Autonomous Research Agents

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Overview

Autonomous research agents use language models to plan and carry out research tasks, from literature search and hypothesis generation to coding, experiments, analysis, and reporting. Common approaches include tool-using coding agents, teams of specialized agents, persistent memory, and training with supervised or reinforcement learning; execution harnesses and formal or domain-specific checks help constrain their work. Agents can perform well on bounded, executable tasks such as model optimization, code reproduction, and formal proof search, but open-ended research remains harder, especially when novelty or scientific validity is difficult to measure. A central challenge is ensuring that reported claims follow from reliable experiments and evidence, which motivates independent verification, held-out evaluation, provenance tracking, and continued human oversight.

Latest papers

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324 papers60 papers in the last four weeks, up 62% on the four weeks before. 0.3% of all new papers.

Apr 8, 2026

Rhizome OS-1: Rhizome's Semi-Autonomous Operating System for Small Molecule Drug Discovery

We present Rhizome OS-1, a semi-autonomous operating system for small molecule drug discovery in which multi-modal AI agents operate as a full multidisciplinary discovery team. These agents function as computational chemists, medicinal chemists, and patent agents: they write and execute analysis code (fingerprint clustering, R-group decomposition, substructure search), visually triage molecular grids using vision capabilities, formulate explicit medicinal chemistry hypotheses across three strategy tiers, assess patent freedom-to-operate, and dynamically adapt generation strategies based on empirical screening feedback. Powered by r1 - a 246M-parameter graph diffusion model trained on 800 million molecular graphs - the system generates novel chemical matter directly on molecular graphs using fragment masking, scaffold decoration, linker design, and graph editing primitives. In two oncology campaigns (BCL6 BTB domain and EZH2 SET domain), the agent team executed 26 seeds and produced 5,231 novel molecules. Across both targets, 91.9% of generated Murcko scaffolds are absent from ChEMBL, with median Tanimoto similarity of 0.56-0.69 to the nearest known active. Boltz-2 binding affinity predictions, calibrated against ChEMBL data, achieved Spearman correlations of -0.53 to -0.64 and ROC AUC values of 0.88-0.93. These results demonstrate that semi-autonomous agent systems, equipped with graph-native generative tools and physics-informed scoring, enable a new paradigm for early-stage drug discovery: scaled, rapid, and adaptive inverse design with embedded medicinal chemistry reasoning.

Yiwen Wang, Gregory Sinenka, Xhuliano Brace

Rhizome Research Inc.

Drug DiscoveryAutonomous Research AgentsMolecular Graph Generation
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Apr 8, 2026

Agent-Driven Corpus Linguistics: A Framework for Autonomous Linguistic Discovery

Corpus linguistics has traditionally relied on human researchers to formulate hypotheses, construct queries, and interpret results - a process demanding specialized technical skills and considerable time. We propose Agent-Driven Corpus Linguistics, an approach in which a large language model (LLM), connected to a corpus query engine via a structured tool-use interface, takes over the investigative cycle: generating hypotheses, querying the corpus, interpreting results, and refining analysis across multiple rounds. The human researcher sets direction and evaluates final output. Unlike unconstrained LLM generation, every finding is anchored in verifiable corpus evidence. We treat this not as a replacement for the corpus-based/corpus-driven distinction but as a complementary dimension: it concerns who conducts the inquiry, not the epistemological relationship between theory and data. We demonstrate the framework by linking an LLM agent to a CQP-indexed Gutenberg corpus (5 million tokens) via the Model Context Protocol (MCP). Given only "investigate English intensifiers," the agent identified a diachronic relay chain (so+ADJ > very > really), three pathways of semantic change (delexicalization, polarity fixation, metaphorical constraint), and register-sensitive distributions. A controlled baseline experiment shows that corpus grounding contributes quantification and falsifiability that the model cannot produce from training data alone. To test external validity, the agent replicated two published studies on the CLMET corpus (40 million tokens) - Claridge (2025) and De Smet (2013) - with close quantitative agreement. Agent-driven corpus research can thus produce empirically grounded findings at machine speed, lowering the technical barrier for a broader range of researchers.

Jia Yu, Weiwei Yu, Pengfei Xiao +1

Zhejiang International Studies University, Hangzhou, China · Tianjin University, Tianjin, China

Corpus LinguisticsAutonomous Research AgentsLLM Tool Use
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Apr 7, 2026

Deep Researcher Agent: An Autonomous Framework for 24/7 Deep Learning Experimentation with Zero-Cost Monitoring

We present Deep Researcher Agent, an open-source framework that enables large language model (LLM) agents to autonomously conduct deep learning experiments around the clock. Unlike existing AI research assistants that focus on paper writing or code generation, our system addresses the full experiment lifecycle: hypothesis formation, code implementation, training execution, result analysis, and iterative refinement. The framework introduces three key innovations: (1) Zero-Cost Monitoring -- a monitoring paradigm that incurs zero LLM API costs during model training by relying solely on process-level checks and log file reads; (2) Two-Tier Constant-Size Memory -- a memory architecture capped at ∼\sim5K characters regardless of runtime duration, preventing the unbounded context growth that plagues long-running agents; and (3) Minimal-Toolset Leader-Worker Architecture -- a multi-agent design where each worker agent is equipped with only 3--5 tools, reducing per-call token overhead by up to 73%. In sustained deployments spanning 30+ days, the framework autonomously completed 500+ experiment cycles across four concurrent research projects, achieving a 52% improvement over baseline metrics in one project through 200+ automated experiments -- all at an average LLM cost of $0.08 per 24-hour cycle. Code is available at https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7.

Xiangyue Zhang

The University of Tokyo

Autonomous Research AgentsAutonomous ExperimentationAI Agent MonitoringMulti-Agent LLMsMemory-Augmented Language Model Agents
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Apr 7, 2026

AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery

Artificial intelligence research increasingly depends on prolonged cycles of reproduction, debugging, and iterative refinement to achieve State-Of-The-Art (SOTA) performance, creating a growing need for systems that can accelerate the full pipeline of empirical model optimization. In this work, we introduce AutoSOTA, an end-to-end automated research system that advances the latest SOTA models published in top-tier AI papers to reproducible and empirically improved new SOTA models. We formulate this problem through three tightly coupled stages: resource preparation and goal setting; experiment evaluation; and reflection and ideation. To tackle this problem, AutoSOTA adopts a multi-agent architecture with eight specialized agents that collaboratively ground papers to code and dependencies, initialize and repair execution environments, track long-horizon experiments, generate and schedule optimization ideas, and supervise validity to avoid spurious gains. We evaluate AutoSOTA on recent research papers collected from eight top-tier AI conferences under filters for code availability and execution cost. Across these papers, AutoSOTA achieves strong end-to-end performance in both automated replication and subsequent optimization. Specifically, it successfully discovers 105 new SOTA models that surpass the original reported methods, averaging approximately five hours per paper. Case studies spanning LLM, NLP, computer vision, time series, and optimization further show that the system can move beyond routine hyperparameter tuning to identify architectural innovation, algorithmic redesigns, and workflow-level improvements. These results suggest that end-to-end research automation can serve not only as a performance optimizer, but also as a new form of research infrastructure that reduces repetitive experimental burden and helps redirect human attention toward higher-level scientific creativity.

Yu Li, Chenyang Shao, Xinyang Liu +13

Department of Electronic Engineering, BNRist, Tsinghua University · Zhongguancun Academy · Peking University +1

Autonomous ExperimentationAutonomous Research AgentsAutomated Algorithm DiscoveryAutomated Machine Learning
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Apr 6, 2026

OpenCLAW-P2P v7.0-P2PCLAW: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review v7.0 -- Mathematical Corrections & Ecosystem Developments Edition

This paper presents OpenCLAW-P2P v7.0, a comprehensive evolution of the decentralized collective-intelligence platform in which autonomous AI agents publish, peer-review, score, and iteratively improve scientific research papers without any human gatekeeper. Building on the v6.0 foundations -- multi-layer persistence, live reference verification, multi-LLM granular scoring, calibrated deception detection, the Silicon Chess-Grid FSM, and the AETHER containerized inference engine -- this release introduces mathematical corrections to the theoretical framework, ensuring dimensional consistency, proper range constraints, and unambiguous notation throughout. Additionally, this edition documents significant ecosystem expansions including the CAJAL family of open-source language models (4B and 9B parameters) fine-tuned for scientific paper generation. The four major subsystems introduced in v6.0 are retained: (i) a Multi-Layer Paper Persistence Architecture with four storage tiers ensuring zero paper loss; (ii) a Multi-Layer Retrieval Cascade reducing latency from >3s to <50ms; (iii) a Live Reference Verification system detecting fabricated citations with >85% accuracy; and (iv) a Scientific API Proxy providing access to seven public scientific databases. Mathematical corrections in v7.0 include: corrected fixed-point condition in the Sufficient Reason theorem; dimensionally consistent progress-rate indicator; fully specified reputation update formula incorporating quality terms q0 and q-bar; clarified attention-logit bound in the AETHER pruning theorem; explicit range documentation for the calibration mapping; non-negativity guarantee for the depth score; discrete-time notation for the PD Governor; and explicit parameter definitions for the HSR weight formula.

Francisco Angulo de Lafuente, Teerth Sharma, Vladimir Veselov +3

Independent AI Researcher & Science Fiction Writer, Madrid, Spain · Bachelor of Technology (AI), Manipal University Jaipur, India · Moscow Institute of Electronic Technology (MIET), Russia +3

Automated Peer ReviewAutonomous Research AgentsScholarly Peer ReviewMulti-Agent LLM Systems
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Apr 6, 2026

Memory Intelligence Agent

Deep research agents (DRAs) integrate LLM reasoning with external tools. Memory systems enable DRAs to leverage historical experiences, which are essential for efficient reasoning and autonomous evolution. Existing methods rely on retrieving similar trajectories from memory to aid reasoning, while suffering from key limitations of ineffective memory evolution and increasing storage and retrieval costs. To address these problems, we propose a novel Memory Intelligence Agent (MIA) framework, consisting of a Manager-Planner-Executor architecture. Memory Manager is a non-parametric memory system that can store compressed historical search trajectories. Planner is a parametric memory agent that can produce search plans for questions. Executor is another agent that can search and analyze information guided by the search plan. To build the MIA framework, we first adopt an alternating reinforcement learning paradigm to enhance cooperation between the Planner and the Executor. Furthermore, we enable the Planner to continuously evolve during test-time learning, with updates performed on-the-fly alongside inference without interrupting the reasoning process. Additionally, we establish a bidirectional conversion loop between parametric and non-parametric memories to achieve efficient memory evolution. Finally, we incorporate a reflection and an unsupervised judgment mechanisms to boost reasoning and self-evolution in the open world. Extensive experiments across eleven benchmarks demonstrate the superiority of MIA.

Jingyang Qiao, Weicheng Meng, Yu Cheng +6

Memory-Augmented Language Model AgentsAutonomous Research AgentsAgent Memory ManagementSelf-Evolving Agents
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Apr 2, 2026

MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization

Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specialized tools in multi-step workflows, yet current AI agents struggle to maintain robust performance and consistently underperform in these high-complexity scenarios. Here we present MolClaw, an autonomous agent that leads drug molecule evaluation, screening, and optimization. It unifies over 30 specialized domain resources through a three-tier hierarchical skill architecture (70 skills in total) that facilitates agent long-term interaction at runtime: tool-level skills standardize atomic operations, workflow-level skills compose them into validated pipelines with quality check and reflection, and a discipline-level skill supplies scientific principles governing planning and verification across all scenarios in the field. Additionally, we introduce MolBench, a benchmark comprising molecular screening, optimization, and end-to-end discovery challenges spanning 8 to 50+ sequential tool calls. MolClaw achieves state-of-the-art performance across all metrics, and ablation studies confirm that gains concentrate on tasks that demand structured workflows while vanishing on those solvable with ad hoc scripting, establishing workflow orchestration competence as the primary capability bottleneck for AI-driven drug discovery.

Lisheng Zhang, Lilong Wang, Xiangyu Sun +14

Peking University Health Science Center, Peking University, Beijing, China · Shanghai AI Laboratory, Shanghai, China · Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

Drug DiscoveryAutonomous Research AgentsMolecular OptimizationLLM Agent OrchestrationAI Agent BenchmarksAgent Skill Learning
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Apr 1, 2026

From Data to Theory: Autonomous Large Language Model Agents for Materials Science

We present an autonomous large language model (LLM) agent for end-to-end, data-driven materials theory development. The model can choose an equation form, generate and run its own code, and test how well the theory matches the data without human intervention. The framework combines step-by-step reasoning with expert-supplied tools, allowing the agent to adjust its approach as needed while keeping a clear record of its decisions. For well-established materials relationships such as the Hall-Petch equation and Paris law, the agent correctly identifies the governing equation and makes reliable predictions on new datasets. For more specialized relationships, such as Kuhn's equation for the HOMO-LUMO gap of conjugated molecules as a function of length, performance depends more strongly on the underlying model, with GPT-5 showing better recovery of the correct equation. Beyond known theories, the agent can also suggest new predictive relationships, illustrated here by a strain-dependent law for changes in the HOMO-LUMO gap. At the same time, the results show that careful validation remains essential, because the agent can still return incorrect, incomplete, or inconsistent equations even when the numerical fit appears strong. Overall, these results highlight both the promise and the current limitations of autonomous LLM agents for AI-assisted scientific modeling and discovery.

Samuel Onimpa Alfred, Veera Sundararaghavan

Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109

Large Language Model-Guided Scientific DiscoveryScientific DiscoveryAutonomous Research AgentsMaterials ScienceLLM Agent Reliability
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Apr 1, 2026

Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory

AI agents increasingly operate over extended time horizons, yet their ability to retain, organize, and recall multimodal experiences remains a critical bottleneck. Building effective lifelong memory requires navigating a vast design space spanning architecture, retrieval strategies, prompt engineering, and data pipelines; this space is too large and interconnected for manual exploration or traditional AutoML to explore effectively. We deploy an autonomous research pipeline to discover Omni-SimpleMem, a unified multimodal memory framework for lifelong AI agents. Starting from a naïve baseline (F1=0.117 on LoCoMo), the pipeline autonomously executes ∼50{\sim}50 experiments across two benchmarks, diagnosing failure modes, proposing architectural modifications, and repairing data pipeline bugs, all without human intervention in the inner loop. The resulting system achieves state-of-the-art on both benchmarks, improving F1 by +411% on LoCoMo (0.117→\to0.598) and +214% on Mem-Gallery (0.254→\to0.797) relative to the initial configurations. Critically, the most impactful discoveries are not hyperparameter adjustments: bug fixes (+175%), architectural changes (+44%), and prompt engineering (+188% on specific categories) each individually exceed the cumulative contribution of all hyperparameter tuning, demonstrating capabilities fundamentally beyond the reach of traditional AutoML. We provide a taxonomy of six discovery types and identify four properties that make multimodal memory particularly suited for autoresearch, offering guidance for applying autonomous research pipelines to other AI system domains. Code is available at this https://github.com/aiming-lab/SimpleMem.

Jiaqi Liu, Zipeng Ling, Shi Qiu +9

UNC-Chapel Hill · University of Pennsylvania · University of California, Santa Cruz +2

Agent MemoryMultimodal MemoryLifelong Learning AgentsAutonomous Research Agents
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Mar 31, 2026

AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs

We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quantitative, computation-intensive domains. Given only a dataset and a natural-language objective, AlphaLab proceeds through three phases without human intervention: (1) it adapts to the domain and explores the data, writing analysis code and producing a research report; (2) it constructs and adversarially validates its own evaluation framework; and (3) it runs large-scale GPU experiments via a Strategist/Worker loop, accumulating domain knowledge in a persistent playbook that functions as a form of online prompt optimization. All domain-specific behavior is factored into adapters generated by the model itself, so the same pipeline handles qualitatively different tasks without modification. We evaluate AlphaLab with two frontier LLMs (GPT-5.2 and Claude Opus 4.6) on three domains: CUDA kernel optimization, where it writes GPU kernels that run 4.4x faster than torch.compile on average (up to 91x); LLM pretraining, where the full system achieves 22% lower validation loss than a single-shot baseline using the same model; and traffic forecasting, where it beats standard baselines by 23-25% after researching and implementing published model families from the literature. The two models discover qualitatively different solutions in every domain (neither dominates uniformly), suggesting that multi-model campaigns provide complementary search coverage. We additionally report results on financial time series forecasting in the appendix, and release all code at https://brendanhogan.github.io/alphalab-paper/.

Brendan R. Hogan, Xiwen Chen, James T. Wilson +5

Morgan Stanley

Autonomous Research AgentsAutonomous ExperimentationLarge Language Model-Guided OptimizationAutomated Algorithm DiscoveryMulti-Agent LLMsGPU Kernel Optimization
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Mar 31, 2026

ASI-Evolve: AI Accelerates AI

Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well-scoped tasks with rapid feedback, it remains unclear whether they can tackle the costly, long-horizon, and weakly supervised research loops that drive real AI progress. We present ASI-Evolve, an agentic framework for AI-for-AI research that closes this loop through a learn-design-experiment-analyze cycle. ASI-Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations. To our knowledge, ASI-Evolve is the first unified framework to demonstrate AI-driven discovery across three central components of AI development: data, architectures, and learning algorithms. In neural architecture design, it discovered 105 SOTA linear attention architectures, with the best discovered model surpassing DeltaNet by +0.97 points, nearly 3x the gain of recent human-designed improvements. In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points, with gains exceeding 18 points on MMLU. In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32, +11.67 points on AIME24, and +5.04 points on OlympiadBench. We further provide initial evidence that this AI-for-AI paradigm can transfer beyond the AI stack through experiments in mathematics and biomedicine. Together, these results suggest that ASI-Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed-loop AI research.

Weixian Xu, Tiantian Mi, Yixiu Liu +6

SJTU · SII · GAIR

Autonomous Research AgentsAutomated Algorithm DiscoveryAI-Assisted Scientific ResearchSelf-Evolving AgentsTraining Data CurationNeural Architecture Search
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Mar 31, 2026

An Empirical Study of Multi-Agent Collaboration for Automated Research

As AI agents evolve, the community is rapidly shifting from single Large Language Models (LLMs) to Multi-Agent Systems (MAS) to overcome cognitive bottlenecks in automated research. However, the optimal multi-agent coordination framework for these autonomous agents remains largely unexplored. In this paper, we present a systematic empirical study investigating the comparative efficacy of distinct multi-agent structures for automated machine learning optimization. Utilizing a rigorously controlled, execution-based testbed equipped with Git worktree isolation and explicit global memory, we benchmark a single-agent baseline against two multi-agent paradigms: a subagent architecture (parallel exploration with post-hoc consolidation) and an agent team architecture (experts with pre-execution handoffs). By evaluating these systems under strictly fixed computational time budgets, our findings reveal a fundamental trade-off between operational stability and theoretical deliberation. The subagent mode functions as a highly resilient, high-throughput search engine optimal for broad, shallow optimizations under strict time constraints. Conversely, the agent team topology exhibits higher operational fragility due to multi-author code generation but achieves the deep theoretical alignment necessary for complex architectural refactoring given extended compute budgets. These empirical insights provide actionable guidelines for designing future autoresearch systems, advocating for dynamically routed architectures that adapt their collaborative structures to real-time task complexity.

Yang Shen, Zhenyi Yi, Ziyi Zhao +4

University of Technology Sydney, Ultimo NSW 2007, Australia · Southern University of Science and Technology, Shenzhen 518055, China · Shenzhen Technology University, Shenzhen 518118, China +1

Multi-Agent LLM SystemsMulti-Agent CollaborationMulti-Agent OrchestrationAutonomous Research AgentsAutomated Machine Learning
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Mar 31, 2026

Nomad: Autonomous Exploration and Discovery

We introduce Nomad, a system for autonomous data exploration and insight discovery. Given a corpus of documents, databases, or other data sources, users rarely know the full set of questions, hypotheses, or connections that could be explored. As a result, query-driven question answering and prompt-driven deep-research systems remain limited by human framing and often fail to cover the broader insight space. Nomad addresses this problem with an exploration-first architecture. It constructs an explicit Exploration Map over the domain and systematically traverses it to balance breadth and depth. It generates and selects hypotheses and investigates them with an explorer agent that can use document search, web search, and database tools. Candidate insights are then checked by an independent verifier before entering a reporting pipeline that produces cited reports and higher-level meta-reports. We also present a comprehensive evaluation framework for autonomous discovery systems that measures trustworthiness, report quality, and diversity. Using corpora of selected UN and WHO reports and arXiv papers on LLM agents, we show that Nomad produces reports with strong numeric grounding, higher overall quality and actionability than baselines, and more diverse insights over several runs. Nomad is a step toward autonomous systems that not only answer user questions or conduct directed research, but also discover which questions, research directions, and insights are worth surfacing in the first place.

Bokang Jia, Samta Kamboj, Satheesh Katipomu +3

Inception, G42 · MBZUAI · G42

Autonomous Research AgentsAutonomous Scientific DiscoveryScientific Hypothesis Generation
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Mar 31, 2026

Towards End-to-End Automation of AI Research

The automation of science is a long-standing ambition in the field of AI. While the community has made significant progress in automating individual components of the scientific process, a system that autonomously navigates the entire research lifecycle -- from conception to publication -- has remained out of reach. Here, we present the strongest demonstration to date toward automating the entire process end-to-end. We present The AI Scientist, which creates research ideas, writes code, runs experiments, plots and analyzes data, writes the entire scientific manuscript and performs its own peer review. Its ideas, execution, and presentation are of sufficient quality to produce a manuscript generated by an AI system that passes the first round of peer review at a major machine learning conference workshop. The workshop has an acceptance rate of 70 percent. Our system leverages modern foundation models within a complex agentic system. We evaluate The AI Scientist in two settings: a focused mode using human-provided code templates as an initial scaffold to conduct research on a specific topic, and a template-free, open-ended mode that leverages agentic search for wider scientific exploration. Both settings produce diverse ideas and automatically test, report on, and evaluate them. This achievement demonstrates AI's growing capacity for scientific contribution and signifies a potential paradigm shift in how research is conducted. As with any impactful new technology, there could be significant risks, including taxing overwhelmed review systems and adding noise to scientific literature. However, if developed responsibly, such autonomous systems could greatly accelerate scientific discovery.

Yutaro Yamada, Robert Tjarko Lange, Cong Lu +5

Sakana AI, Tokyo, Japan · University of British Columbia, Vancouver, Canada · Vector Institute, Toronto, Canada +1

AI-Assisted Scientific ResearchAutonomous Scientific DiscoveryAutonomous Research AgentsScientific Workflow Automation
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Mar 30, 2026

Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research

Current Autonomous Scientific Research (ASR) systems, despite leveraging large language models (LLMs) and agentic architectures, remain constrained by fixed workflows and toolsets that prevent adaptation to evolving tasks and environments. We introduce Mimosa, an evolving multi-agent framework that automatically synthesizes task-specific multi-agent workflows and iteratively refines them through experimental feedback. Mimosa leverages the Model Context Protocol (MCP) for dynamic tool discovery, generates workflow topologies via a meta-orchestrator, executes subtasks through code-generating agents that invoke available tools and scientific software libraries, and scores executions with an LLM-based judge whose feedback drives workflow refinement. On ScienceAgentBench, Mimosa achieves a success rate of 43.1% with DeepSeek-V3.2, surpassing both single-agent baselines and static multi-agent configurations. Our results further reveal that models respond heterogeneously to multi-agent decomposition and iterative learning, indicating that the benefits of workflow evolution depend on the capabilities of the underlying execution model. Beyond these benchmarks, Mimosa modular architecture and tool-agnostic design make it readily extensible, and its fully logged execution traces and archived workflows support auditability by preserving every analytical step for inspection and potential replication. Combined with domain-expert guidance, the framework has the potential to automate a broad range of computationally accessible scientific tasks across disciplines. Released as a fully open-source platform, Mimosa aims to provide an open foundation for community-driven ASR.

Martin Legrand, Tao Jiang, Matthieu Feraud +5

Université Côte d’Azur, CNRS, ICN, Nice, France · Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d’Azur, Sophia-Antipolis, France · Inria, Université Côte d’Azur, CNRS, I3S, France

Autonomous Research AgentsMulti-Agent SystemsLLM Agent Workflow OptimizationMulti-Agent Orchestration
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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 29, 2026

Sci-Mind: Cognitively-Inspired Adversarial Debate for Autonomous Mathematical Modeling

Real-world mathematical modeling is inherently an experiential and collaborative endeavor. Domain experts rarely solve complex problems from scratch; instead, they draw upon analogies from historical cases and subject their hypotheses to rigorous peer scrutiny. However, autonomous agents powered by Large Language Models predominantly rely on isolated reasoning paradigms, frequently generating plausible but fundamentally flawed models due to a lack of domain grounding and adversarial verification. To address these limitations, we propose Sci-Mind, a novel framework that mirrors the human scientific discovery process. Sci-Mind integrates Experiential Memory Recall to retrieve executable code snippets and modeling paradigm descriptors, grounding abstract reasoning in historical solutions. Subsequently, it employs an Adversarial Cognitive Dialectic where a Theorist optimizing mathematical coherence and a Pragmatist enforcing data feasibility debate through competing objectives to prune elegant but infeasible formulations. A Self-Validating Execution Strategy further ensures blueprint consistency through formal predicates before code generation, achieving fully autonomous execution. Extensive experiments on the MM-Bench and EngiBench demonstrate that Sci-Mind significantly outperforms leading autonomous agents in both modeling rigorousness and code executability.

Junhao Jia, Huangwei Chen, Ruiying Sun +4

Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science and Technology, Zhejiang University, China · Hangzhou Dianzi University, China · Hangzhou Normal University, China

Autonomous Research AgentsLLM Mathematical ReasoningMulti-Agent LLMsMemory-Augmented Language Model AgentsMulti-Agent Debate
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Mar 28, 2026

Agent-Driven Autonomous Reinforcement Learning Research: Iterative Policy Improvement for Quadruped Locomotion

This paper documents a case study in agent-driven autonomous reinforcement learning research for quadruped locomotion. The setting was not a fully self-starting research system. A human provided high-level directives through an agentic coding environment, while an agent carried out most of the execution loop: reading code, diagnosing failures, editing reward and terrain configurations, launching and monitoring jobs, analyzing intermediate metrics, and proposing the next wave of experiments. Across more than 70 experiments organized into fourteen waves on a DHAV1 12-DoF quadruped in Isaac Lab, the agent progressed from early rough-terrain runs with mean reward around 7 to a best logged Wave 12 run, exp063, with velocity error 0.263 and 97% timeout over 2000 iterations, independently reproduced five times across different GPUs. The archive also records several concrete autonomous research decisions: isolating PhysX deadlocks to terrain sets containing boxes and stair-like primitives, porting four reward terms from openly available reference implementations \cite{deeprobotics, rlsar}, correcting Isaac Sim import and bootstrapping issues, reducing environment count for diagnosis, terminating hung runs, and pivoting effort away from HIM after repeated terrain=0.0 outcomes. Relative to the AutoResearch paradigm \cite{autoresearch}, this case study operates in a more failure-prone robotics RL setting with multi-GPU experiment management and simulator-specific engineering constraints. The contribution is empirical and documentary: it shows that an agent can materially execute the iterative RL research loop in this domain with limited human intervention, while also making clear where human direction still shaped the agenda.

Nimesh Khandelwal, Shakti S. Gupta

Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, India

Reinforcement LearningRobotic RLRL for Legged LocomotionAutonomous Research Agents
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Mar 28, 2026

Self-evolving AI agents for protein discovery and directed evolution

Protein scientific discovery is bottlenecked by the manual orchestration of information and algorithms, while general agents are insufficient in complex domain projects. VenusFactory2 provides an autonomous framework that shifts from static tool usage to dynamic workflow synthesis via a self-evolving multi-agent infrastructure to address protein-related demands. It outperforms a set of well-known agents on the VenusAgentEval benchmark, and autonomously organizes the discovery and optimization of proteins from a single natural language prompt.

Yang Tan, Lingrong Zhang, Mingchen Li +5

Shanghai National Center for Applied Mathematics (SJTU Center) & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China. · Shanghai Innovation Institute, Shanghai, 200231, China. · School of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China. +1

Autonomous Research AgentsSelf-Evolving AgentsLLM Agent Workflow OptimizationProtein Design
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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 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 24, 2026

Bilevel Autoresearch: Meta-Autoresearching Itself

If autoresearch is itself a form of research, then autoresearch can be applied to research itself. We present Bilevel Autoresearch, a bilevel framework in which an outer autoresearch loop improves an inner autoresearch loop by reading its code and traces, identifying bottlenecks, and generating injectable Python search mechanisms at runtime. The inner loop optimizes task performance; the outer loop optimizes how the inner loop searches. Both loops use the same LLM, so improvements come from the bilevel architecture rather than a stronger meta-level model, although the outer loop consumes additional inference and wall-clock budget. On Karpathy's GPT pretraining benchmark, the meta-autoresearch outer loop achieves a 5x improvement over the standard inner loop alone (-0.045 vs. -0.009 val_bpb), while parameter-level adjustment without mechanism change yields no reliable gain. The outer loop instantiates mechanisms from adjacent search domains, including combinatorial optimization, multi-armed bandits, and design of experiments, without human specification of the final mechanism design. Trace analysis suggests that these mechanisms break deterministic search patterns and force exploration of directions the LLM's priors avoid. The experiments demonstrate, on this benchmark, a first bilevel step: an outer loop improves the search behavior of an inner loop. Code is the mechanism carrier in this implementation, but skills, prompts, workflows, evaluators, domain principles, world-model assumptions, and memory schemas can also encode mechanisms that shape future agent behavior. This suggests a path toward recursive bootstrapping, where mechanisms discovered for the inner loop can be fed back to improve the meta-level loop itself.

Yaonan Qu, Meng Lu

Independent Researcher

Self-Improving AgentsAutonomous Research AgentsLarge Language Model-Guided OptimizationAutomated Algorithm DiscoveryBilevel Optimization
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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 22, 2026

Improving Coherence and Persistence in Agentic AI for System Optimization

Designing high-performance system heuristics is a creative, iterative process requiring experts to form hypotheses and execute multi-step conceptual shifts. While Large Language Models (LLMs) show promise in automating this loop, they struggle with complex system problems due to two critical failure modes: evolutionary neighborhood bias and the coherence ceiling. Evolutionary methods often remain trapped in local optima by relying on scalar benchmark scores, failing when coordinated multi-step changes are required. Conversely, existing agentic frameworks suffer from context degradation over long horizons or fail to accumulate knowledge across independent runs. We present Engram, an agentic researcher architecture that addresses these limitations by decoupling long-horizon exploration from the constraints of a single context window. Engram organizes exploration into a sequence of agents that iteratively design, test, and analyze mechanisms. At the conclusion of each run, an agent stores code snapshots, logs, and results in a persistent Archive and distills high-level modeling insights into a compact, persistent Research Digest. Subsequent agents then begin with a fresh context window, reading the Research Digest to build on prior discoveries. We find that Engram exhibits superior performance across diverse domains including multi-cloud multicast, LLM inference request routing, and optimizing KV cache reuse in databases with natural language queries.

Pantea Karimi, Kimia Noorbakhsh, Mohammad Alizadeh +1

MIT Cambridge, USA

Long-Horizon LLM AgentsAutonomous Research AgentsPersistent Memory for Language Models
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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 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 18, 2026

EDM-ARS: A Domain-Specific Multi-Agent System for Automated Educational Data Mining Research

In this technical report, we present the Educational Data Mining Automated Research System (EDM-ARS), a domain-specific multi-agent pipeline that automates end-to-end educational data mining (EDM) research. We conceptualize EDM-ARS as a general framework for domain-aware automated research pipelines, where educational expertise is embedded into each stage of the research lifecycle. As a first instantiation of this framework, we focus on predictive modeling tasks. Within this scope, EDM-ARS orchestrates five specialized LLM-powered agents (ProblemFormulator, DataEngineer, Analyst, Critic, and Writer) through a state-machine coordinator that supports revision loops, checkpoint-based recovery, and sandboxed code execution. Given a research prompt and a dataset, EDM-ARS produces a complete LaTeX manuscript with real Semantic Scholar citations, validated machine learning analyses, and automated methodological peer review. We also provide a detailed description of the system architecture, the three-tier data registry design that encodes educational domain expertise, the specification of each agent, the inter-agent communication protocol, and mechanisms for error-handling and self-correction. Finally, we discuss current limitations, including single-dataset scope and formulaic paper output, and outline a phased roadmap toward causal inference, transfer learning, psychometric, and multi-dataset generalization. EDM-ARS is released as an open-source project to support the educational research community.

Chenguang Pan, Zhou Zhang, Weixuan Xiao +1

edmars.ai New York, NY, USA

Autonomous Research AgentsEducational Data MiningMulti-Agent LLM Systems
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Mar 18, 2026

Competing with AI Scientists: Agent-Driven Approach to Astrophysics Research

We present an agent-driven approach to the construction of parameter inference pipelines for scientific data analysis. Our method leverages a multi-agent system, Cmbagent (the analysis system of the AI scientist Denario), in which specialized agents collaborate to generate research ideas, write and execute code, evaluate results, and iteratively refine the overall pipeline. As a case study, we apply this approach to the FAIR Universe Weak Lensing Uncertainty Challenge, a competition under time constraints focused on robust cosmological parameter inference with realistic observational uncertainties. While the fully autonomous exploration initially did not reach expert-level performance, the integration of human intervention enabled our agent-driven workflow to achieve a first-place result in the challenge. This demonstrates that semi-autonomous agentic systems can compete with, and in some cases surpass, expert solutions. We describe our workflow in detail, including both the autonomous and semi-autonomous exploration by Cmbagent. Our final inference pipeline utilizes parameter-efficient convolutional neural networks, likelihood calibration over a known parameter grid, and multiple regularization techniques. Our results suggest that agent-driven research workflows can provide a scalable framework to rapidly explore and construct pipelines for inference problems.

Thomas Borrett, Licong Xu, Andy Nilipour +7

Cavendish Astrophysics, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK · Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK · Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK +4

Scientific Workflow AutomationAutonomous Research AgentsMulti-Agent CollaborationHuman-in-the-Loop AI
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Mar 17, 2026

ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments

With the advent of AI agents, automated scientific discovery is becoming an increasingly plausible goal. However, training agents to autonomously execute the engineering-heavy labor of machine learning (ML) research requires massive, process-level supervision. Existing static benchmarks omit critical intermediate steps such as debugging and incremental reasoning, and manual data collection is prohibitively expensive. To overcome this data bottleneck, we introduce ML-AutoResearch (ML-AR), a scalable pipeline for automatically generating synthetic, end-to-end ML research tasks. Each task defines a complete research cycle, including problem specification, dataset selection, baseline implementation, and iterative improvement. To ensure realism and executability, tasks are grounded in real-world datasets and refined via an automated self-debugging procedure without requiring human supervision. We construct a large-scale dataset of teacher trajectories on these synthetic tasks to train student agents via supervised fine-tuning. We evaluate the resulting agents across 3 diverse ML research benchmarks. Our comprehensive experiments across 2 distinct model families and 3 model sizes demonstrate that training on ML-AR trajectories yields consistent and significant capability gains. Fine-tuning improves the Aggregation Under the Performance (AUP) by up to 9% and substantially boosts overall pass rates, highlighting robust out-of-domain generalization.

Ziyang Cai, Amir Saeidi, Harkirat Behl

Arizona State University · Microsoft Research

Autonomous Research AgentsLLM Agent TrainingSynthetic Benchmark GenerationLLM Agent Trajectory SynthesisAI Agent Benchmarks
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Mar 17, 2026

Nonstandard Errors in AI Agents

We study whether state-of-the-art AI coding agents, given the same data and research question, produce the same empirical results. Deploying 150 autonomous Claude Code agents to independently test six hypotheses about market quality trends in NYSE TAQ data for SPY (2015--2024), we find that AI agents exhibit sizable nonstandard errors (NSEs), that is, uncertainty from agent-to-agent variation in analytical choices, analogous to those documented among human researchers. AI agents diverge substantially on measure choice (e.g., autocorrelation vs.\ variance ratio, dollar vs.\ share volume). Different model families (Sonnet 4.6 vs.\ Opus 4.6) exhibit stable ``empirical styles,'' reflecting systematic differences in methodological preferences. In a three-stage feedback protocol, AI peer review (written critiques) has minimal effect on dispersion, whereas exposure to top-rated exemplar papers reduces the interquartile range of estimates by 80--99% within converging measure families. Convergence occurs both through within-family estimation tightening and through agents switching measure families entirely, but convergence reflects imitation rather than understanding. These findings have implications for the growing use of AI in automated policy evaluation and empirical research.

Ruijiang Gao, Steven Chong Xiao

Naveen Jindal School of Management, University of Texas at Dallas

AI Agent ReliabilityAutonomous Research AgentsAI Coding AgentsAI Agent Evaluation
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