Messier: A High-Resolution Corpus for Cross-Benchmark Agent Evaluation

arXiv:2607.25891v3 Announce Type: replace Abstract: Comprehensively evaluating AI agents across interactive environments is difficult due to fragmented tasks, scaffolds, verifiers, and scoring rules. Unfortunately, existing efforts to unify these evaluations are limited in scale and domain, making costly reruns necessary and leaving available data incomparable. We introduce Messier, a unified corpus of 923,158 records spanning 30 benchmarks, 745 agents, 11,891 tasks, and 71,969 verifiers. Messier combines public evaluation results with new runs on six underrepresented professional and scientif

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