Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
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Computer Science > Computer Vision and Pattern Recognition
Title:Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
Abstract:Long-form video editing over heterogeneous footage requires agents to coordinate source selection, multimodal analysis, timeline construction, narration and subtitle alignment, rendering, and revision while exposing intermediate state for inspection and repair. We present Crayotter, an open-source multimodal multi-agent demo system for prompt-driven long-form video editing. Crayotter organizes production around coverage-aware material preparation, artifact-grounded editing research, and tool-grounded timeline execution. Across these stages, retrieval reports, video analyses, editing blueprints, scheduler events, tool calls, intermediate renders, and final exports are treated as first-class artifacts rather than hidden transient state. The workbench supports local assets, agent-assisted retrieval, progress monitoring, artifact preview, failure diagnosis, interrupted-job resumption, and resource-aware asynchronous execution for long-running workflows. In a 23-theme evaluation, Crayotter achieves the highest human overall score (3.40/5) among the compared systems, with its largest margins in theme alignment, narrative coherence, and editing smoothness. These results show that long-horizon video editing agents can be made traceable, inspectable, and practically controllable through observable production artifacts. Code, traces, and examples are publicly available at this https URL.
| Comments: | 10 pages, 5 figures |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2606.07636 [cs.CV] |
| (or arXiv:2606.07636v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07636
arXiv-issued DOI via DataCite
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Submission history
From: Lecheng Yan [view email][v1] Sun, 31 May 2026 14:07:57 UTC (5,230 KB)
[v2] Fri, 17 Jul 2026 08:44:11 UTC (5,482 KB)
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