Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving

We introduce PHYSICS, a comprehensive benchmark for university-level physics problem solving.It contains 1,297 expert-annotated problems covering six core areas: classical mechanics, quantum mechanics, thermodynamics and statistical mechanics, electromagnetism, atomic physics, and optics.Each problem requires advanced physics knowledge and mathematical reasoning.We develop a robust automated evaluation system for precise and reliable validation.Our evaluation of leading foundation models reveals substantial limitations.Even the most advanced model, o3-mini, achieves only 59.9% accuracy, highlighting significant challenges in solving high-level scientific problems.Through comprehensive error analysis, exploration of diverse prompting strategies, and Retrieval-Augmented Generation (RAG)-based knowledge augmentation, we identify key areas for improvement, laying the foundation for future advancements.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.findings-acl.610
Primary Topic
Education and Critical Thinking Development
Type
article
Field-Weighted Citation Impact
0.00

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Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving

Yilun Zhao, Tianyu Yang, K.T. Feng, Arman Cohan et al.
Education and Critical Thinking Development
article

Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving

Yilun Zhao, Tianyu Yang, K.T. Feng, Arman Cohan, Chen Zhao, Yixin Liu, John Sous
article en

Abstract

We introduce PHYSICS, a comprehensive benchmark for university-level physics problem solving.It contains 1,297 expert-annotated problems covering six core areas: classical mechanics, quantum mechanics, thermodynamics and statistical mechanics, electromagnetism, atomic physics, and optics.Each problem requires advanced physics knowledge and mathematical reasoning.We develop a robust automated evaluation system for precise and reliable validation.Our evaluation of leading foundation models reveals substantial limitations.Even the most advanced model, o3-mini, achieves only 59.9% accuracy, highlighting significant challenges in solving high-level scientific problems.Through comprehensive error analysis, exploration of diverse prompting strategies, and Retrieval-Augmented Generation (RAG)-based knowledge augmentation, we identify key areas for improvement, laying the foundation for future advancements.

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Education and Critical Thinking Development
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