PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning

Large language models demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based reasoning - a complex task requiring physics theorems and constraints. We present PhysReason, a 1,200-problem benchmark comprising knowledge-based (25%) and reasoning-based (75%) problems, where the latter are divided into three difficulty levels (easy, medium, hard). Notably, problems require an average of 8.1 solution steps, with hard requiring 15.6, reflecting the complexity of physics-based reasoning. We propose the Physics Solution Auto Scoring Framework, incorporating efficient answer-level and comprehensive step-level evaluations. Top-performing models like Deepseek-R1, Gemini-2.0-Flash-Thinking, and o3-mini-high achieve less than 60% on answer-level evaluation, with performance dropping from knowledge questions (75.11%) to hard problems (31.95%). Through step-level evaluation, we identified four key bottlenecks: Physics Theorem Application, Physics Process Understanding, Calculation, and Physics Condition Analysis. These findings position PhysReason as a novel and comprehensive benchmark for evaluating physics-based reasoning capabilities in large language models. Our code and data will be published athttps:/dxzxy12138.github.io/PhysReason.

@article{zhang2025_2502.12054,
  title={ PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning },
  author={ Xinyu Zhang and Yuxuan Dong and Yanrui Wu and Jiaxing Huang and Chengyou Jia and Basura Fernando and Mike Zheng Shou and Lingling Zhang and Jun Liu },
  journal={arXiv preprint arXiv:2502.12054},
  year={ 2025 }
}