@inproceedings{rege-etal-2026-agentic,
    title = "Agentic Very Long Video Understanding",
    author = "Rege, Aniket  and
      Sadhu, Arka  and
      Li, Yuliang  and
      Li, Kejie  and
      Vinayak, Ramya Korlakai  and
      Chai, Yuning  and
      Lee, Yong Jae  and
      Kim, Hyo Jin",
    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://preview.aclanthology.org/ingest-acl/2026.acl-long.2161/",
    pages = "46575--46602",
    ISBN = "979-8-89176-390-6",
    abstract = "The advent of always-on personal AI assistants, enabled by all-day wearable devices such as smart glasses, demands a new level of contextual understanding{---}one that goes beyond short, isolated events to encompass the continuous, longitudinal stream of egocentric video. Achieving this vision requires advances in long-horizon video understanding, where systems must interpret and recall visual and audio information spanning days or even weeks. Existing methods, including large language models and retrieval-augmented generation, are constrained by limited context windows and lack the ability to perform compositional, multi-hop reasoning over very long video streams. In this work, we address these challenges through EGAgent, an enhanced agentic framework centered on entity scene graphs, which represent people, places, objects, and their relationships over time. Our system equips a planning agent with tools for structured search and reasoning over these graphs, as well as hybrid visual and audio search capabilities, enabling detailed, cross-modal, and temporally coherent reasoning. Experiments on the EgoLifeQA and Video-MME-long datasets show that our method achieves state-of-the-art performance on EgoLifeQA (57.5{\%}) and competitive performance on Video-MME-long (74.1{\%}) for complex longitudinal video understanding tasks."
}