PHYSICS: Benchmarking Foundation Models on University-Level Physics Problem Solving
Feng, Kaiyue, Zhao, Yilun, Liu, Yixin, Yang, Tianyu, Zhao, Chen, Sous, John, Cohan, Arman
–arXiv.org Artificial Intelligence
We introduce PHYSICS, a comprehensive benchmark for university-level physics problem solving. It contains 1297 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.
arXiv.org Artificial Intelligence
Jun-3-2025
- Country:
- Asia > Singapore (0.04)
- Europe
- Denmark > Capital Region
- Copenhagen (0.04)
- United Kingdom > England
- Cambridgeshire > Cambridge (0.04)
- Denmark > Capital Region
- Genre:
- Research Report (1.00)
- Industry:
- Education > Curriculum > Subject-Specific Education (0.46)
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