Towards a Large Physics Benchmark
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Abstract (arXiv)
We introduce a benchmark framework developed by and for the scientific community to evaluate, monitor and steer large language model development in fundamental physics. Building on philosophical concepts of scientific understanding and creativity, we develop a scoring system in which each question is scored by an expert for its correctness, difficulty, and surprise. The questions are of three forms: (i) multiple-choice questions for conceptual understanding, (ii) analytical problems requiring mathematical derivation, and (iii) openended tasks requiring complex problem solving. Our current dataset contains diverse set of examples, including a machine learning challenge to classify high-energy physics events, such as the four top quark signal. To ensure continued relevance, we propose a living benchmark, where physicists contribute questions, for instance alongside new publications. We invite contributions via: http://www.physicsbenchmarks.org/. We hope that this benchmark will enable a targeted AI development that can make a meaningful contribution to fundamental physics research.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 29 Jul 2025.
Builds on
11 of 23 references.
References · 11
- 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
- 2020Fact or Fiction: Verifying Scientific Claimscited by 546
- 2025Humanity's Last Examcited by 467
- 2024BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrievalcited by 153
- 2023SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Researchcited by 112
- 2025The Leaderboard Illusioncited by 57
- 2025Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physicscited by 33
- 2024Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publicationscited by 26
- 2024SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluationcited by 23
- 2025Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Modelscited by 14
- 2023Towards a Benchmark for Scientific Understanding in Humans and Machinescited by 9
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Citing papers per year: 2026: 1.
1 paper.