[Submitted on 21 Oct 2026]
Title:VisCurate: Evaluating Behavioral Equivalence of Visual Skills
View a PDF of the paper titled VisCurate: Evaluating Behavioral Equivalence of Visual Skills, by Md Farhan Ishraq and 3 other authors
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Abstract:Modern vision-based agents have shifted toward skill-based problem-solving, where a skill is an executable image-to-image operation, e.g., cropping, thresholding, or edge detection. As these skill libraries grow, they accumulate duplicated, mislabeled, and defective skills. The signals normally used to curate them (a skill's name, description, or source code) say little about how the skill visually changes the image. We present VisCurate, a framework and benchmark that curates visual skill libraries from observed execution rather than from text. VisCurate runs each candidate skill pair on a shared probe battery and assigns one of six behavioral relations, each tied to a library edit that is safe to make. The benchmark injects seven defect types into a clean library at controlled rates, producing 300 reproducible instances with ground-truth relations and repairs. While text baselines accept up to five of six false merges on hard negatives, our verifier accepts none and recovers 51.2% of the pairs that are truly mergeable. On two real, uncurated libraries, VisCurate finds that 92% of the skills a package shares with its downstream copy are redundant, while execution confirms only 10 of 17 published equivalence claims. The curation problem also remains open, as the strongest of the nine automated curators recovers only about half of the repairs. We emphasize that visual skill libraries should be curated by behavior, not by description.
Comments: Accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2027), Main Conference
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.30405 [cs.CV]
(or arXiv:2610.30405v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.30405
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Md. Farhan Ishraq [view email] [v1] Wed, 21 Oct 2026 20:11:08 UTC (173 KB)
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