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

arXiv:2503.21821 · cs.AI · Submitted 2025-03-26 · Read on arXiv

cs.AI

Submitted: 2025-03-26

Updated: 2026-10-12

Journal ref: Findings of ACL 2025

Code: https://github.com/yale-nlp/Physics

License: http://creativecommons.org/licenses/by/4.0/

The gist: We introduce Physics, a benchmark for evaluating foundation models on advanced physics problem solving.

Terminology

Abstract

We introduce Physics, a benchmark for evaluating foundation models on advanced physics problem solving. It contains 1034 annotated, open-ended problems from PhD-qualifying exams across six core physics domains. Solving these problems requires applying domain-specific knowledge to physical setups and carrying out multi-step mathematical reasoning. Each problem includes a worked solution and independently checked reference answers. An LLM-based grading protocol evaluates individual answers and complete multi-part problems, supporting numeric, symbolic, and text responses. We evaluate 53 proprietary and open-weight models. On test problems without figures, high accuracy is concentrated in a small group of thinking models, with the strongest open-weight models approaching proprietary performance. Most open-weight models still solve fewer than half of these problems. Our analysis of Qwen3.5-9B reveals failures in applying physics knowledge and interpreting figures, with misread figures affecting several subquestions.

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