@inproceedings{liu-etal-2026-longvideoagent,
title = "{L}ong{V}ideo{A}gent: Multi-Agent Reasoning with Long Videos",
author = "Liu, Runtao and
Liu, Ziyi and
Tang, Jiaqi and
Ma, Yue and
Pi, Renjie and
Zhang, Jipeng and
Chen, Qifeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1876/",
pages = "40404--40416",
ISBN = "979-8-89176-390-6",
abstract = "Recent advances in multimodal LLMs and systems that use tools for long-video QA point to the promise of reasoning over hour-long episodes. However, many methods still compress content into lossy summaries or rely on limited toolsets, weakening temporal grounding and missing fine-grained cues. We propose a multi-agent framework in which a master LLM coordinates a grounding agent to localize question-relevant segments and a vision agent to extract targeted textual observations. The master agent plans with a step limit, and is trained with reinforcement learning to encourage concise, correct, and efficient multi-agent cooperation. This design helps the master agent focus on relevant clips via grounding, complements subtitles with visual detail, and yields interpretable trajectories. On our proposed *LongTVQA* and *LongTVQA+* which are episode-level datasets aggregated from TVQA/TVQA+, our multi-agent system significantly outperforms strong non-agent baselines. Experiments also show reinforcement learning further strengthens reasoning and planning for the trained agent."
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%0 Conference Proceedings
%T LongVideoAgent: Multi-Agent Reasoning with Long Videos
%A Liu, Runtao
%A Liu, Ziyi
%A Tang, Jiaqi
%A Ma, Yue
%A Pi, Renjie
%A Zhang, Jipeng
%A Chen, Qifeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-etal-2026-longvideoagent
%X Recent advances in multimodal LLMs and systems that use tools for long-video QA point to the promise of reasoning over hour-long episodes. However, many methods still compress content into lossy summaries or rely on limited toolsets, weakening temporal grounding and missing fine-grained cues. We propose a multi-agent framework in which a master LLM coordinates a grounding agent to localize question-relevant segments and a vision agent to extract targeted textual observations. The master agent plans with a step limit, and is trained with reinforcement learning to encourage concise, correct, and efficient multi-agent cooperation. This design helps the master agent focus on relevant clips via grounding, complements subtitles with visual detail, and yields interpretable trajectories. On our proposed *LongTVQA* and *LongTVQA+* which are episode-level datasets aggregated from TVQA/TVQA+, our multi-agent system significantly outperforms strong non-agent baselines. Experiments also show reinforcement learning further strengthens reasoning and planning for the trained agent.
%U https://aclanthology.org/2026.acl-long.1876/
%P 40404-40416
Markdown (Informal)
[LongVideoAgent: Multi-Agent Reasoning with Long Videos](https://aclanthology.org/2026.acl-long.1876/) (Liu et al., ACL 2026)
ACL
- Runtao Liu, Ziyi Liu, Jiaqi Tang, Yue Ma, Renjie Pi, Jipeng Zhang, and Qifeng Chen. 2026. LongVideoAgent: Multi-Agent Reasoning with Long Videos. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 40404–40416, San Diego, California, United States. Association for Computational Linguistics.