让长视频编辑过程可追踪、可检查，支持多智能体协作与中断恢复。
Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
- 通过可追溯的中间产物组织多智能体工作流，实现全流程透明化。
- 在23个主题评估中人类评分达3.40/5，主题对齐与叙事连贯性最优。
- 适合需要可解释性与可控性的长视频自动化编辑场景。
长时长视频编辑需协调源素材选择、多模态分析、时间线构建、旁白与字幕对齐、渲染及修订等任务，并暴露中间状态供检查和修复。本文提出Crayotter，一个开源的多模态多智能体演示系统，支持提示驱动的长视频编辑。该系统围绕覆盖感知的素材准备、基于成果的编辑研究和基于工具的时间线执行展开。各阶段产生的检索报告、视频分析、编辑蓝图、调度事件、工具调用、中间渲染结果及最终输出均作为第一类成果而非临时状态。工作台支持本地资产、智能体辅助检索、进度监控、成果预览、故障诊断、中断任务恢复以及资源感知的异步执行。在23个主题的评估中，Crayotter获得最高人类综合评分（3.40/5），在主题一致性、叙事连贯性和剪辑流畅性上优势显著。结果表明，通过可观测的生产成果，长周期视频编辑智能体可实现可追踪、可检查、可控制。代码、执行轨迹与示例已公开于https://github.com/idwts/Crayotter。
原文摘要 · Abstract (English)
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 https://github.com/idwts/Crayotter.
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