@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://aclanthology.org/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 $\textbf{Fusion Deficit Paradox}$."
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<abstract>We introduce 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 visual, audio, and text modalities. Our benchmark excels in three key areas: 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. b) Diverse and Challenging Task Scenarios: We have designed six challenging task scenarios, including both Intra-Event and Inter-Event Tasks. 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 Fusion Deficit Paradox.</abstract>
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%0 Conference Proceedings
%T LongInsightBench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding.
%A Han, ZhaoYang
%A Lin, Qihan
%A Liang, Hao
%A Chen, Bowen
%A Liu, Zhou
%A Zhang, Wentao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F han-etal-2026-longinsightbench
%X We introduce 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 visual, audio, and text modalities. Our benchmark excels in three key areas: 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. b) Diverse and Challenging Task Scenarios: We have designed six challenging task scenarios, including both Intra-Event and Inter-Event Tasks. 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 Fusion Deficit Paradox.
%U https://aclanthology.org/2026.findings-acl.965/
%P 19332-19358
Markdown (Informal)
[LongInsightBench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding.](https://aclanthology.org/2026.findings-acl.965/) (Han et al., Findings 2026)
ACL