@inproceedings{feng-etal-2025-physics,
title = "Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving",
author = "Feng, Kaiyue and
Zhao, Yilun and
Liu, Yixin and
Yang, Tianyu and
Zhao, Chen and
Sous, John and
Cohan, Arman",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.610/",
doi = "10.18653/v1/2025.findings-acl.610",
pages = "11717--11743",
ISBN = "979-8-89176-256-5",
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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%0 Conference Proceedings
%T Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving
%A Feng, Kaiyue
%A Zhao, Yilun
%A Liu, Yixin
%A Yang, Tianyu
%A Zhao, Chen
%A Sous, John
%A Cohan, Arman
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F feng-etal-2025-physics
%X 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.
%R 10.18653/v1/2025.findings-acl.610
%U https://aclanthology.org/2025.findings-acl.610/
%U https://doi.org/10.18653/v1/2025.findings-acl.610
%P 11717-11743
Markdown (Informal)
[Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving](https://aclanthology.org/2025.findings-acl.610/) (Feng et al., Findings 2025)
ACL