2025/04/02 by Yuan Xu, Hana Kimlee, Xu, Yinggan +4 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Human-Computer Interaction (cs.HC) #Semantic Web and Ontologies #Statistical and Computational Modeling #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2504.01911
openalex publication_date 2025/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.