Large Language Model-Guided Scientific Discovery

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The field studies how large language models can help turn scientific literature and data into hypotheses, mechanisms, algorithms, and new materials, making discovery more systematic and widening the range of ideas scientists can test. Common approaches combine literature retrieval or knowledge graphs with tool-using agents, symbolic regression, evolutionary search, Bayesian optimization, and feedback from simulations or experiments. Work spans research-idea generation, equation and mechanism discovery, experimental planning, and materials or molecular design, with evaluation ranging from scientific benchmarks and formal verification to closed-loop laboratory tests. A central challenge is ensuring that promising outputs are novel, physically or biologically grounded, reproducible, and generalizable: strong predictive fit or fluent explanations alone do not establish a valid discovery, so independent checks and human oversight remain important.

Latest papers

278 papers49 papers in the last four weeks, up 206% on the four weeks before. 0.3% of all new papers.

Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
May 1, 2026Fang Wu, Weihao Xuan, Heli Qi +26Molecular Generation Generative Modeling
Stanford University
Models Recall What They Violate: Constraint Adherence in Multi-Turn LLM Ideation
Apr 30, 2026Garvin KruthofLLM Evaluation LLM Alignment
Technical University of Munich
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
METASYMBO: Multi-Agent Language-Guided Metamaterial Discovery via Symbolic Latent Evolution
Apr 30, 2026Jianpeng Chen, Wangzhi Zhan, Dongqi Fu +5Materials Science Large Language Model-Guided Scientific Discovery
Virginia Tech · Meta AI · University of California Los Angeles +2
Agentic Fusion of Large Atomic and Language Models to Accelerate Superconductor Discovery
Apr 26, 2026Mingze Li, Yu Rong, Songyou Li +16AI Agents for Scientific Discovery Materials Property Prediction
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China. · DAMO Academy, Alibaba Group, Hangzhou, China. · Hupan Lab, Hangzhou, China. +2
Doubly Saturated Ramsey Graphs: A Case Study in Computer-Assisted Mathematical Discovery
Apr 23, 2026Benjamin Przybocki, John Mackey, Marijn J. H. Heule +1Large Language Model-Guided Scientific Discovery
Carnegie Mellon University, Pittsburgh, USA
Generative Discovery of Magnetic Insulators under Competing Physical Constraints
Apr 22, 2026Qiulin Zeng, Tahiya Chowdhury, Md Shafayat HossainMaterials Science Crystal Structure Generation
Department of Math, University of Southern California, Los Angeles, CA, 90007 · Department of Computer Science, Colby College, Waterville, ME, 04901 · Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States +1
Expanding the extreme-k dielectric materials space through physics-validated generative reasoning
Apr 22, 2026Hossain Hridoy, Tahiya Chowdhury, Md Shafayat HossainMaterials Science Large Language Model-Guided Scientific Discovery
Department of Chemical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. · Department of Computer Science, Colby College, Waterville, ME, USA. · Department of Materials Science and Engineering, University of California, Los Angeles, CA, USA. +2
Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies
Apr 22, 2026Shuai Chen, Chengzhi ZhangMulti-Agent LLM Systems Computational Creativity
Department of Information Management, Nanjing University of Science and Technology, Nanjing, 210094 China
LLM-guided phase diagram construction through high-throughput experimentation
Apr 22, 2026Ryo Tamura, Haruhiko Morito, Yuna Oikawa +7Materials Science Large Language Model-Guided Scientific Discovery
Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan · Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan · RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan +8
Evaluation-driven Scaling for Scientific Discovery
Apr 21, 2026Haotian Ye, Haowei Lin, Jingyi Tang +22Large Language Model-Guided Optimization Inference-Time Scaling
Wizard Intelligence Learning Lab, Stanford University · Peking University, Tsinghua University
ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis
Apr 18, 2026Hao Wang, Jindong Han, Wei Fan +1AI Agents for Scientific Discovery Climate Science
1The Hong Kong University of Science and Technology (Guangzhou) · 3Shandong University · University of Auckland +1
Debate as Reward: A Multi-Agent Reward System for Scientific Ideation via RL Post-Training
Apr 17, 2026Moein Salimi, Babak Hosseini Mohtasham, Amin Aghakasiri +6Reward Hacking RL for Language Models
Sharif University of Technology · University of Tehran · Sharif University of Technology[cs.AI]
Large Language Models Meet Biomedical Knowledge Graphs for Mechanistically Grounded Therapeutic Prioritization
Apr 17, 2026Chih-Hsuan Wei, Chi-Ping Day, Zhizheng Wang +8LLM Grounding Large Language Model-Guided Scientific Discovery
Division of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH); Bethesda, MD 20894, USA · Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20894, USA · Dartmouth Health, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA +2
PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research
Apr 16, 2026Tingjia Miao, Wenkai Jin, Muhua Zhang +19LLM Evaluation AI Agent Benchmarks
School of Artificial Intelligence, Shanghai Jiao Tong University · Zhiyuan College, Shanghai Jiao Tong University · School of Physics and Astronomy, Shanghai Jiao Tong University +7
Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling
Apr 14, 2026Xiaoxiao Liang, Juyuan Zhang, Liming Pan +1Latent Dynamics Modeling Symbolic Regression
School of Cyber Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.
Continuous Knowledge Metabolism: Generating Scientific Hypotheses from Evolving Literature
Apr 14, 2026Jinkai Tao, Yubo Wang, Xiaoyu Liu +1Scientific Discovery Scientific Hypothesis Generation
TsingyuAI, Beijing, China · School of Information, Central University of Finance and Economics, Beijing, China · Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China +1
Agentic LLM Reasoning in a Self-Driving Laboratory for Air-Sensitive Lithium Halide Spinel Conductors
Apr 13, 2026Yuxing Fei, Bernardus Rendy, Xiaochen Yang +7Autonomous Experimentation Laboratory Automation
Department of Materials Science & Engineering, University of California, Berkeley, Berkeley, California 94720, United States · Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States · Energy Storage Research Alliance, Argonne National Laboratory, Lemont, Illinois 60439, United States +2
The AI Telco Engineer: Toward Autonomous Discovery of Wireless Communications Algorithms
Apr 11, 2026Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer +3Automated Algorithm Discovery Wireless Communications
All authors are with NVIDIA.
GIANTS: Generative Insight Anticipation from Scientific Literature
Apr 10, 2026Joy He-Yueya, Anikait Singh, Ge Gao +5Large Language Model-Guided Scientific Discovery Research Idea Generation
Stanford University · New York University
Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents
Apr 10, 2026Maochen Sun, Youzhi Zhang, Gaofeng MengDrug Discovery Memory-Augmented Language Model Agents
Institute of Automation, Chinese Academy of Sciences, China · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, China · Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, China
Epistemic Blinding: An Inference-Time Protocol for Auditing Prior Contamination in LLM-Assisted Analysis
Apr 7, 2026Michael CuccareseLLM Auditing Data Contamination in Language Models
Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation
Apr 6, 2026Srujan P Mule, Aniketh Garikaparthi, Manasi PatwardhanScientific Idea Evaluation Large Language Model-Guided Scientific Discovery
IISER Pune · TCS Research
InferenceEvolve: Towards Automated Causal Effect Estimators through Self-Evolving AI
Apr 5, 2026Can Wang, Hongyu Zhao, Yiqun ChenCausal Effect Estimation Automated Algorithm Discovery
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA · Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
The limits of bio-molecular modeling with large language models : a cross-scale evaluation
Apr 3, 2026Yaxin Xu, Yue Zhou, Tianyu Zhao +2LLM Evaluation Large Language Model-Guided Scientific Discovery
Southern University of Science and Technology, Shenzhen, 518055, China. · Pengcheng Laboratory, Shenzhen, 518055, China. · Institute of Mechanics, Chinese Academy of Sciences, Beijing, 100190, China.
From Data to Theory: Autonomous Large Language Model Agents for Materials Science
Apr 1, 2026Samuel Onimpa Alfred, Veera SundararaghavanLarge Language Model-Guided Scientific Discovery Scientific Discovery
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109
Bethe Ansatz with a Large Language Model
Mar 31, 2026Balázs Pozsgay, István VonaQuantum Many-Body Physics Large Language Model-Guided Scientific Discovery
MTA-ELTE “Momentum” Integrable Quantum Dynamics Research Group, ELTE Eötvös Loránd University, Budapest, Hungary · Holographic Quantum Field Theory Research Group, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary
FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration
Mar 31, 2026Qiyao Wang, Hongbo Wang, Longze Chen +6Research Idea Generation Large Language Model-Guided Scientific Discovery
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Dalian University of Technology +3
Deep Research of Deep Research: From Transformer to Agent, From AI to AI for Science
Mar 30, 2026Yipeng YuDeep Research Agents AI Agents for Scientific Discovery
Learning to Predict Future-Aligned Research Proposals with Language Models
Mar 28, 2026Heng Wang, Pengcheng Jiang, Jiashuo Sun +4LLM Evaluation Research Idea Generation
University of Illinois Urbana-Champaign

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Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao+25

Stanford University

Molecular Generation Generative Modeling 3D Molecular Generation Conditional Generative Modeling Protein Design Large Language Model-Guided Scientific Discovery+1

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Deep learning in de novo protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synthesize molecular geometries without explicitly reasoning about which residues or interactions are functionally essential. As a result, design decisions are entangled with continuous sampling dynamics, limiting interpretability, controllability, and systematic reuse of biochemical knowledge. We introduce Proteo-R1, a reasoning-guided protein design framework that explicitly decouples molecular understanding from geometric generation. Proteo-R1 adopts a dual-expert architecture in which a multimodal large language model (MLLM) serves as an understanding expert, analyzing protein sequences, structures, and textual context to identify key functional residues that govern binding and specificity. These residue-level decisions are then passed as hard constraints to a separate diffusion-based generation expert, which performs conditional co-design while respecting the fixed interaction anchors. This factorization mirrors how human experts approach molecular engineering: first, reasoning about critical interactions, then optimizing geometry subject to those constraints. By operationalizing reasoning as explicit residue-level commitments rather than latent textual guidance, Proteo-R1 achieves stable, interpretable, and modular integration of LLM reasoning with state-of-the-art geometric generative models. Code, data, and demos are available at https://smiles724.github.io/r1/.