The advent of Multimodal Large Language Models (MLLMs) has expanded AI capabilities to visual modalities, yet existing evaluation benchmarks remain limited to single-video understanding, overlooking the critical need for multi-video understanding in real-world scenarios (e.g., sports analytics and autonomous driving). To address this significant gap, we introduce MVU-Eval, the first comprehensive benchmark for evaluating Multi-Video Understanding for MLLMs. Specifically, our MVU-Eval mainly assesses eight core competencies through 1,824 meticulously curated question-answer pairs spanning 4,959 videos from diverse domains, addressing both fundamental perception tasks and high-order reasoning tasks. These capabilities are rigorously aligned with real-world applications such as multi-sensor synthesis in autonomous systems and cross-angle sports analytics. Through extensive evaluation of state-of-the-art open-source and closed-source models, we reveal significant performance discrepancies and limitations in current MLLMs' ability to perform understanding across multiple videos. The benchmark will be made publicly available to foster future research.
@article{arxiv.2511.07250,
title = {MVU-Eval: Towards Multi-Video Understanding Evaluation for Multimodal LLMs},
author = {Tianhao Peng and Haochen Wang and Yuanxing Zhang and Zekun Wang and Zili Wang and Gavin Chang and Jian Yang and Shihao Li and Yanghai Wang and Xintao Wang and Houyi Li and Wei Ji and Pengfei Wan and Steven Huang and Zhaoxiang Zhang and Jiaheng Liu},
journal= {arXiv preprint arXiv:2511.07250},
year = {2025}
}