[2601.13719] Hierarchical Long Video Understanding with Audiovisual Entity Cohesion and Agentic Search

Computer Science > Computer Vision and Pattern Recognition arXiv:2601.13719 (cs) [Submitted on 20 Jan 2026 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Hierarchical Long Video Understanding with Audiovisual Entity Cohesion and Agentic Search Authors:Xinlei Yin, Xiulian Peng, Xiao Li, Zhiwei Xiong, Yan Lu View a PDF of the paper titled Hierarchical Long Video Understanding with Audiovisual Entity Cohesion and Agentic Search, by Xinlei Yin and 4 other authors View PDF HTML (experimental) Abstract:Long video understanding presents significant challenges for vision-language models due to extremely long context windows. Existing solutions relying on naive chunking strategies with retrieval-augmented generation, typically suffer from information fragmentation and a loss of global coherence. We present HAVEN, a unified framework for long-video understanding that enables coherent and comprehensive reasoning by integrating audiovisual entity cohesion and hierarchical video indexing with agentic search. First, we preserve semantic consistency by integrating entity-level representations across visual and auditory streams, while organizing content into a structured hierarchy spanning global summary, scene, segment, and entity levels. Then we employ an agentic search mechanism to enable dynamic retrieval and reasoning across these layers, facilitating coherent narrative reconstruction and fine-grained entity tracking. Extensive experiments demonstrate that our method achiev...