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Plans You Can Check: Verifier-Grounded Learning of an Open-Weight Planner for Executable Video-Editing

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

Practical video editing is not only pixel generation: an editor must turn a brief, a clip pool, music metadata, and hard constraints into an executable timeline. We study this decision layer as \emph{executable video-editing planning} and introduce RefineCut, which, unlike workflow systems that wrap a prompted frontier model, trains a compact open-weight planner for it. The planner edits a typed timeline through structured patches covering clip selection, trimming, ordering, transitions, and duration and music alignment; a deterministic verifier applies each patch and checks it against an expl

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