Paper Detail
Shifang Zhao, Yihan Hu, Ying Shan, Yunchao Wei, Xiaodong Cun
Editing the video content with audio alignment forms a digital human-made art in current social media. However, the time-consuming and repetitive nature of manual video editing has long been a challenge for filmmakers and professional content creators alike. In this paper, we introduce CutClaw, an autonomous multi-agent framework designed to edit hours-long raw footage into meaningful short videos that leverages the capabilities of multiple Multimodal Language Models~(MLLMs) as an agent system. It produces videos with synchronized music, followed by instructions, and a visually appealing appearance. In detail, our approach begins by employing a hierarchical multimodal decomposition that captures both fine-grained details and global structures across visual and audio footage. Then, to ensure narrative consistency, a Playwriter Agent orchestrates the whole storytelling flow and structures the long-term narrative, anchoring visual scenes to musical shifts. Finally, to construct a short edited video, Editor and Reviewer Agents collaboratively optimize the final cut via selecting fine-grained visual content based on rigorous aesthetic and semantic criteria. We conduct detailed experiments to demonstrate that CutClaw significantly outperforms state-of-the-art baselines in generating high-quality, rhythm-aligned videos. The code is available at: https://github.com/GVCLab/CutClaw.
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@misc{zhao2026cutclaw,
title = {CutClaw: Agentic Hours-Long Video Editing via Music Synchronization},
author = {Shifang Zhao and Yihan Hu and Ying Shan and Yunchao Wei and Xiaodong Cun},
year = {2026},
abstract = {Editing the video content with audio alignment forms a digital human-made art in current social media. However, the time-consuming and repetitive nature of manual video editing has long been a challenge for filmmakers and professional content creators alike. In this paper, we introduce CutClaw, an autonomous multi-agent framework designed to edit hours-long raw footage into meaningful short videos that leverages the capabilities of multiple Multimodal Language Models\textasciitilde{}(MLLMs) as an agent system. },
url = {https://huggingface.co/papers/2603.29664},
keywords = {multimodal language models, multi-agent framework, video editing, audio alignment, narrative consistency, visual content selection, aesthetic criteria, semantic criteria, code available, huggingface daily},
eprint = {2603.29664},
archiveprefix = {arXiv},
}
{}