PhysUniBench: A Multi-Modal Physics Reasoning Benchmark at Undergraduate Level

Lintao Wang, Encheng Su, Jiaqi Liu, Pengze Li, Jiabei Xiao, Wenlong Zhang, Xi Chen, Yuan Meng, LEI BAI, Wanli Ouyang, SHIXIANG TANG, Aoran Wang, Xinzhu Ma

Keywords: physics, reasoning, benchmark, large language model, multi-modal large language model

Abstract: Physics problem-solving is a challenging domain for large AI models, requiring integration of conceptual understanding, mathematical reasoning, and interpretation of physical diagrams. Existing evaluations fail to capture the full breadth and complexity of undergraduate physics, whereas this level provides a rigorous yet standardized testbed for pedagogically relevant assessment of multi-step physical reasoning. To this end, we present PhysUniBench, a large-scale multimodal benchmark designed to evaluate and improve the reasoning capabilities of multimodal large language models (MLLMs) specifically on undergraduate-level physics problems. PhysUniBench consists of 3,304 physics questions spanning 8 major sub-disciplines of physics, each accompanied by one visual diagrams. The benchmark includes both open-ended and multiple-choice questions, systematically curated and difficulty-rated through an iterative model-in-the-loop process. The benchmark's construction involved a rigorous multi-stage process, including multiple roll-outs, expert-level evaluation, automated filtering of easily solved problems, and a nuanced difficulty grading system with five levels. Through extensive experiments, we observe that current state-of-the-art models encounter substantial challenges in physics reasoning. For example, GPT-5 achieves only about 53.7% accuracy in the proposed PhysUniBench. These results highlight that current MLLMs struggle with advanced physics reasoning, especially on multi-step problems and those requiring precise diagram interpretation. By providing a broad and rigorous assessment tool, PhysUniBench aims to drive progress in AI for Science, encouraging the development of models with stronger physical reasoning, problem-solving skills, and multimodal understanding. The benchmark and evaluation scripts are available at https://anonymous.4open.science/r/PhysUniBenchmark-5784.

Primary Area: datasets and benchmarks

Submission Number: 2029