SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics
  Reasoning
Authors: name: Kun Xiang, name: Heng Li, name: Terry Jingchen Zhang, name: Yinya Huang, name: Zirong Liu, name: Peixin Qu, name: Jixi He, name: Jiaqi Chen, name: Yu-Jie Yuan, name: Jianhua Han, name: Hang Xu, name: Hanhui Li, name: Mrinmaya Sachan, name: Xiaodan Liang

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.