Understanding how (multimodal) large language models perform on physics problems requires benchmarks that reflect the difficulty and breadth of expert-level physical reasoning. Existing physics benchmarks remain limited in the following two important ways: (1) short of high-difficulty datasets, and (2) lack of comprehensive coverage of visual forms, knowledge points, and step-by-step solution processes. As a result, model performance on current datasets may not be fully representative of their ability to solve complex physics problems. To address these issues, we present PhysElite, a large-scale bilingual multimodal benchmark for Olympiad-level physics reasoning. PhysElite contains 11,586 Olympiad-tier problems. For each problem, we provide corresponding visual diagrams, step-by-step bilingual Chinese-English solution derivations, and the final answer. We benchmark 18 open-source and closed-source MLLMs, and find that even the strongest model reaches only 33.7% answer accuracy. We additionally conduct step-level process evaluation to diagnose where models fail in the reasoning chain. Our datasets are released at https://huggingface.co/datasets/physelite/PhysElite.
@inproceedings{anonymous2026physelite,title={PhysElite: How Far Are {LLM}s from Solving Olympiad-Level Physics Problems?},author={Anonymous},booktitle={The Fortieth Annual Conference on Neural Information Processing Systems Evaluations and Datasets Track},year={2026},url={https://openreview.net/forum?id=f3ceI8ijyu}}
MATH-AI
Dense Neuro-Symbolic Reasoning in a Unified Geometry State
Ruoran Xu, Wending Gao, Haoyu Cheng, Xiaoqiang Kang, and Qiufeng Wang
In The Sixth Workshop on Mathematical Reasoning and AI, 2026