Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics
A new methodology for enhancing reasoning in Large Language Models (LLMs) has been proposed, focusing on the importance of logicality in scientific reasoning. The research emphasizes the need for logicality to ensure valid reasoning steps and reliable conclusions. By applying this methodology to physics, the authors demonstrate improvements in logical faithfulness and task performance in LLMs.
- ▪The study investigates the internal logicality of LLM scientific reasoning.
- ▪A methodology was developed to improve logicality in LLMs through assessment criteria and data sampling methods.
- ▪Experiments showed that enriched scientific logicality significantly aids in solving scientific problems.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17104 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.17104 (cs) [Submitted on 16 May 2026] Title:Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics Authors:Zhaoxin Yu, Nan Xu, Kun Chen, Jiahao Zhao, Lei Wang, Wenji Mao View a PDF of the paper titled Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics, by Zhaoxin Yu and 4 other authors View PDF HTML (experimental) Abstract:With the continuous advancement of reasoning abilities in Large Language Models (LLMs), their application to scientific reasoning tasks has gained significant research attention. Current research primarily emphasizes boosting LLMs' performance on scientific QA benchmarks by training on larger, more comprehensive datasets with extended reasoning chains.
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