FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning
Current multimodal benchmarks for scientific reasoning primarily evaluate local information extraction -- models recognize symbols and values and then perform textual inference. They do not assess whether models can reason over the global structural properties of formal diagrams, such as topology, conservation constraints, and the consistent mapping between visual patterns and algebraic expressions. We introduce FeynmanBench, a benchmark of over 2,000 tasks centered on Feynman diagrams spanning the electromagnetic, weak, and strong interactions of the Standard Model. Each instance couples a diagram image with minimal textual conventions and requires models to recover the full physical con...
| Authors | Zeyu Wang, Jingye Xu, Xiaogang Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 April 2026 |
| Last updated at source | 1 June 2026 |
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