SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning

Kun Xiang, HengLi, Terry Jingchen Zhang, Yinya Eleanor Huang, Zirong Liu, Peixin Qu, Jixi He, Jiaqi Chen, Yu-Jie Yuan, Jianhua Han, Hang Xu, Hanhui Li, Mrinmaya Sachan, Xiaodan Liang

SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning: 6 upvotes on Hugging Face Daily Papers, #37 of 76 papers on 2025-05-28. Day-by-day upvote history.

We present SeePhys, a large-scale multimodal benchmark for LLM reasoning grounded in physics questions ranging from middle school to PhD qualifying exams. The benchmark covers 7 fundamental domains spanning the physics discipline, incorporating 21 categories of highly heterogeneous diagrams. In contrast to prior works where visual elements mainly serve auxiliary purposes, our benchmark features a substantial proportion of vision-essential problems (75\%) that mandate visual information extraction for correct solutions. Through extensive evaluation, we observe that even the most advanced visual reasoning models (e.g., Gemini-2.5-pro and o4-mini) achieve sub-60\% accuracy on our benchmark. These results reveal fundamental challenges in current large language models' visual understanding capabilities, particularly in: (i) establishing rigorous coupling between diagram interpretation and physics reasoning, and (ii) overcoming their persistent reliance on textual cues as cognitive shortcuts.

Paper page on Hugging Face · arXiv

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