Crayotter: 用于长视频编辑的可追踪多智能体工作流
Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
AI总结：
提出Crayotter，一个开源多模态多智能体系统，通过三阶段工作流（材料准备、基于工件的编辑研究、工具驱动的执行）实现长视频编辑的可追踪性和选择性修订，在人类评估中优于基线方法。
摘要：
从异构素材编辑长视频不仅需要选择片段：智能体必须在材料准备、时间线构建、后期制作和修订过程中保持叙事意图，同时留下足够的证据以诊断失败。我们提出 \textbf{Crayotter}，一个用于提示驱动视频编辑的开源多模态多智能体系统。Crayotter 将制作组织为三个阶段：覆盖感知的材料准备、基于工件的编辑研究以及工具驱动的时间线执行。每个阶段外化可检查的工件，包括覆盖报告、多模态分析、编辑蓝图、工具调用和中间渲染。这些工件使编辑运行可追踪，并允许诊断和选择性修订失败的片段，而无需完全重启。我们在23个编辑主题上评估Crayotter，与CapCut-Mate和CutClaw进行比较。在人类评估下，Crayotter的平均得分为3.40/5，而两个基线分别为2.44和1.70，在主题对齐、叙事连贯性和编辑流畅性方面持续提升。我们还描述了一个可重放的轨迹模式和可验证的奖励设计，为这些工作流未来的策略优化做准备。代码、轨迹和示例可在 https://github.com/idwts/Crayotter 公开获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
