Large Language Model-Guided Scientific Discovery

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18 papers in the last four weeks, up 80% on the four weeks before. 0.2% of all new papers.

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ARIA: A Causal-Aware Framework for Rescuing LLM Reasoning in Trustworthy Materials Discovery
Jun 21, 2026Yi Cao, Liaoyaqi Wang, Jieneng Chen +3Knowledge Augmentation for Language Models Materials Property Prediction
Learning the ARTS of Search for Automated Discovery
Jun 20, 2026Gurusha Juneja, Arnav Kumar Jain, Deepak Nathani +2Inference-Time Search Automated Algorithm Discovery
University of California, Santa Barbara · Université de Montréal and Mila- Quebec AI Institute
AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces
Jun 17, 2026Zongmin Zhang, Yuyang Lou, Bowen Zhang +6Multi-Agent LLM Systems Materials Science
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Jun Kowloon, Hong Kong 999077, China · Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China · Laboratory of Artificial Chemical Intelligence (LIAC), EPFL, Lausanne, Switzerland +2
LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms
Jun 17, 2026Masaya Hagai, Yuta Suzuki, Tomoya Murata +2Automated Algorithm Discovery Quantum Chemistry
Lattice Lab, Toyota Motor Corporation · Department of Chemistry, Graduate School of Science, Nagoya University · National Institute of Informatics
Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
Jun 16, 2026Ziqi Zhou, Yubo Ye, Sumeet Atul Vadhavka +2Scientific Discovery Digital Twins
Rochester Institute of Technology, Rochester, NY, 14623, USA
SciOrch: Learning to Orchestrate Expert LLMs for Solving Frontier Multimodal Scientific Reasoning Tasks
Jun 14, 2026Jingru Guo, Xiangyuan Xue, Lian Zhang +6LLM Agent Orchestration Multimodal Reasoning
1Imperial College London · 2The Chinese University of Hong Kong · University of Illinois Urbana-Champaign +4
Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks
Jun 12, 2026Li Xia, Baoxun WangScientific Hypothesis Generation Large Language Model-Guided Scientific Discovery
School of Economics and Management, Tsinghua University · Platform & Content Group, Tencent
An LLM System for Autonomous Variational Quantum Circuit Design
Jun 11, 2026Kenya Sakka, Wataru Mizukami, Kosuke MitaraiQuantum Chemistry Quantum Kernel Methods
Center for Quantum Information and Quantum Biology, The University of Osaka, 1-2 Machikaneyama, Toyonaka 560-0043, Japan · Graduate School of Engineering Science, The University of Osaka 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
Towards Diverse Scientific Hypothesis Search with Large Language Models
Jun 9, 2026Haorui Wang, Parshin Shojaee, Kazem Meidani +7Automated Algorithm Discovery Scientific Hypothesis Generation
Georgia Institute of Technology · Virginia Tech · Carnegie Mellon University +3
MASS: Deep Research for Social Sciences with Memory-Augmented Social Simulation
Jun 8, 2026Yongrui Liu, Deyi XiongHuman Behavior Simulation Large Language Model-Based Social Simulation
The International Joint Institute of Tianjin University, Fuzhou, Tianjin University, China · TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China
Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts
Jun 8, 2026Xu Li, Hanzhe Tu, Xun HanGraph Retrieval-Augmented Generation Scientific Hypothesis Generation
Southwest Petroleum University, Chengdu, Sichuan, China · Sichuan Police College, Luzhou, Sichuan, China
DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations
Jun 7, 2026Lei Lin, Ronghao Wang, Chunbao Zhou +2Scientific Reasoning Scientific Hypothesis Generation
Computer Network Information Center, Chinese Academy of Sciences, China
GPT-Micro: A large language paradigm for accelerated, inexpensive, and thermodynamics-consistent discovery of constitutive models in manufacturing
Jun 6, 2026Soumik Dutta, Kiarash Naghavi Khanghah, Sania Shree +4Manufacturing Materials Property Prediction
Department of Mechanical and Aerospace Engineering, Rutgers University, USA · Department of Mechanical, Aerospace & Manufacturing Engineering, University of Connecticut, USA · Edison Welding Institute, USA +2
FunctionEvolve: Structure-Guided Symbolic Regression with LLMs
Jun 5, 2026Zeyu Xia, Jun Zhu, Dong YanGenetic Programming Large Language Model-Guided Scientific Discovery
Bosch Center for Artificial Intelligence · Department of Computer Science and Technology, Tsinghua University · Department of Computer Science and Technology +1
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
Jun 3, 2026Xinyu Pang, Zhanke Zhou, Xuan Li +5Large Language Model-Guided Optimization Large Language Model-Guided Scientific Discovery
Beijing National Research Center for Information Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, P.R. China · TMLR Group, Department of Computer Science, Hong Kong Baptist University · Lenovo Research
Iteris: Agentic Research Loops for Computational Mathematics
Jun 1, 2026Leheng Chen, Zihao Liu, Wanyi He +1Mathematical Reasoning Large Language Model-Guided Scientific Discovery
School of Mathematical Sciences, Peking University · 5Zhongguancun Academy · 2Beijing International Center for Mathematical Research and the New Cornerstone Science Laboratory, Peking University +2
Evolutionary Discovery of Bivariate Bicycle Codes with LLM-Guided Search
Jun 1, 2026Juan Cruz-Benito, Andrew W. Cross, David Kremer +1Automated Algorithm Discovery Quantum Error Correction
IBM Research, IBM T. J. Watson Research Center, NY, USA
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
May 31, 2026Teddy Ferdinan, Bartłomiej Koptyra, Mikołaj Langner +42AI for Science Scientific Reasoning
Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery
May 30, 2026Tyler H. McCormickCausal Identifiability Scientific ML
Departments of Statistics and Sociology, University of Washington, Seattle, WA, USA.
Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials
May 29, 2026Edward W. Staley, Tom Arbaugh, Michael Pekala +6Materials Science Large Language Model-Guided Scientific Discovery
Johns Hopkins Applied Physics Laboratory, Laurel, MD 20723 · Johns Hopkins University, Baltimore, MD 21218
EvoGens: A Population-Based Heuristic Search Framework for Scientific Idea Generation
May 29, 2026Xu Li, Hanzhe Tu, Xinyi Li +3Scientific Hypothesis Generation Research Idea Generation
Southwest Petroleum University, Chengdu, China · Sichuan Police College, Luzhou, China
MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
May 29, 2026Etinosa Osaro, Santosh Adhikari, Stamatia Zavitsanou +2Machine Learning Interatomic Potentials Large Language Model-Guided Optimization
PsiQuantum, 700 Hansen Way, Palo Alto, CA 94304
ProjectionBench: Evaluating Scientific Hypothesis Generation in LLMs Under Progressive Information Disclosure
May 28, 2026A. J. Lew, Y. Cao, M. J. BuehlerLLM Evaluation Scientific Reasoning
Unreasonable Labs · Unreasonable Labs Mountain View, CA 94043, USA
From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals
May 28, 2026Yeyong Yu, Wenya Hu, Xing Wu +1LLM Evaluation Pairwise Preference Evaluation
School of Computer Engineering & Science, Shanghai University, Shanghai, 200444, China. · Center of Materials Informatics and Data Science, Materials Genome2026 Institute, Shanghai University, Shanghai, 200444, China. · Key Laboratory of Silicate Cultural Relics Conservation (Shanghai University), Ministry of Education, China. +1
MOOSE-Copilot: A Web-Based Interactive Assistant for Unified Exploratory and Fine-Grained Scientific Hypothesis Discovery
May 28, 2026Hongran An, Zonglin YangScientific Hypothesis Generation Human-in-the-Loop AI
Central Conservatory of Music · Nanyang Technological University
Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
May 27, 2026Evgeny S. Saveliev, Samuel Holt, Nabeel Seedat +3Genomics Large Language Model-Guided Scientific Discovery
University of Cambridge · Thomson Reuters Foundational Research · U. Colorado, Anschutz Medical Campus +1
MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents
May 27, 2026Thao Nguyen, Heng JiMolecular Optimization Multi-Agent LLM Systems
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign
The Compressive Knowledge Graph Hypothesis: Which Graph Facts Matter for Scientific Hypothesis Generation?
May 26, 2026Shashwat Sourav, Viktoriia Baibakova, Sanjay Das +4LLM Grounding LLM Reasoning with Graphs
1Washington University in St. Louis · 2Oak Ridge National Laboratory · 3Lawrence Berkeley National Laboratory +1
DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking
May 25, 2026Matt L. Wiemann, Lindsay M. Smith, Peter Melchior +4Physical Reasoning Scientific Discovery
Princeton University · Work done during internship at NYU/Polymathic AI. · Boston University +3
LLM-AutoSciLab: Closed-Loop Scientific Discovery via Active Experimentation with LLMs
May 21, 2026Sanchit Kabra, Nikhil Abhyankar, Saaketh Desai +2Scientific Discovery Scientific Hypothesis Generation
1Virginia Tech · 2Sandia National Laboratories

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ARIA: A Causal-Aware Framework for Rescuing LLM Reasoning in Trustworthy Materials Discovery

Yi Cao, Liaoyaqi Wang, Jieneng Chen, Benjamin Van Durme+2

Knowledge Augmentation for Language Models Materials Property Prediction Causal Reasoning in Language Models Materials Science Large Language Model-Guided Scientific Discovery Inverse Design

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Generative models have revolutionized the process of materials discovery, yet they often fail to satisfy underlying physical causality. Through an analysis of Large Language Models (LLMs) augmented with knowledge graphs derived from current literature, we uncover a phenomenon termed contextual tunneling, where models "over-anchor" on narrow, retrieved evidence while suppressing global physical reasoning. To address this problem, we introduce ARIA, a causal-aware framework that conditions knowledge use on mechanistic completeness. ARIA routes each query through a three-tier cascade: (i) direct causal reasoning when complete evidence chains of Process-Structure-Property (PSP) are available, (ii) physics-informed analogical transfer for sparse or novel material systems, and (iii) explicit parametric fallback when external evidence is incomplete. As a proof of concept, we construct a Knowledge Graph (KG) containing 2,839 extracted PSP relations from peer-reviewed articles in the materials literature and evaluate ARIA on forward prediction and inverse design tasks for two-dimensional (2D) materials. ARIA mitigates contextual tunneling, improves over unaugmented and naive KG-augmented baselines, and provides further gains when an online literature search is used for evidence enrichment. Crucially, ARIA produces auditable causal traces, enabling physically grounded and trustworthy AI-assisted materials discovery.

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