@inproceedings{han-etal-2026-longinsightbench,
    title = "{L}ong{I}nsight{B}ench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding.",
    author = "Han, ZhaoYang  and
      Lin, Qihan  and
      Liang, Hao  and
      Chen, Bowen  and
      Liu, Zhou  and
      Zhang, Wentao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.965/",
    pages = "19332--19358",
    ISBN = "979-8-89176-395-1",
    abstract = "We introduce $\textbf{LongInsightBench}$, the first benchmark designed to assess models' ability to understand long videos, with a focus on human language, viewpoints, actions, and other contextual elements, while integrating $\textbf{visual, audio, and text}$ modalities. Our benchmark excels in three key areas: $\textbf{a) Long-Duration, Human-Centric Videos:}$ We carefully selected approximately 1,000 videos from open-source datasets FineVideo based on duration limit and multi-modal information density, focusing on content like lectures, interviews, and vlogs, which contain rich human-centric semantic and contextual attributes. $\textbf{b) Diverse and Challenging Task Scenarios:}$ We have designed six challenging task scenarios, including both Intra-Event and Inter-Event Tasks. $\textbf{c) Rigorous and Comprehensive Quality Assurance Pipelines:}$ We have developed a three-step, semi-automated data quality assurance pipeline to ensure the difficulty and validity of the synthesized questions and answer options. Based on LongInsightBench, we designed a series of experiments. which shows that Omni-modal models(OLMs) still face challenge in tasks requiring precise temporal localization (T-Loc) and long-range causal inference (CE-Caus). Surprisingly, extended experiments reveal the information loss in modal fusion of OLMs, which we called the $\textbf{Fusion Deficit Paradox}$."
}