The rapid progress of Large Language Models (LLMs) has spurred growing interest in Multi-modal LLMs (MLLMs) and motivated the development of benchmarks to evaluate their perceptual and comprehension abilities. Existing benchmarks, however, are limited to static images or single videos, overlooking the complex interactions across multiple videos. To address this gap, we introduce the Multi-Video Perception Evaluation Benchmark (MVPBench), a new benchmark featuring 14 subtasks across diverse visual domains designed to evaluate models on extracting relevant information from video sequences to make informed decisions. MVPBench includes 5K question-answering tests involving 2.7K video clips sourced from existing datasets and manually annotated clips. Extensive evaluations reveal that current models struggle to process multi-video inputs effectively, underscoring substantial limitations in their multi-video comprehension. We anticipate MVPBench will drive advancements in multi-video perception.
@article{arxiv.2603.22756,
title = {MVPBench: A Multi-Video Perception Evaluation Benchmark for Multi-Modal Video Understanding},
author = {Purui Bai and Tao Wu and Jiayang Sun and Xinyue Liu and Huaibo Huang and Ran He},
journal= {arXiv preprint arXiv:2603.22756},
year = {2026}
}
Comments
15 pages, 7 figures, accepted by IJCNN 2026, code and dataset available at https://github.com/MVPBench/MVPBench