SciCode-Verified: How Benchmark Defects Underestimated the Scientific-Coding Ability of Language Models
Score
78揭示了广泛使用基准的严重缺陷，颠覆了关于模型能力停滞的既有认知。
审计方法严谨，提供完整审计轨迹和逐项修正理由，可复现性强。
对模型评估者和开发者有直接参考价值，可能影响现有基准的选择与解读。
针对2026年最新模型得分停滞问题，具有高度现实针对性。
来自arXiv预印本，虽未经同行评审，但审计流程和公开数据支撑了可信度。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
High information gain with rigorous auditing and counterintuitive results, providing a reproducible correction to a widely used benchmark.
AI commentary
「The biggest shock for me is that the benchmark we trust may have been penalizing correct answers all along. For model developers and evaluation researchers, this means conclusions based on SciCode need to be revisited; for industry, it suggests that some models' capability ceilings may be far higher than previously reported. I strongly recommend reading the original audit trail.」
Quick overview
SciCode is the standard benchmark for evaluating language models' scientific coding ability, yet recent scores have plateaued around 60% subproblem accuracy. A domain-expert audit of all 65 test problems uncovered 263 defects, 192 of which caused correct, instruction-following solutions to be wrongly rejected, with 78% requiring specialized physics or mathematics knowledge to detect. The corrected benchmark, SciCode-Verified, adds missing specifications, repairs grading, and tightens overly lenient tests. Re-evaluating twelve frontier model snapshots shows substantial recovery: subproblem accuracy rises from 45-60% to 84-98%, and main-problem accuracy from 9-27% to 69-92%. This indicates the bottleneck was not model capability but benchmark quality. The authors release SciCode-Verified with a complete audit trail as the corrected public standard.
- An expert audit of all 65 SciCode problems found 263 defects, 192 of which caused correct solutions to be wrongly rejected.
- 78% of the defects require specialized physics or math knowledge to detect.
- On SciCode-Verified, subproblem accuracy of frontier models jumps from 45-60% to 84-98%.
- The results show that benchmark defects, not model capability, caused the plateau.
Full text
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