Related papers: On LLM-Based Scientific Inductive Reasoning Beyond Equations

On LLM-Based Scientific Inductive Reasoning Beyond Equations

URL: http://arxiv.org/abs/2509.16226v1
Date: Fri, 12 Sep 2025 10:11:52 GMT
Title: On LLM-Based Scientific Inductive Reasoning Beyond Equations
Authors: Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun,
Abstract summary: We propose the task of LLM-Based Scientific Inductive Reasoning Beyond Equations.<n>We introduce a new benchmark, SIRBench-V1, to evaluate the inductive reasoning abilities of LLMs in scientific settings.
Score: 51.61971971921903
License: http://creativecommons.org/licenses/by/4.0/
Abstract: As large language models (LLMs) increasingly exhibit human-like capabilities, a fundamental question emerges: How can we enable LLMs to learn the underlying patterns from limited examples in entirely novel environments and apply them effectively? This question is central to the ability of LLMs in inductive reasoning. Existing research on LLM-based inductive reasoning can be broadly categorized based on whether the underlying rules are expressible via explicit mathematical equations. However, many recent studies in the beyond-equations category have emphasized rule design without grounding them in specific scenarios. Inspired by the parallels between inductive reasoning and human scientific discovery, we propose the task of LLM-Based Scientific Inductive Reasoning Beyond Equations and introduce a new benchmark, SIRBench-V1, to evaluate the inductive reasoning abilities of LLMs in scientific settings. Our experimental results show that current LLMs still struggle with this task, underscoring its difficulty and the need for further advancement in this area.

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