Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing

• URL: http://arxiv.org/abs/2606.07636v1
• Date: Sun, 31 May 2026 14:07:57 GMT
• Title: Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
• Abstract summary: Crayotter is an open-source multimodal multi-agent system for prompt-driven video editing.<n>Crayotter organizes production into three phases: coverage-aware material preparation, artifact-based editing research, and tool-grounded timeline execution.
• Score: 18.640980775340637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Editing a long-form video from heterogeneous footage requires more than selecting clips: an agent must preserve narrative intent across material preparation, timeline construction, post-production, and revision while leaving enough evidence to diagnose failures. We present \textbf{Crayotter}, an open-source multimodal multi-agent system for prompt-driven video editing. Crayotter organizes production into three phases: coverage-aware material preparation, artifact-based editing research, and tool-grounded timeline execution. Each phase externalizes inspectable artifacts, including coverage reports, multimodal analyses, editing blueprints, tool calls, and intermediate renders. These artifacts make an editing run traceable and allow failed segments to be diagnosed and selectively revised instead of requiring a full restart. We evaluate Crayotter on 23 editing themes against CapCut-Mate and CutClaw. Under human evaluation, Crayotter achieves an average score of 3.40/5, compared with 2.44 and 1.70 for the two baselines, with consistent gains in theme alignment, narrative coherence, and editing smoothness. We additionally describe a replayable trajectory schema and verifiable reward design that prepare these workflows for future policy optimization. Code, traces, and examples are publicly available at https://github.com/idwts/Crayotter.

Related papers

• Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation [20.067016616921222]
  CutCraft is the first benchmark for editing-technique execution in multi-shot audio-video generation.<n>We design an agentic editing baseline that decomposes generation into planning, shot-level synthesis, and post-hoc composition.<n>We find unstable shot structures, weak control of transition execution, and sharp degradation on higher-order montage.
  arXiv  Detail & Related papers  (2026-09-08T05:35:58Z)
• Better Call CineCrew: Consistent Ultra-Long Narrative-to-Film Generation [53.27387848819395]
  Long-form narrative-to-film generation requires shot-level controllability and cross-clip consistency.<n>We introduce a structured orchestration layer for film-oriented script-to-video generation.
  arXiv  Detail & Related papers  (2026-09-07T16:33:14Z)
• CoVEBench: Can Video Editing Models Handle Complex Instructions? [26.871333176496126]
  CoVEBench is a compositional video editing benchmark comprising 416 curated source videos, 626 multi-point editing instructions, and 9,990 fine-grained checklist items.<n>It evaluates models via MLLM-judged instruction compliance and video fidelity, alongside automated metrics for video quality.
  arXiv  Detail & Related papers  (2026-06-07T02:29:41Z)
• R^3: Composed Video Retrieval via Reasoning-Guided Recalling and Re-ranking [73.0537447183962]
  We present a zero-shot composed video retrieval pipeline built around Reasoning-guided Recalling and Reranking.<n>The model generates a reasoning trace that describes the expected target video after applying the edit.<n>Experiments have demonstrated the effectiveness of our method in addressing this challenge.
  arXiv  Detail & Related papers  (2026-05-31T09:20:53Z)
• CutClaw: Agentic Hours-Long Video Editing via Music Synchronization [96.62825277039117]
  CutClaw is an autonomous multi-agent framework designed to edit hours-long raw footage into meaningful short videos.<n>It produces videos with synchronized music, followed by instructions, and a visually appealing appearance.<n>We conduct detailed experiments to demonstrate that CutClaw significantly outperforms state-of-the-art baselines in generating high-quality, rhythm-aligned videos.
  arXiv  Detail & Related papers  (2026-03-31T12:25:53Z)
• FlowDC: Flow-Based Decoupling-Decay for Complex Image Editing [52.54102743380658]
  We propose FlowDC, which decouples the complex editing into multiple sub-editing effects and superposes them in parallel during the editing process.<n>FlowDC shows superior results compared with existing methods.
  arXiv  Detail & Related papers  (2025-12-12T09:08:39Z)
• Prompt-Driven Agentic Video Editing System: Autonomous Comprehension of Long-Form, Story-Driven Media [35.60423976124236]
  We present a prompt-driven, modular editing system that helps creators restructure multi-hour content through free-form prompts rather than timelines.<n>At its core is a semantic indexing pipeline that builds a global narrative via temporal segmentation, guided memory compression, and cross-granularity fusion.<n>Our system scales prompt-driven editing, preserves narrative coherence, and balances automation with creator control.
  arXiv  Detail & Related papers  (2025-09-20T21:22:56Z)
• Cut2Next: Generating Next Shot via In-Context Tuning [93.14744132897428]
  Multi-shot generation demands purposeful, film-like transitions and strict cinematic continuity.<n>Current methods often prioritize basic visual consistency, neglecting crucial editing patterns.<n>We introduce Next Shot Generation (NSG): a subsequent, high-quality shot that critically synthesizes professional editing patterns.
  arXiv  Detail & Related papers  (2025-08-11T17:56:59Z)
• Shape-for-Motion: Precise and Consistent Video Editing with 3D Proxy [48.72918598961146]
  We present Shape-for-Motion, a novel framework that incorporates a 3D proxy for precise and consistent video editing.<n>Our framework supports various precise and physically-consistent manipulations across the video frames, including pose editing, rotation, scaling, translation, texture modification, and object composition.
  arXiv  Detail & Related papers  (2025-06-27T17:59:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.