{"ID":2825948,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20618","arxiv_id":"2512.20618","title":"LongVideoAgent: Multi-Agent Reasoning with Long Videos","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. Code and data will be shared at https://longvideoagent.github.io/.","short_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 f...","url_abs":"https://arxiv.org/abs/2512.20618","url_pdf":"https://arxiv.org/pdf/2512.20618v1","authors":"[\"Runtao Liu\",\"Ziyi Liu\",\"Jiaqi Tang\",\"Yue Ma\",\"Renjie Pi\",\"Jipeng Zhang\",\"Qifeng Chen\"]","published":"2025-12-23T18:59:49Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CV\",\"cs.LG\",\"cs.MA\"]","methods":"[\"Reinforcement Learning\",\"Large Language Model\"]","has_code":false}