How good are frontier models at physics?
Recorded: Sept. 17, 2026, 12:28 a.m.
| Original | Summarized |
[2609.13009] How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks
Skip to main content Search Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2609.13009 (cs) [Submitted on 11 Sep 2026] 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: Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ali Ansari [view email] [v1]
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The study investigates the actual capabilities of frontier language models when applied to physics, addressing the discrepancy between reported benchmark scores and expert assessments. The investigation was prompted by low performance scores observed on leading physics benchmarks, including those featured in the Artificial Analysis Intelligence Index 2026, which suggested that these models struggle with the demanding requirements of scientific reasoning and quantitative problem-solving. To validate these initial findings, the researchers reevaluated frontier models by assessing them on six widely used physics benchmarks and employing domain experts to audit the results. The auditing process involved having faculty and graduate researchers with relevant expertise meticulously review the problem statements, the provided reference solutions, and the models' responses. The goal of this expert review was to differentiate between genuine errors made by the language models, errors in the benchmarking process itself, incorrect reference solutions, or issues arising from ambiguous or underspecified questions. A key observation from this process was that most instances initially flagged as incorrect in the benchmarking reflected flaws in the evaluation setup rather than actual deficits in the models' physics reasoning abilities. To refine the assessment, experts were subsequently engaged to address these identified benchmarking issues by correcting erroneous reference solutions and either repairing or excluding questions deemed fundamentally flawed. Following this expert-validated correction process, substantial performance improvements were documented for the models. Specifically, the measured mean@4 scores for GPT-5.6-Sol increased significantly, rising from 47.3% to 78.7% on the HLE-Physics benchmark and from 61.0% to 87.2% on the CMT-Benchmark. Furthermore, after applying the expert corrections, the corrected pass@4 metric reached 94.4% across the fifty-four retained CritPt challenges. Similar substantial increases were observed when scores were corrected for the audited subsets of UGPhysics, PRISM-Physics, and PHYBench. These empirical results collectively suggest that existing benchmarks significantly underestimate the actual capacity of frontier models to solve well-posed physics problems. Consequently, the findings highlight an urgent necessity for the development of more rigorous, expert-validated evaluation methods to accurately gauge the scientific reasoning capabilities of these advanced models, especially given the observed near-saturation of performance on these closed-ended tasks. |