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.
View on arXiv@article{xiang2025_2505.19099,
title={ SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning },
author={ Kun Xiang and Heng Li and Terry Jingchen Zhang and Yinya Huang and Zirong Liu and Peixin Qu and Jixi He and Jiaqi Chen and Yu-Jie Yuan and Jianhua Han and Hang Xu and Hanhui Li and Mrinmaya Sachan and Xiaodan Liang },
journal={arXiv preprint arXiv:2505.19099},
year={ 2025 }
}