VisCurate: Evaluating Behavioral Equivalence of Visual Skills

VisCurate is a framework and benchmark that curates visual skill libraries by evaluating the actual visual behavior of skills rather than relying on textual metadata. It runs candidate skill pairs on a shared probe battery, assigns one of six behavioral relations, and uses these to safely edit the library. The benchmark introduces controlled defect types, producing 300 reproducible instances, and demonstrates that VisCurate outperforms text-based baselines in detecting false merges and recovering mergeable pairs, while revealing high redundancy in real libraries.

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• agents
• benchmarks