment throughput linearly; a Hidden Consistent Evaluation protocol that delivers a reliable evaluation signal; and ReAct agents that dynamically scope their actions and debug interactively. On MLE-bench-30, AIRA2†^{\dagger}_{2} achieves a mean Percentile Rank of 81.5% at 24 hours and 83.1% at 72 hours, outperforming the strongest baseline, which achieves 72.7%. On AIRS-Bench, AIRA2_2 exceeds human state-of-the-art on 6 out of 20 diverse research tasks. Ablations confirm that each architectural component is necessary, that performance follows a predictable scaling law that transfers across LLM backbones, and that the "overfitting" reported in prior work was driven by evaluation noise rather than true data memorization.

Karen Hambardzumyan, Nicolas Baldwin, Edan Toledo +22

FAIR at Meta · University College London · University of Oxford

Autonomous Research AgentsAI Agent ArchitectureAI Agents for Scientific DiscoveryAI Agent EvaluationAI Agent Benchmarks
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Mar 27, 2026

Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery

Recent work has questioned whether large language models (LLMs) can perform genuine in-context learning (ICL) for scientific experimental design, with prior studies suggesting that LLM-based agents exhibit no sensitivity to experimental feedback. We shed new light on this question by carrying out 800 independently replicated experiments on iterative perturbation discovery in Cell Painting high-content screening. We compare an LLM agent that iteratively updates its hypotheses using experimental feedback to a zero-shot baseline that relies solely on pretraining knowledge retrieval. Access to feedback yields a +53.4%+53.4\% increase in discoveries per feature on average (p=0.003p = 0.003). To test whether this improvement arises from genuine feedback-driven learning rather than prompt-induced recall of pretraining knowledge, we introduce a random feedback control in which hit/miss labels are permuted. Under this control, the performance gain disappears, indicating that the observed improvement depends on the structure of the feedback signal (+13.0+13.0 hits, p=0.003p = 0.003). We further examine how model capability affects feedback utilization. Upgrading from Claude Sonnet 4.5 to 4.6 reduces gene hallucination rates from ∼33%{\sim}33\%--45%45\% to ∼3{\sim}3--9%9\%, converting a non-significant ICL effect (+0.8+0.8, p=0.32p = 0.32) into a large and highly significant improvement (+11.0+11.0, p=0.003p=0.003) for the best ICL strategy. These results suggest that effective in-context learning from experimental feedback emerges only once models reach a sufficient capability threshold.

Gilles Wainrib, Barbara Bodinier, Haitem Dakhli +5

Owkin Inc.

AI Agents for Scientific DiscoveryLarge Language Model-Guided Scientific DiscoveryIn-Context Learning
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Mar 25, 2026

AI-Supervisor: Autonomous AI Research Supervision via a Persistent Research World Model

Existing automated research systems operate as stateless, linear pipelines -- generating outputs without maintaining any persistent understanding of the research landscape they navigate. They process papers sequentially, propose ideas without structured gap analysis, and lack mechanisms for agents to verify, challenge, or refine each other's findings. We present AI-Supervisor, a multi-agent orchestration framework where specialized agents provide end-to-end AI research supervision driven by human interests -- from literature review through gap discovery, method development, evaluation, and paper writing -- through autonomous exploration and self-correcting updates of research knowledge. Unlike sequential pipelines, AI-Supervisor maintains a continuously evolving Research World Model, implemented as a Knowledge Graph, that captures methods, benchmarks, known limitations, and unexplored gaps, serving as shared memory across all agents and enabling agents to explore and build upon a structured understanding of the research landscape. The framework introduces three architectural contributions: (1) structured gap discovery that decomposes methods into core modules, validates their performance across benchmarks, and maps the specific gaps each module creates; (2) self-correcting discovery loops that probe why modules succeed on certain problems and fail on others, whether benchmarks carry hidden biases, and whether evaluation protocols remain adequate for emerging challenges; and (3) self-improving development loops governed by cross-domain mechanism search that iteratively targets failing modules by finding solutions from other scientific fields. All agents operate under a consensus mechanism where independent findings are corroborated before being committed to the Research World Model.

Yunbo Long

Autonomous Research AgentsAI Agents for Scientific DiscoveryMulti-Agent LLM Systems
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Mar 23, 2026

Closing the Auto-Research Loop: An AI Co-Scientist for Production Search Ranking

We present an AI Co-Scientist framework that closes the research loop for the production search-ranking system of a large online travel platform -- pairing LLM agents with direct cloud-compute access so that idea generation, code implementation, GPU experimentation, and result analysis iterate end-to-end with a human scientist in the loop. The framework uses a hybrid agent architecture: single-LLM agents handle routine work, while multi-LLM consensus (GPT-5.2, Gemini Pro 3, Claude Opus 4.5) is invoked for higher-stakes decisions. On the production ranking task, a human-designed transformer baseline (V2) yielded +0.118%+0.118\% over a pre-transformer baseline (V1); the AI Co-Scientist's automated loop on top of V2 contributed an additional +0.083%+0.083\%, for a combined +0.201%+0.201\% offline gain delivered in roughly one extra week of wall-clock time (single-run numbers; statistical limits discussed in the paper). The most useful AI proposals -- unified long-sequence layouts, slot-type embeddings, and multi-phase learning-rate schedules -- are standard practice in NLP and Vision but were absent from our production stack, suggesting that LLM agents can serve as cross-disciplinary connectors for ranking teams. We also report deployment context, negative results, and lessons learned.

Liwei Wu, Cho-Jui Hsieh

Trip.com Group · UCLA

Autonomous Research AgentsLearning to RankAI Agents for Scientific DiscoveryHuman-in-the-Loop AIMulti-Agent LLM Systems
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Mar 23, 2026

Early Discoveries of Algorithmist I: Promise of Provable Algorithm Synthesis at Scale

Designing algorithms with provable guarantees that also work well in practice remains difficult, requiring both mathematical reasoning and careful implementation. Existing approaches that bridge worst-case theory and empirical performance, such as beyond-worst-case analysis and data-driven algorithm selection, typically assume prior distributional knowledge or restrict attention to a fixed pool of algorithms. Recent progress in LLMs suggests a new possibility: provable algorithm synthesis on the fly. To study this, we built Algorithmist, an autonomous researcher agent on top of GitHub Copilot that runs a multi-agent research-and-review loop, with separate stages for idea generation, algorithm and proof development, proof-guided implementation, and review of proofs, code, and their alignment. We evaluate Algorithmist on research-level tasks in private data analysis and clustering. When asked to design practical methods that jointly satisfy privacy, approximation, and interpretability requirements, it produced provably sound and empirically effective algorithms, together with research-style writeups and audited implementations. It also found improved algorithms in some settings, explained principled barriers in others, and uncovered a subtle proof bug in prior published work. More broadly, our results suggest a new paradigm in which LLM systems generate research-paper-quality algorithmic artifacts tailored to each dataset and deployment setting. They also point to a proof-first code-synthesis paradigm, in which code is developed alongside a structured natural-language proof intermediate representation and kept aligned with it throughout synthesis.

Janardhan Kulkarni

Automated Algorithm DiscoveryProgram SynthesisAI Agents for Scientific Discovery
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Mar 22, 2026

TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology

Modern seismic networks resolve earthquake sequences in unprecedented detail, yet explaining how large earthquakes emerge from evolving fault systems remains difficult. We introduce TRACE, a seismology-guided artificial intelligence agent that plans and executes workflows while preserving auditable evidence chains from observations to physical interpretation. We evaluated TRACE through 104 benchmark tasks and two complementary earthquake sequences. For the well-studied 2019 Ridgecrest sequence, TRACE constructed a high-resolution catalog from continuous waveforms and retrospectively recovered delayed cascading activation between the Mw 6.4 and Mw 7.1 earthquakes without a prescribed target interpretation. In the less-understood 2025-2026 Sanriku sequence off northeastern Japan, TRACE developed a testable interpretation of progressive destabilization within a segmented megathrust. Its synthesis linked coupled seismic-aseismic activation around the MJ 6.9 sequence and subsequent persistent, spatially segmented shallow-interface activity to a megathrust patch that lay between regions of past large coseismic slip and later hosted the MJ 7.7 rupture. These results open a path from seismic observations to testable physical insight.

Feng Liu, Xin Cui, Jian Xu +11

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, China. · School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China. +3

Physical ReasoningAI Agents for Scientific DiscoverySeismologyMulti-Agent ReasoningAutonomous Research Agents
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Mar 22, 2026

AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation

Phase-field modeling links thermodynamics and kinetics to microstructural evolution, but multiphysics frameworks such as MOOSE require expertise to construct inputs, manage campaigns, diagnose failures, and validate results. We introduce AutoMOOSE, an open-source multi-agent framework that orchestrates the simulation lifecycle from a single natural-language prompt. Six specialized agents--Architect, Input Writer, Runner, Reviewer, Visualization, and a physics-grounded Skeptic--generate, execute, analyze, and adversarially test simulations against conservation laws, asymptotic limits, and scaling relations. We validate AutoMOOSE in two domains: non-conserved copper grain growth governed by Allen-Cahn dynamics and conserved Fe-Cr spinodal decomposition governed by Cahn-Hilliard dynamics. On a prospectively specified 25-task grain-growth benchmark spanning temperature, grain count, resolution, and model formulation, AutoMOOSE generates valid inputs for all 25 tasks, completes 19 simulations with measurable coarsening, and delivers 15 results that satisfy Burke-Turnbull kinetics (R2≥0.90R^2 \geq 0.90) and survive Skeptic falsification. The failures trace to two identifiable generator defects. An ensemble of 1000 simulations recovers the prescribed activation energy to within 1% after finite-size extrapolation (Q∞=0.228Q_\infty = 0.228 eV versus 0.2300.230 eV). In the conserved domain, an agent-generated CALPHAD-based Fe-Cr simulation limits relative mass drift to 6.3×10−66.3 \times 10^{-6}, dissipates free energy to a stable plateau, and evolves toward the equilibrium tie-line. Controlled ablations show that the full pipeline converts stochastic raw generation--3-7 successful tasks out of 8 across repeated trials--into consistent success on all 8 tasks. AutoMOOSE therefore provides a practical route from natural-language specification to reproducible, physics-validated phase-field simulation campaigns.

Sukriti Manna, Henry Chan, Subramanian K. R. S. Sankaranarayanan

Department of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL 60607, USA · Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA

Computational Materials ScienceAI Agents for Scientific DiscoveryPhysics-Based SimulationMultiphysics Simulation
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Mar 20, 2026

AI Agents Can Already Autonomously Perform Experimental High Energy Physics

Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipeline with minimal expert-curated input. Given access to a HEP dataset, an execution framework, and a corpus of prior experimental literature, we find that Claude Code succeeds in automating all stages of a typical analysis: event selection, background estimation, uncertainty quantification, statistical inference, and paper drafting. We argue that the experimental HEP community is underestimating the current capabilities of these systems, and that most proposed agentic workflows are too narrowly scoped or scaffolded to specific analysis structures. We present a proof-of-concept framework, Just Furnish Context (JFC), that integrates autonomous analysis agents with literature-based knowledge retrieval and multi-agent review, and show that this is sufficient to plan, execute, and document a credible high energy physics analysis. We demonstrate this by conducting analyses on open data from ALEPH, DELPHI, and CMS to perform electroweak, QCD, and Higgs boson measurements. We present two of those results in a condensed short paper form -- a CMS Run1 Open Data H→τ+τ−H\to τ^+τ^- to demonstrate performance on a well-established result, and the first Lund plane measurement on LEP data -- a genuinely novel result and, to our knowledge, the first produced autonomously by an AI agent. Rather than replacing physicists, these tools promise to offload the repetitive technical burden of analysis code development, freeing researchers to focus on physics insight, truly novel method development, and rigorous validation. Given these developments, we advocate for new strategies for how the community trains students, organizes analysis efforts, and allocates human expertise.

Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak +2

Department of Physics, Massachusetts Institute of Technology · NSF AI Institute for Artificial Intelligence and Fundamental Interactions · CERN

Autonomous Research AgentsAI Agents for Scientific DiscoveryData Analysis AgentsLLM Agent HarnessesHigh-Energy Physics
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Mar 20, 2026

Embodied Science: Closing the Discovery Loop with Agentic Embodied AI

Artificial intelligence has demonstrated remarkable capability in predicting scientific properties, yet scientific discovery remains an inherently physical, long-horizon pursuit governed by experimental cycles. Most current computational approaches are misaligned with this reality, framing discovery as isolated, task-specific predictions rather than continuous interaction with the physical world. Here, we argue for embodied science, a paradigm that reframes scientific discovery as a closed loop tightly coupling agentic reasoning with physical execution. We propose a unified Perception-Language-Action-Discovery (PLAD) framework, wherein embodied agents perceive experimental environments, reason over scientific knowledge, execute physical interventions, and internalize outcomes to drive subsequent exploration. By grounding computational reasoning in robust physical feedback, this approach bridges the gap between digital prediction and empirical validation, offering a roadmap for autonomous discovery systems in the life and chemical sciences.

Xiang Zhuang, Chenyi Zhou, Kehua Feng +10

Zhejiang University, Hangzhou, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · Tongji University, Shanghai, China

AI Agents for Scientific DiscoveryAutonomous ExperimentationEmbodied AIScientific Reasoning in Language Models
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Mar 18, 2026

Toward Evaluation Frameworks for Multi-Agent Scientific AI Systems

We analyze the challenges of benchmarking scientific (multi)-agentic systems, including the difficulty of distinguishing reasoning from retrieval, the risks of data/model contamination, the lack of reliable ground truth for novel research problems, the complications introduced by tool use, and the replication challenges due to the continuously changing/updating knowledge base. We discuss strategies for constructing contamination-resistant problems, generating scalable families of tasks, and the need for evaluating systems through multi-turn interactions that better reflect real scientific practice. As an early feasibility test, we demonstrate how to construct a dataset of novel research ideas to test the out-of-sample performance of our system. We also discuss the results of interviews with several researchers and engineers working in quantum science. Through those interviews, we examine how scientists expect to interact with AI systems and how these expectations should shape evaluation methods.

Marcin Abram

Los Angeles, California 90089, USA

Scientific Agent BenchmarksAI Agent BenchmarksMulti-Agent LLM SystemsAI Agents for Scientific Discovery
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Mar 17, 2026

OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materials

Physics-aware multimodal large language models (MLLMs) can localize and characterize two-dimensional (2D) material flakes from optical microscopy images, producing rich candidate observations and scientific reasoning. However, downstream workflows also require reliable operations over these outputs, including counting, selection, measurement, and rendering. We present OpenQlaw, a tool-using agentic artificial intelligence (AI) system that connects a general vision-language orchestrator with Physics-Aware Instruction Tuning (QuPAINT), a physics-aware domain expert. Through evaluation on fully reviewed optical microscopy images, we show that summary counts derived from longer free-form candidate enumerations can become inconsistent with the underlying returned observations. Controlled replay experiments further show that structuring the same observations alone does not resolve this behavior, while deterministic delegation returns the exact requested record count in all cohort trials. To address this, we introduce State-Grounded OpenQlaw, which converts expert outputs into image-scoped observation records and performs deterministic scientific operations over explicitly identified records with refusal conditions when required information is unavailable. This preserves the expert's original analysis while making downstream operations exact over the selected record and independently evaluable from candidate-list discrepancies.

Sankalp Pandey, Thanh-Dat Truong, Xuan-Bac Nguyen +5

Quantum AI Lab, University of Arkansas, Fayetteville, AR 72701, USA · MonArk NSF Quantum Foundry, USA · Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112, USA +1

Tool-Using Vision-Language AgentsAI Agents for Scientific DiscoveryMultimodal Large Language ModelsMaterials Science
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Mar 16, 2026

Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents

Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks. Protein design provides a natural testbed: although machine learning (ML) methods achieve strong results, these are largely restricted to canonical amino acids and narrow objectives, leaving unfilled need for a generalist tool for broad design pipelines. We introduce Agent Rosetta, an LLM agent paired with a structured environment for operating Rosetta, the leading physics-based heteropolymer design software, capable of modeling non-canonical building blocks and geometries. Agent Rosetta iteratively refines designs to achieve user-defined objectives, combining LLM reasoning with Rosetta's generality. We evaluate Agent Rosetta on design with canonical amino acids, matching specialized models and expert baselines, and with non-canonical residues -- where ML approaches fail -- achieving comparable performance. Critically, prompt engineering alone often fails to generate Rosetta actions, demonstrating that environment design is essential for integrating LLM agents with specialized software. Our results show that properly designed environments enable LLM agents to make scientific software accessible while matching specialized tools and human experts.

Jacopo Teneggi, S. M. Bargeen A. Turzo, Tanya Marwah +4

Polymathic AI · Johns Hopkins University · Center for Computational Biology, Flatiron Institute +3

Tool-Augmented Language Model AgentsAI Agents for Scientific DiscoveryProtein DesignLLM Agent Harnesses
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Mar 15, 2026

CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad

Evolve-based agent such as AlphaEvolve is one of the notable successes in using Large Language Models (LLMs) to build AI Scientists. These agents tackle open-ended scientific problems by iteratively improving and evolving programs, leveraging the prior knowledge and reasoning capabilities of LLMs. Despite the success, existing evolve-based agents lack targeted guidance for evolution and effective mechanisms for organizing and utilizing knowledge acquired from past evolutionary experience. Consequently, they suffer from decreasing evolution efficiency and exhibit oscillatory behavior when approaching known performance boundaries. To mitigate the gap, we develop CausalEvolve, equipped with a causal scratchpad that leverages LLMs to identify and reason about guiding factors for evolution. At the beginning, CausalEvolve first identifies outcome-level factors that offer complementary inspirations in improving the target objective. During the evolution, CausalEvolve also inspects surprise patterns during the evolution and abductive reasoning to hypothesize new factors, which in turn offer novel directions. Through comprehensive experiments, we show that CausalEvolve effectively improves the evolutionary efficiency and discovers better solutions in 4 challenging open-ended scientific tasks.

Yongqiang Chen, Chenxi Liu, Zhenhao Chen +3

MBZUAI · Carnegie Mellon University · TMLR Group, Hong Kong Baptist University +1

AI Agents for Scientific DiscoveryLarge Language Model-Guided Scientific DiscoveryOpen-Ended EvolutionLarge Language Model-Guided Optimization
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Mar 15, 2026

An End-to-end Architecture for Collider Physics and Beyond

We present, to our knowledge, the first language-driven agent system capable of executing end-to-end collider phenomenology tasks, instantiated within a decoupled, domain-agnostic architecture for autonomous High-Energy Physics phenomenology. Guided only by natural-language prompts supplemented with standard physics notation, ColliderAgent carries out workflows from a theoretical Lagrangian to final phenomenological outputs without relying on package-specific code. In this framework, a hierarchical multi-agent reasoning layer is coupled to Magnus, a unified execution backend for phenomenological calculations and simulation toolchains. We validate the system on representative literature reproductions spanning leptoquark and axion-like-particle scenarios, higher-dimensional effective operators, parton-level and detector-level analyses, and large-scale parameter scans leading to exclusion limits. These results point to a route toward more automated, scalable, and reproducible research in collider physics, cosmology, and physics more broadly.

Shi Qiu, Zeyu Cai, Jiashen Wei +7

School of Physics, Peking University, Beijing 100871, China · Center for High Energy Physics, Peking University, Beijing 100871, China · School of Physics, Zhengzhou University, Zhengzhou 450001, China +3

Autonomous Research AgentsAI Agents for Scientific DiscoveryMulti-Agent LLM Systems
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Mar 13, 2026

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics

While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher. What distinguishes research from routine execution is the progressive accumulation of knowledge - learning which approaches fail, recognizing patterns across systems, and applying understanding to new problems. However, the prevailing paradigm in AI-driven computational science treats each execution in isolation, largely discarding hard-won insights between runs. Here we present QMatSuite, an open-source platform closing this gap. Agents record findings with full provenance, retrieve knowledge before new calculations, and in dedicated reflection sessions correct erroneous findings and synthesize observations into cross-compound patterns. In benchmarks on a six-step quantum-mechanical simulation workflow, accumulated knowledge reduces reasoning overhead by 67% and improves accuracy from 47% to 3% deviation from literature - and when transferred to an unfamiliar material, achieves 1% deviation with zero pipeline failures.

Haonan Huang

Department of Physics, Princeton University, Princeton, NJ 08540, USA.

AI Agents for Scientific DiscoveryComputational Materials ScienceMemory-Augmented Language Model Agents
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Mar 12, 2026

ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics

Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language. Here we introduce ELISA (Embedding-Linked Interactive Single-cell Agent), an interpretable framework that unifies scGPT expression embeddings with BioBERT-based semantic retrieval and LLM-mediated interpretation for interactive single-cell discovery. An automatic query classifier routes inputs to gene marker scoring, semantic matching, or reciprocal rank fusion pipelines depending on whether the query is a gene signature, natural language concept, or mixture of both. Integrated analytical modules perform pathway activity scoringacross 60+ gene sets, ligand-receptor interaction prediction using 280+ curated pairs, condition-aware comparative analysis, and cell-type proportion estimation all operating directly on embedded data without access to the original count matrix. Benchmarked across six diverse scRNA-seq datasets spanning inflammatory lung disease, pediatric and adult cancers, organoid models, healthy tissue, and neurodevelopment, ELISA significantly outperforms CellWhisperer, a classical lexical retriever (BM25), and a random baseline in cell type retrieval (combined permutation test, p<2×10−5p < 2\times10^{-5} for each), with particularly large gains on gene-signature queries (Cohen's d=5.98d = 5.98 for MRR). ELISA replicates published biological findings (mean composite score 0.88), and generates candidate hypotheses through grounded LLM reasoning, bridging the gap between transcriptomic data exploration and biological discovery.

Omar Coser

AI Agents for Scientific DiscoverySingle-Cell TranscriptomicsSingle-Cell RNA SequencingBiomedical IRLarge Language Model-Guided Scientific Discovery
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Mar 12, 2026

Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems

Today's scientific challenges, from climate modeling to Inertial Confinement Fusion design to novel material design, require exploring huge design spaces. In order to enable high-impact scientific discovery, we need to scale up our ability to test hypotheses, generate results, and learn from them rapidly. We present MADA (Multi-Agent Design Assistant), a Large Language Model (LLM) powered multi-agent framework that coordinates specialized agents for complex design workflows. A Job Management Agent (JMA) launches and manages ensemble simulations on HPC systems, a Geometry Agent (GA) generates meshes, and an Inverse Design Agent (IDA) proposes new designs informed by simulation outcomes. While general purpose, we focus development and validation on Richtmyer--Meshkov Instability (RMI) suppression, a critical challenge in Inertial Confinement Fusion. We evaluate on two complementary settings: running a hydrodynamics simulations on HPC systems, and using a pre-trained machine learning surrogate for rapid design exploration. Our results demonstrate that the MADA system successfully executes iterative design refinement, automatically improving designs toward optimal RMI suppression with minimal manual intervention. Our framework reduces cumbersome manual workflow setup, and enables automated design exploration at scale. More broadly, it demonstrates a reusable pattern for coupling reasoning, simulation, specialized tools, and coordinated workflows to accelerate scientific discovery.

Harshitha Menon, Charles F. Jekel, Kevin Korner +15

Lawrence Livermore National Laboratory, Livermore, CA, USA · Sandia National Laboratories, Albuquerque, NM, USA · Los Alamos National Laboratory, Los Alamos, NM, USA +1

AI Agents for Scientific DiscoveryMulti-Agent LLM SystemsInverse Design
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Mar 10, 2026

From Phase Prediction to Phase Design: A ReAct Agent Framework for High-Entropy Alloy Discovery

Discovering high-entropy alloy (HEA) compositions that reliably form a target crystal phase is a high-dimensional inverse design problem that conventional trial-and-error experimentation and forward-only machine learning models cannot efficiently solve. Here we present a ReAct (Reasoning + Acting) LLM agent that autonomously proposes, validates, and iteratively refines HEA compositions by querying a calibrated XGBoost surrogate trained on 4,753 experimental records across four phases (FCC, BCC, BCC+FCC, BCC+IM), achieving 94.66% accuracy (F1 macro = 0.896). Against Bayesian optimisation (BO) and random search baselines, the full-prompt agent achieves descriptor-space rediscovery rates of 38%, 18%, and 38% for FCC, BCC, and BCC+FCC (Mann--Whitney p≤0.039p \leq 0.039), with proposals lying 2.4--22.8×\times closer to the experimental phase manifold than random search. An ablation reveals that domain priors shift the agent from landmark-alloy recall toward compositionally diverse exploration -- an uninformed agent scores higher rediscovery by concentrating on literature-dense families, while the full-prompt agent explores underrepresented space (unique ratio 1.0 vs.\ 0.39 for BCC+FCC). These regimes represent distinct criteria: proximity to known literature versus genuine discovery. Spearman analysis confirms agent reasoning is statistically aligned with empirical phase distributions (ρ=0.736ρ= 0.736, p=0.004p = 0.004 for BCC). This work establishes LLM-guided agentic reasoning as a principled, transparent, and manifold-aware complement to gradient-free optimisation for inverse alloy design.

Iman Peivaste, Salim Belouettar

Luxembourg Institute of Science and Technology (LIST), 5, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, 4362, Luxembourg · Department of Physics and Materials Science, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg

Inverse DesignMaterials ScienceLarge Language Model-Guided Scientific DiscoveryAI Agents for Scientific DiscoveryLarge Language Model-Guided Optimization
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