Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks. Yet whether these systems can effectively engage in forms of inductive reasoning relevant to scientific discovery remains an open question. In this work, we introduce FALSIFYBENCH, an evaluation framework for hypothesis-driven reasoning inspired by the classic Wason 2-4-6 task, in which agents must discover hidden semantic properties by iteratively proposing examples and receiving feedback. This task captures key elements of scientific reasoning: hypothesis generation, evidence gathering, and belief revision in response to both confirming and disconfirming evidence. Our evaluation of 12 LLMs across model families and scales shows that reasoning models are generally stronger scientific reasoners than instruction-tuned models, although no model comes close to optimal performance. The primary driver of success is the capacity for negative testing: models that actively seek to falsify their hypotheses consistently outperform those that primarily seek confirmation. Moreover, a fine-grained turn-level analysis, neglected in previous work, reveals that failure is tied to identifiable patterns in how models navigate the hypothesis space.
MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solving
Florida International University（佛罗里达国际大学）；SRI International（SRI国际（斯坦福国际研究院））；United States Military Academy（美国军事学院（西点军校））；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Central Florida（中佛罗里达大学）；Vanderbilt University（范德堡大学）
Investigating Advanced Reasoning of Large Language Models via Black-Box Interaction
College of Artificial Intelligence, Nanjing University of Aeronautics（人工智能学院，南京航空航天大学）；School of Electronics Engineering（电子工程学院）；Computer Science, Peking University（计算机科学，北京大学）；Theta Health Inc.（Theta健康公司）
SciR: A Controllable Benchmark for Scientific Reasoning in LLMs
Idiap Research Institute（Idiap研究 institute）；EPFL（瑞士联邦理工学院）；School of Computer Science, University of Sheffield（谢菲尔德大学计算机科学学院）；University of Manchester（曼彻斯特大学）；National Biomarker Centre, CRUK Manchester Institute（国家生物标志物中心，CRUK曼彻斯特研究所）
MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所认知与决策智能复杂系统重点实验室）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学交叉学科学院）
National Kapodistrian University of Athens（雅典国立卡波季斯特里安大学）；University of Piraeus（比雷埃夫斯大学）；National Center for Scientific Research “Demokritos”（德谟克利特国家科学研究中心）
Evaluating AI Alignment in LLMs: Output Analysis of Value Priorities Across 75 Models with Human Benchmarking
School of Social Sciences, Nanyang Technological University（南洋理工大学社会科学学院）；Interdisciplinary Graduate Programme, Nanyang Technological University（南洋理工大学跨学科研究生项目）；Faculty of Arts and Social Sciences, National University of Singapore（新加坡国立大学人文与社会科学学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理学院计算与信息学院）；School of Social Sciences, Singapore Management University（新加坡管理学院社会科学学院）
Playing ZendoWorld: Challenging AI Agents on Active Visual Concept Induction
AIML Lab, TU Darmstadt（人工智能机器学习实验室，达姆施塔特工业大学）；Hessian Center for AI (hessian.AI)（黑森州人工智能中心）；Psychology of Information Processing, TU Darmstadt（信息处理心理学，达姆施塔特工业大学）；Centre for Cognitive Science, TU Darmstadt（认知科学中心，达姆施塔特工业大学）；Cornell University（康奈尔大学）；Max Planck Institute for Informatics, SIC（马克斯·普朗克信息学研究所，SIC组）；German Center for AI (DFKI)（德国人工智能研究中心）
