Can Theoretical Physics Research Benefit from Language Agents?

Sirui Lu, Zhijing Jin, Terry Jingchen Zhang, Pavel Kos, Juan Ignacio Cirac, Bernhard Schölkopf

Additional Submission Instructions: For the camera-ready version, please include the author names and affiliations, funding disclosures, and acknowledgements.

Track: Track 1: Original Research/Position/Education/Attention Track

Keywords: LLM Agents, Physics Research, AI4Science

TL;DR: We analyze current LLM capabilities for physics—from mathematical reasoning to code generation--identifying critical gaps in physical intuition, constraint satisfaction, and reliable reasoning.

Abstract: Large Language Models (LLMs) are rapidly advancing across diverse domains, yet their application in theoretical physics remains immature. This position paper argues that LLM agents can potentially help accelerate theoretical, computational, and applied physics when properly integrated with domain knowledge and toolbox. We analyze current LLM capabilities for physics---from mathematical reasoning to code generation---identifying critical gaps in physical intuition, constraint satisfaction, and reliable reasoning. We envision future physics-specialized LLMs that could handle multimodal data, propose testable hypotheses, and design experiments. Realizing this vision requires addressing fundamental challenges: ensuring physical consistency and developing robust verification methods. We call for collaborative efforts between physics and AI communities to advance scientific discovery in physics.

Submission Number: 466