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

• URL: http://arxiv.org/abs/2505.19099v4
• Date: Tue, 17 Jun 2025 14:31:36 GMT
• Title: SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning
• Authors: Kun Xiang, Heng Li, Terry Jingchen Zhang, Yinya Huang, Zirong Liu, Peixin Qu, Jixi He, Jiaqi Chen, Yu-Jie Yuan, Jianhua Han, Hang Xu, Hanhui Li, Mrinmaya Sachan, Xiaodan Liang,
• Abstract summary: We present SeePhys, a large-scale multimodal benchmark for reasoning grounded in physics questions.<n>The benchmark covers 7 fundamental domains spanning the physics discipline, incorporating 21 categories of highly heterogeneous diagrams.<n>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.
• Score: 89.48883747910448
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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