How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks

arXiv:2609.13009 (cs)

Authors:Ali Ansari, Haoran Sun, Andy Zeyi Liu, Mark Jabbour, Yongshan Ding, Steven Girvin, Yu He, Sohrab Ismail-Beigi, Aleksander Kubica, Owen D. Miller, Corey O'Hern, Vidvuds Ozolins, David Poland, A. Douglas Stone, Frank C. van den Bosch, Logan Wright, Navid Akbari, Santanu Antu, Kangle Cai, Andrew Calabrese-Day, Mateo Cárdenes Wuttig, Meng Cheng, Barry T. Chiang, Ali Ghorashi, Shouzhen Gu, Haoyang Huang, Zhibo Kang, Lukas Kienesberger, Hantian Liu, Charles Lomba, Zhongling Lu, Wenchao Ma, Rohin E. McIntosh, Evan McKinney, Ivan Rojkov, Xulei Sun, Yarone Meir Tokayer, Naveen Balaji Umasankar, Mira Varma, Leda Wang, Qimin Wang, Tyler Wang, Haoyu Wei, Jinming Yang, Jinchen Zhao, Sherlock Tingrui Zhao, Qinyuan Zheng, Jay S. Zou, Lucas Baker, Arman Cohan, John Sous

Abstract:Low reported scores on leading physics benchmarks, including those featured in the Artificial Analysis Intelligence Index (2026), suggest that frontier language models still struggle with advanced physics, a demanding test of their scientific reasoning and quantitative problem-solving abilities. Yet this impression does not always align with domain experts' experiences using these models in their work. We revisit these reported findings by evaluating frontier models on six widely used physics benchmarks and auditing them with experts, focusing on text-only problems with verifiable final answers. For each subfield of physics, faculty and graduate researchers with relevant expertise carefully review problem statements, reference solutions, and model responses to distinguish genuine model errors from grader errors, incorrect reference solutions, and ambiguous or underspecified questions. Most audited cases initially evaluated as incorrect reflect these benchmarking issues rather than errors in the models' physics reasoning. We then ask experts to address these benchmarking issues by correcting erroneous reference solutions and repairing or excluding flawed questions. We find that GPT-5.6-Sol's measured mean@4 rises from 47.3% to 78.7% on HLE-Physics and from 61.0% to 87.2% on CMT-Benchmark, while its corrected pass@4 reaches 94.4% on the 54 retained CritPt challenges. Corrected scores are computed on the retained evaluation subsets following expert review. Scores on the audited subsets of UGPhysics, PRISM-Physics, and PHYBench also rise substantially after correction. These findings suggest that current benchmarks substantially understate frontier models' ability to solve well-posed physics problems. Near-saturation on these closed-ended tasks highlights the need for more demanding, expert-validated evaluations.

Artificial Intelligence (cs.AI)

arXiv:2609.13009 [cs.AI]

(or arXiv:2609.13009v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.13009 arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.