OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs

Caorui Li; Yu Chen; Yiyan Ji; Jin Xu; Zhenyu Cui; Shihao Li; Yuanxing Zhang; Wentao Wang; Zhenghao Song; Dingling Zhang; Ying He; Haoxiang Liu; Yuxuan Wang; Qiufeng Wang; Jiafu Tang; Zhenhe Wu; Jiehui Luo; Zhiyu Pan; Weihao Xie; Chenchen Zhang; Zhaohui Wang; Jiayi Tian; Yanghai Wang; Zhe Cao; Minxin Dai; Ke Wang; Runzhe Wen; Yinghao Ma; Yaning Pan; Sungkyun Chang; Termeh Taheri; Haiwen Xia; Christos Plachouras; Emmanouil Benetos; Yizhi Li; Ge Zhang; Jian Yang; Tianhao Peng; Zili Wang; Minghao Liu; Junran Peng; Zhaoxiang Zhang; Jiaheng Liu

arXiv · 2510.10689

OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs

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Abstract

Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively evaluate synergistic reasoning capabilities across audio and visual modalities, often neglecting either one of the modalities or integrating them in a logically inconsistent manner. To bridge this gap, we introduce OmniVideoBench, a large-scale and rigorously designed benchmark dedicated to assessing synergistic audio-visual understanding, with a strong emphasis on modality complementarity and logical consistency. Specifically, OmniVideoBench comprises 1000 high-quality question-answer(QA) pairs, each annotated with step-by-step reasoning traces, derived from 628 diverse videos ranging from several seconds to 30 minutes, and manually verified to guarantee complete correctness and uniqueness. Moreover, OmniVideoBench encompasses 13 carefully designed question types, covering temporal reasoning, spatial localization, counting, causal inference, summarization, and beyond, thereby capturing the essential challenges of video understanding. Evaluation of multiple MLLMs on OmniVideoBench reveals a pronounced gap between model performance and human reasoning, with open-source models lagging significantly behind their closed-source counterparts, underscoring the inherent difficulty of genuine audio-visual reasoning. We will release OmniVideoBench to foster the development of MLLMs with stronger and more generalizable reasoning capabilities.

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Caorui Li, Yu Chen, Yiyan Ji, Jin Xu, Zhenyu Cui, Shihao Li, Yuanxing Zhang, Wentao Wang, Zhenghao Song, Dingling Zhang, Ying He, Haoxiang Liu, Yuxuan Wang, Qiufeng Wang, Jiafu Tang, Zhenhe Wu, Jiehui Luo, Zhiyu Pan, Weihao Xie, Chenchen Zhang, Zhaohui Wang, Jiayi Tian, Yanghai Wang, Zhe Cao, Minxin Dai, Ke Wang, Runzhe Wen, Yinghao Ma, Yaning Pan, Sungkyun Chang, Termeh Taheri, Haiwen Xia, Christos Plachouras, Emmanouil Benetos, Yizhi Li, Ge Zhang, Jian Yang, Tianhao Peng, Zili Wang, Minghao Liu, Junran Peng, Zhaoxiang Zhang, Jiaheng Liu. 2025-10-12. OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs. https://arxiv.org/abs/2510.10689

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