[2604.03893] FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning

Computer Science > Artificial Intelligence arXiv:2604.03893 (cs) [Submitted on 4 Apr 2026] Title:FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning Authors:Zeyu Wang, Xiaogang Li, Peiyao Xiao, Qinhao Kong, Ben Wang, Chengliang Xu, Zichao Chen, Bing Zhao, Hu Wei View a PDF of the paper titled FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning, by Zeyu Wang and 8 other authors View PDF HTML (experimental) Abstract:Breakthroughs in frontier theory often depend on the combination of concrete diagrammatic notations with rigorous logic. While multimodal large language models (MLLMs) show promise in general scientific tasks, current benchmarks often focus on local information extraction rather than the global structural logic inherent in formal scientific notations. In this work, we introduce FeynmanBench, the first benchmark centered on Feynman diagram tasks. It is designed to evaluate AI's capacity for multistep diagrammatic reasoning, which requires satisfying conservation laws and symmetry constraints, identifying graph topology, converting between diagrammatic and algebraic representations, and constructing scattering amplitudes under specific conventions and gauges. To support large-scale and reproducible evaluation, we developed an automated pipeline producing diverse Feynman diagrams along with verifiable topological annotations and amplitude results. Our database spans the electromagnetic, weak, and strong interactions ...