Autonomous Experimentation

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15 papers in the last four weeks, up 114% on the four weeks before. 0.1% of all new papers.

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CARE: Context-Aware Ranking Evolution with Executable Scoring Programs for Budgeted Reaction Optimization
Jun 12, 2026Guanyu Liu, Weiyi Kong, Chao Tang +5Large Language Model-Guided Optimization Program Synthesis
University of Macau · University of Toronto · UCLA +3
Closed-loop discovery of out-of-distribution processing protocols by evolutionary search and uncertainty-aware learning
Jun 11, 2026Yu Liu, Stanislav Udovenko, Ching-Che Lin +4Materials Science Autonomous Experimentation
Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA · Materials Science and Engineering Department, Materials Research Institute, the Pennsylvania State University, University Park, Pennsylvania 16802, USA · Department of Materials Science and NanoEngineering, Rice University, Houston, TX 77005, USA +4
Arbor: Tree Search as a Cognition Layer for Autonomous Agents
Jun 10, 2026Neha Prakriya, Chaojun Hou, Zheng Gong +5LLM Inference Efficiency Multi-Agent System Optimization
Training and Inference Optimization Team AMD
ATLAS: Active Theory Learning for Automated Science
Jun 10, 2026Noémi Éltető, Nathaniel D. Daw, Kimberly L. Stachenfeld +1Cognitive Modeling Active Learning
Google DeepMind London, UK · Princeton University; Google DeepMind Princeton, NJ, USA · Google DeepMind; Colombia University New York, NY, USA +1
The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning
Jun 10, 2026Marko Cvjetko, Benedikt Hartl, Michael Levin +2Agentic RL Reinforcement Learning
Inria Centre at the University of Bordeaux, Bordeaux, France · Allen Discovery Center at Tufts University, Medford, MA, USA · Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA +1
Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
Jun 10, 2026Jiajie Jin, Yuyang Hu, Kai Qiu +15Long-Horizon LLM Agents Autonomous Experimentation
Gaoling School of Artificial Intelligence, Renmin University of China · Microsoft Research
A-Evolve-Training: Autonomous Post-Training of a 30B Model
Jun 9, 2026Zhan Shi, Bing He, Yisi Sang +2LLM Post-Training Language Model Self-Improvement
Amazon
LAP: An Agent-to-Instrument Protocol for Autonomous Science
Jun 2, 2026Linwu Zhu, Liqiang Gao, Yan Chen +2AI Agents for Scientific Discovery Agent Communication Protocols
Shiyanjia Lab
Beyond One-shot: AI Agents for Learning in Field Experiments
Jun 1, 2026Junjie Luo, Ritu Agarwal, Gordon GaoTool-Using Agents Autonomous Experimentation
Johns Hopkins School of Medicine, Baltimore, MD, USA · Carey Business School, Johns Hopkins University, Baltimore, MD, USA
AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation
May 27, 2026Shanghua Gao, Ada Fang, Marinka ZitnikProtein Fitness Prediction Multi-Agent Collaboration
Harvard University
When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery
May 26, 2026Neel Tushar Shah, Manglam KartikModel Misspecification Autonomous Scientific Discovery
Indian Institute of Technology Bombay, Mumbai, India.
GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing
May 26, 2026Tamerlan Aghayev, Maxime Elkael, Michele Polese +116G Networks Wireless Communications
Institute for Intelligent Networked Systems at Northeastern University, Boston, MA.
Claw AI Lab: An Autonomous Multi-Agent Research Team
May 21, 2026Fan Wu, Cheng Chen, Zhenshan Tan +12Multi-Agent LLM Systems LLM Agent Orchestration
1NTU · 2A*STAR · 4NUIST +3
Sibyl-AutoResearch: Autonomous Research Needs Self-Evolving Trial-and-Error Harnesses, Not Paper Generators
May 21, 2026Chengcheng Wang, Qinhua Xie, Wei He +3Scientific Workflow Automation Autonomous Experimentation
University of Sydney · 2East China Normal University · 3TokenRhythm AI +1
Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale
May 20, 2026Ralph Bulanadi, Jefferey Baxter, Arpan Biswas +5Pairwise Preference Learning Active Learning
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA · University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, TN 37996, USA · Department of Material Science and Engineering, School of Materials and Chemical Technology, Institute of Science Tokyo, Yokohama, 226-8502, Japan +3
Qumus: Realization of An Embodied AI Quantum Material Experimentalist
May 18, 2026Lihan Shi, Zhaoyi Joy Zheng, Xinzhe Juan +14AI Agents for Scientific Discovery Materials Science
Department of Physics, Princeton University, Princeton, New Jersey 08544, USA · Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey 08544, USA · Department of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA +5
Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials
May 18, 2026Chih-Yu Lee, Haotong Liang, Ryan Kim +4Materials Science Autonomous Scientific Discovery
Department of Materials Science and Engineering, University of Maryland, College Park, MD, USA · Materials Measurement Science, Division of the National Institute of Standards and Technology, Gaithersburg, MD, USA · Institute of Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland, USA +1
From Prompts to Protocols: An AI Agent for Laboratory Automation
May 15, 2026Angelos Angelopoulos, James F. Cahoon, Ron AlterovitzLLM Agent Orchestration Laboratory Automation
Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA · Department of Chemistry, University of North Carolina at Chapel Hill, NC 27599, USA
LEAP: Trajectory-Level Evaluation of LLMs in Iterative Scientific Design
May 14, 2026Marilyn Zhang, Tianfeng Chen, Fabián Barzuna +2LLM Evaluation AI Agent Benchmarks
NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol
May 13, 2026Naruki Yoshikawa, Ryo TamuraLLM Agent Orchestration Human-in-the-Loop AI
National Institute for Materials Science, Tsukuba, Ibaraki, Japan · Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive
May 12, 2026Taicheng Guo, Nitesh V. Chawla, Olaf Wiest +1Multi-Fidelity Optimization Large Language Model-Guided Optimization
University of Notre Dame
DataMaster: Data-Centric Autonomous AI Research
May 11, 2026Yaxin Du, Xiyuan Yang, Zhifan Zhou +12Autonomous Experimentation Autonomous Research Agents
1Shanghai Jiao Tong University · 2Carnegie Mellon University · 4Beijing University of Aeronautics and Astronautics +1
GEAR: Genetic AutoResearch for Agentic Code Evolution
May 8, 2026Ahmadreza Jeddi, Minh Ngoc Le, Hakki C. Karaimer +2Evolutionary Optimization Autonomous Experimentation
University of Toronto · Vector Institute · AI Center-Toronto, Samsung Electronics +1
LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments
May 7, 2026Boris Slautin, Utkarsh Pratiush, Yu Liu +2Scientific Hypothesis Generation Large Language Model-Guided Scientific Discovery
Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37923, USA
Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes
May 7, 2026Jingjie Ning, Xiaochuan Li, Ji Zeng +2Multi-Agent Systems Self-Improving Agents
School of Computer Science, Carnegie Mellon University
Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
May 6, 2026Zhenning Yang, Yuhan Chen, Patrick Tser Jern Kon +5AI for Science AI Agents for Scientific Discovery
University of Michigan
Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships
May 1, 2026Guillaume LambardMaterials Science Autonomous Experimentation
Data-driven materials design group, Center for Basic Research on Materials (CBRM) National Institute for Materials Science (NIMS), Namiki 1-1, Tsukuba, Ibaraki, 305-0044, Japan
AgentEconomist: An End-to-end Agentic System Translating Economic Intuitions into Executable Computational Experiments
Apr 30, 2026Jiaju Chen, Jinghua Piao, Xia Xu +4AI-Assisted Scientific Research Large Language Model-Guided Scientific Discovery
Zhongguancun Academy Beijing, China · University of Science and Technology of China Hefei, China · Department of Electronic Engineering, Tsinghua University Beijing, China +1
End-to-end autonomous scientific discovery on a real optical platform
Apr 29, 2026Shuxing Yang, Fujia Chen, Rui Zhao +10Autonomous Scientific Discovery Long-Horizon LLM Agents
State Key Laboratory of Extreme Photonics and Instrumentation, College of Information Science and Electronic Engineering, ZJU-Hangzhou Global Scientific and Technological Innovation Center, The Electromagnetics Academy at Zhejiang University, Zhejiang University, Hangzhou 310027, China · Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Jinhua Institute of Zhejiang University, Zhejiang University, Jinhua 321099, China · Shaoxing Institute of Zhejiang University, Zhejiang University, Shaoxing 312000, China +3
Automated Adversarial Collaboration for Advancing Theory Building in the Cognitive Sciences
Apr 28, 2026Suyog Chandramouli, George Kachergis, Akshay JagadishMulti-Agent LLM Systems Computational Cognitive Modeling
Department of Psychology, Princeton University · Department of Psychology, Stanford University · Princeton AI Lab, Princeton University

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CARE: Context-Aware Ranking Evolution with Executable Scoring Programs for Budgeted Reaction Optimization

Guanyu Liu, Weiyi Kong, Chao Tang, Zeyu Wang+4

University of Macau · University of Toronto · UCLA+3

Large Language Model-Guided Optimization Program Synthesis Autonomous Experimentation

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High-throughput experimentation can evaluate many reaction conditions, yet combinatorial condition spaces still exceed the available experiment budget. This makes experiment selection a sequential decision problem: each new condition must be chosen from limited observations before its outcome is known. LLMs can express task-specific selection logic. A direct recommendation, however, is neither a persistent executable object that can be validated and revised nor an independently auditable decision rule. We introduce CARE, a reference-conditioned controller that separates program synthesis from experiment selection. An LLM writes an executable scoring program that ranks the remaining conditions, while a non-LLM reference policy supplies a numerical candidate and support summary. CARE forms an optional alternative from the program, applies a reference-conditioned intervention gate to compare it with the reference, and records the decision before the selected outcome is revealed. Each new outcome updates controller state and can trigger retention, revision, or regeneration of the active program. This outcome-guided program evolution changes the scoring logic without updating the LLM parameters. In matched offline replay with 30 seeds on eight reaction-optimization tasks, CARE attains the lowest normalized regret, the highest normalized best-so-far AUC, and the highest Top-1% Success@15 among the evaluated methods. These results support using a scoring program written by an LLM as one component of a reference-conditioned optimizer rather than as a standalone experiment selector.

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