Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving
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.
Sources
- Phi-4 Technical Report
- Pixtral 12B
- Yi: Open Foundation Models by 01.AI
- Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena
- InternLM2 Technical Report
- Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
- Training Verifiers to Solve Math Word Problems
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models
- Gemma 2: Improving Open Language Models at a Practical Size
- FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI
- ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
- The Llama 3 Herd of Models
- OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
- Mixtral of Experts
- Aria: An Open Multimodal Native Mixture-of-Experts Model
- MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark
- GPT-4o System Card
- OpenAI o1 System Card
- Humanity's Last Exam
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