Large Language Models (LLMs) are playing an expanding role in physics research by enhancing reasoning, symbolic manipulation, and numerical computation. However, ensuring the reliability and interpretability of their outputs remains a significant challenge. In our framework, we conceptualize the collaboration between AI and human scientists as a dynamic interplay among three modules: the reasoning module, the interpretation module, and the AI-scientist interaction module. Recognizing that effective physics reasoning demands rigorous logical consistency, quantitative precision, and deep integration with established theoretical models, we introduce the interpretation module to improve the understanding of AI-generated outputs, which is not previously explored in the literature. This module comprises multiple specialized agents, including summarizers, model builders, UI builders, and testers, which collaboratively structure LLM outputs within a physically grounded framework, by constructing a more interpretable science model. A case study demonstrates that our approach enhances transparency, facilitates validation, and strengthens AI-augmented reasoning in scientific discovery.

本研究解决了大型语言模型（LLMs）在物理研究中输出可靠性和可解释性不足的问题。通过引入解释模块来提高对AI生成输出的理解，论文提出了一种新的框架，使得AI与人类科学家的合作更为高效。研究表明，该方法增强了透明度，促进了验证，并加强了科学发现中的AI辅助推理。 

提升AI科学家的理解能力：使大型语言模型像物理学家一样进行可解释推理