Scientific Reasoning

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1 paper in the last four weeks, down 75% on the four weeks before. 0.0% of all new papers.

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SciPaths: Forecasting Pathways to Scientific Discovery
May 14, 2026Eric Chamoun, Yizhou Chi, Yulong Chen +4Scientific Reasoning Scientific Discovery
University of Cambridge, Cambridge, United Kingdom · Singapore University of Technology and Design, Singapore · The Alan Turing Institute, London, United Kingdom
MaD Physics: Evaluating information seeking under constraints in physical environments
May 11, 2026Moksh Jain, Mehdi Bennani, Johannes Bausch +4Scientific Reasoning AI Agent Benchmarks
Google DeepMind · Mila – Quebec AI Institute, Université de Montréal · Work done during an internship at Google DeepMind
ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning
May 11, 2026Wanghan Xu, Yuhao Zhou, Hengyuan Zhao +8LLM Sycophancy Scientific Reasoning
Shanghai Jiao Tong University · Shanghai Artificial Intelligence Laboratory · This work was done during his internship at Shanghai Artificial Intelligence Laboratory. +4
Bolek: A Multimodal Language Model for Molecular Reasoning
May 4, 2026Frederic Grabowski, Jacek Szczerbiński, Maciej Jaśkowski +4LLM Grounding Scientific Reasoning
1Ingenix.ai, Warsaw, Poland · Laboratory of Bioinformatics and Computational Genomics, Warsaw University of Technology, Warsaw, Poland
SciResearcher: Scaling Deep Research Agents for Frontier Scientific Reasoning
May 2, 2026Tianshi Zheng, Rui Wang, Xiyun Li +5AI for Science Deep Research Agents
HKUST · CUHK · Tencent AI Lab
LSR-Ben: A Logical and Scientific Reasoning Benchmark for Evaluating Process Reward Models
May 2, 2026Zhouhao Sun, Xuan Zhang, Xiao Ding +10Logical Reasoning Process Reward Models
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, China · Beijing Academy of Artificial Intelligence, Beijing, China
In-Context Examples Suppress Scientific Knowledge Recall in LLMs
Apr 30, 2026Chaemin Jang, Woojin Park, Hyeok Yun +2Knowledge Conflicts in Language Models Scientific Reasoning
Korea Advanced Institute of Science and Technology · Shanghai Jiao Tong University
AI scientists produce results without reasoning scientifically
Apr 20, 2026Martiño Ríos-García, Nawaf Alampara, Chandan Gupta +5AI Agent Reliability Scientific Reasoning
Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldstr. 10, 07743 Jena, Germany · Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India · School of Interdisciplinary Research, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India +3
A Multitask Large Reasoning Model for Molecular Science
Mar 13, 2026Pengfei Liu, Shuang Ge, Xiaobo Wang +6Scientific Reasoning Multi-Task Learning
Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, Southern Medical University, Guangzhou, Guangdong, 510515, China · Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518000, China · Guangdong Provincial Key Laboratory of Psychoactive Substances Monitoring and Safety, Anti-Drug Technology Center of Guangdong Province (National Anti-Drug Laboratory Guangdong Regional Center), Guangzhou, 510230, China +4

May 14, 2026, cs.CLJ/K move · Enter open · S save

SciPaths: Forecasting Pathways to Scientific Discovery

Eric Chamoun, Yizhou Chi, Yulong Chen, Rui Cao+3

University of Cambridge, Cambridge, United Kingdom · Singapore University of Technology and Design, Singapore · The Alan Turing Institute, London, United Kingdom

Scientific Reasoning Scientific Discovery Forecasting Benchmarks Large Language Model-Guided Scientific Discovery

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Scientific progress depends on sequences of enabling contributions, yet existing AI4Science benchmarks largely focus on citation prediction, literature retrieval, or idea generation rather than the dependencies that make progress possible. In this paper, we introduce discovery pathway forecasting: given a target scientific contribution and the prior literature available at a specified time, the task is to (1) identify the enabling contributions required to realize it and (2) ground each in prior work when such prior work exists. We present SciPaths, a benchmark of 262 expert-annotated gold pathways and 2,444 silver pathways constructed from machine learning and natural language processing papers, where each pathway records enabling contributions, roles, rationales, and prior-work groundings or unmapped decisions. Evaluating frontier and open-weight language models, we find that the best model reaches only 0.189 F1 under strict semantic matching, with core methodological dependencies hardest to recover. Prior-work grounding improves substantially when gold enabling contributions are provided, showing that decomposition quality is a major bottleneck for end-to-end pathway recovery. SciPaths therefore shifts evaluation toward a missing capability in scientific forecasting: reasoning backward from a target contribution to the enabling scientific building blocks and prior-work dependencies that make it feasible.

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