用多智能体协作让AI物理学家推理更透明可验证。
Advancing AI-Scientist Understanding: Multi-Agent LLMs with Interpretable Physics Reasoning
- 设计多智能体系统分工处理推理、解释与交互。
- 输出经结构化处理，可生成可执行的物理模型。
- 适合需要可解释性的人机协同科研场景。
大语言模型在物理研究中日益重要，辅助符号运算、数值计算与科学推理。但其输出的可靠性、透明性与可解释性仍是挑战。本文提出一种多智能体语言模型物理学家框架，通过推理、解释与人机交互三模块实现协作。针对物理推理需逻辑严谨、量化准确并符合理论模型的要求，设计由摘要生成器、模型构建者、可视化工具和测试者组成的专用智能体团队，将大模型输出系统化为透明、物理可信的科学模型。案例研究表明，该方法显著提升可解释性，支持系统化验证，增强人机协同解决物理问题与发现新规律的能力。本工作实现了自由形式的大模型推理向可解释、可执行科学模型的跨越，推动更透明、可验证的AI增强型科研。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are playing an increasingly important role in physics research by assisting with symbolic manipulation, numerical computation, and scientific reasoning. However, ensuring the reliability, transparency, and interpretability of their outputs remains a major challenge. In this work, we introduce a novel multi-agent LLM physicist framework that fosters collaboration between AI and human scientists through three key modules: a reasoning module, an interpretation module, and an AI-scientist interaction module. Recognizing that effective physics reasoning demands logical rigor, quantitative accuracy, and alignment with established theoretical models, we propose an interpretation module that employs a team of specialized LLM agents-including summarizers, model builders, visualization tools, and testers-to systematically structure LLM outputs into transparent, physically grounded science models. A case study demonstrates that our approach significantly improves interpretability, enables systematic validation, and enhances human-AI collaboration in physics problem-solving and discovery. Our work bridges free-form LLM reasoning with interpretable, executable models for scientific analysis, enabling more transparent and verifiable AI-augmented research.
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