PhysElite: How Far Are LLMs from Solving Olympiad-Level Physics Problems?

Title:PhysElite: How Far Are LLMs from Solving Olympiad-Level Physics Problems?

Abstract: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 this https URL.

Artificial Intelligence (cs.AI); Multimedia (cs.MM); Mathematical Physics (math-ph)

arXiv:2608.25097 [cs.AI]

(or arXiv:2608.25097v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.25097 Focus to learn more arXiv-issued DOI via DataCite

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