[2608.04975] SciCode-Verified: How Benchmark Defects Underestimated the Scientific-Coding Ability of Language Models

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arXiv:2608.04975 (cs)

[Submitted on 5 Aug 2026]

Title:SciCode-Verified: How Benchmark Defects Underestimated the Scientific-Coding Ability of Language Models

Authors:Sihan Hu, Lyuhan Huang, Youjin Deng, Kun Chen<br>View a PDF of the paper titled SciCode-Verified: How Benchmark Defects Underestimated the Scientific-Coding Ability of Language Models, by Sihan Hu and 3 other authors

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Abstract:SciCode is the standard measure of the scientific-coding ability of language models: research-level problems that demand both frontier scientific theory and its implementation as working numerical code. It is a component of the Artificial Analysis Intelligence Index and a standing evaluation in government and national-laboratory suites. Yet its scores have recently plateaued: the strongest 2026 models cluster tightly around 60\% subproblem accuracy, and a successor model ties its predecessor. We trace this stagnation to defects in the benchmark itself. A per-problem, domain-expert audit of all 65 test problems uncovers 263 defects; 192 of them, spread across 91\% of the main problems, cause correct, instruction-following solutions to be wrongly rejected---through non-reproducible gold answers, over-tight tolerances, or self-contradictory specifications. Critically, 78\% of these score-suppressing defects require specialized physics or mathematics knowledge to detect, not mere clerical proofreading. We corrected every confirmable defect to produce SciCode-Verified. The corrections add only the specifications a well-posed problem requires, repair grading, and tighten the tests that were too lenient; every change is recorded with its justification and independently re-checked by a second domain expert. We re-evaluate twelve frontier model snapshots on the corrected benchmark and find a substantial recovery: subproblem accuracy rises from 45--60\% to 84--98\%, and main-problem accuracy from 9--27\% to 69--92\%. State-of-the-art models are far more proficient in scientific coding than SciCode has suggested---the bottleneck was not model capability, but the quality of the evaluation instrument. We release SciCode-Verified with its complete audit trail as the corrected public standard.

Comments:<br>47 pages, 2 figures, 6 tables. Project repository: this https URL

Subjects:

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2608.04975 [cs.SE]

(or<br>arXiv:2608.04975v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2608.04975

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Sihan Hu [view email]<br>[v1]<br>Wed, 5 Aug 2026 15:45:55 UTC (160 KB)

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