Messier: A High-Resolution Corpus for Cross-Benchmark Agent Evaluation
Score
81首次大规模统一Agent评估数据，揭示基准间能力进展差异与聚合规则影响，信息增量显著。
方法论严谨，数据标准化详尽，实证分析丰富，论文结构完整，质量高。
提供的语料库和基础架构可直接用于Agent能力比较与基准审计，实用价值高。
当前Agent评估碎片化问题突出，该论文及时提供了解决方案，时效性强。
源自arXiv预印本，由多个机构合作完成，数据公开，信源可信度高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper achieves high standards in data scale, standardization, and depth of empirical analysis, providing solid infrastructure to address key dilemmas in agent evaluation.
AI commentary
「This paper solves a headache I've had: agent scores across papers are incomparable. With Messier, we can now do cross-benchmark comparisons like standardized test scores. Especially valuable for teams building evaluation frameworks or tracking capability ceilings.」
Quick overview
Messier presents a unified corpus of 957,253 records spanning 30 benchmarks, 714 agents, 11,891 tasks, and 74,205 verifiers. By standardizing across model, scaffold, environment, and other dimensions, it reveals uneven frontier progress: function calling is saturated, programming improves fastest, and enterprise workflows remain hardest. Strict all-pass aggregation can obscure progress and alter rankings. The corpus enables agent capability scaling, benchmark auditing, and failure analysis, achieving Spearman ρ=0.81 with Epoch's capability index.
- Builds a high-resolution corpus with nearly 1M records, unifying evaluation data from 30 benchmarks.
- Reveals uneven frontier progress: function calling saturated, programming fastest, enterprise workflows hardest.
- Shows strict all-pass aggregation can obscure progress and artificially alter agent rankings.
- Provides reusable infrastructure for capability scaling, benchmark auditing, and failure analysis.
Full text
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