How good are frontier models at physics? --> Computer Science > Artificial Intelligence arXiv:2609.13009 (cs) [Submitted on 11 Sep 2026] Title:How Good Are Frontier Models at Physics? Expert Re-Gradi
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How good are frontier models at physics?
--> Computer Science > Artificial Intelligence arXiv:2609.13009 (cs) [Submitted on 11 Sep 2026] Title:How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks 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 View a PDF of the paper titled How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks, by Ali Ansari and 50 other authors View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.13009 [cs.AI] (or arXiv:2609.13009v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.13009 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ali Ansari [view email] [v1] Fri, 11 Sep 2026 16:06:50 UTC (178 KB) Full-text links: Access Paper: View a PDF of the paper titled How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks, by Ali Ansari and 50 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Au
--> Computer Science > Artificial Intelligence arXiv:2609.13009 (cs) [Submitted on 11 Sep 2026] Title:How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks 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 View a PDF of the paper titled How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks, by Ali Ansari and 50 other authors View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.13009 [cs.AI] (or arXiv:2609.13009v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.13009 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ali Ansari [view email] [v1] Fri, 11 Sep 2026 16:06:50 UTC (178 KB) Full-text links: Access Paper: View a PDF of the paper titled How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks, by Ali Ansari and 50 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Au