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    <abstract>Video editing has become essential in digital media creation, yet existing automated systems are restricted to short segment processing and domain-specific tasks. They face two critical limitations: i) inability to handle diverse video comprehension and editing operations, and ii) lack of long-video understanding for coherent narrative creation. We propose VideoAgent, an all-in-one agentic framework addressing these challenges through two key innovations. First, we develop automated video shot creation with shot planning agents for coherent narratives and cross-modal retrieval for aligned visual content. Second, we design a multi-agent orchestration framework integrating over thirty specialized editing agents. Intent parsing filters relevant tools while textual-gradient graph optimization assembles complex editing pipelines. Extensive experiments on our newly proposed VideoEdit benchmark and public datasets demonstrate VideoAgent’s superiority over existing multimodal LLMs and agentic systems. VideoAgent achieves 87-95% orchestration success rates while reducing API costs by 60%. Human evaluation across six video categories shows that VideoAgent produces professional-quality content approaching human-level performance, with ratings only 4% below human-created videos.</abstract>
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%0 Conference Proceedings
%T VideoAgent: All-in-One Framework for Video Understanding and Editing
%A Zhou, Hengji
%A Huang, Lingxuan
%A Wang, Jian
%A Zhou, Bing
%A Wu, Si
%A Xia, Lianghao
%A Huang, Chao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhou-etal-2026-videoagent
%X Video editing has become essential in digital media creation, yet existing automated systems are restricted to short segment processing and domain-specific tasks. They face two critical limitations: i) inability to handle diverse video comprehension and editing operations, and ii) lack of long-video understanding for coherent narrative creation. We propose VideoAgent, an all-in-one agentic framework addressing these challenges through two key innovations. First, we develop automated video shot creation with shot planning agents for coherent narratives and cross-modal retrieval for aligned visual content. Second, we design a multi-agent orchestration framework integrating over thirty specialized editing agents. Intent parsing filters relevant tools while textual-gradient graph optimization assembles complex editing pipelines. Extensive experiments on our newly proposed VideoEdit benchmark and public datasets demonstrate VideoAgent’s superiority over existing multimodal LLMs and agentic systems. VideoAgent achieves 87-95% orchestration success rates while reducing API costs by 60%. Human evaluation across six video categories shows that VideoAgent produces professional-quality content approaching human-level performance, with ratings only 4% below human-created videos.
%U https://aclanthology.org/2026.emnlp-main.949/
%P 21860-21906

Markdown (Informal)

[VideoAgent: All-in-One Framework for Video Understanding and Editing](https://aclanthology.org/2026.emnlp-main.949/) (Zhou et al., EMNLP 2026)

• VideoAgent: All-in-One Framework for Video Understanding and Editing (Zhou et al., EMNLP 2026)

ACL

• Hengji Zhou, Lingxuan Huang, Jian Wang, Bing Zhou, Si Wu, Lianghao Xia, and Chao Huang. 2026. VideoAgent: All-in-One Framework for Video Understanding and Editing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 21860–21906, Budapest, Hungary. Association for Computational Linguistics.