Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics
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. However, these approaches neglect the essence of the scientific reasoning process -- logicality, which is the rational foundation to ensure the validity of reasoning steps leading to reliable conclusions. In this work, we make the first systematic investigation into the internal logicality underlying LLM scientific reasoning, and develop a ...
| Authors | Zhaoxin Yu, Nan Xu, Kun Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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