CVBench: Evaluating Cross-Video Synergies for Complex Multimodal Understanding and Reasoning

• URL: http://arxiv.org/abs/2508.19542v2
• Date: Thu, 28 Aug 2025 14:22:38 GMT
• Title: CVBench: Evaluating Cross-Video Synergies for Complex Multimodal Understanding and Reasoning
• Authors: Nannan Zhu, Yonghao Dong, Teng Wang, Xueqian Li, Shengjun Deng, Yijia Wang, Zheng Hong, Tiantian Geng, Guo Niu, Hanyan Huang, Xiongfei Yao, Shuaiwei Jiao,
• Abstract summary: We present CVBench, the first comprehensive benchmark designed to assess cross-video relational reasoning rigorously.<n>CVBench comprises 1,000 question-answer pairs spanning three tiers: cross-video object association, cross-video event association, and cross-video complex reasoning.<n>Built from five domain-diverse video clusters, the benchmark challenges models to synthesise information across dynamic visual contexts.
• Score: 11.478276629279526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While multimodal large language models (MLLMs) exhibit strong performance on single-video tasks (e.g., video question answering), their ability across multiple videos remains critically underexplored. However, this capability is essential for real-world applications, including multi-camera surveillance and cross-video procedural learning. To bridge this gap, we present CVBench, the first comprehensive benchmark designed to assess cross-video relational reasoning rigorously. CVBench comprises 1,000 question-answer pairs spanning three hierarchical tiers: cross-video object association (identifying shared entities), cross-video event association (linking temporal or causal event chains), and cross-video complex reasoning (integrating commonsense and domain knowledge). Built from five domain-diverse video clusters (e.g., sports, life records), the benchmark challenges models to synthesise information across dynamic visual contexts. Extensive evaluation of 10+ leading MLLMs (including GPT-4o, Gemini-2.0-flash, Qwen2.5-VL) under zero-shot or chain-of-thought prompting paradigms. Key findings reveal stark performance gaps: even top models, such as GPT-4o, achieve only 60% accuracy on causal reasoning tasks, compared to the 91% accuracy of human performance. Crucially, our analysis reveals fundamental bottlenecks inherent in current MLLM architectures, notably deficient inter-video context retention and poor disambiguation of overlapping entities. CVBench establishes a rigorous framework for diagnosing and advancing multi-video reasoning, offering architectural insights for next-generation MLLMs. The data and evaluation code are available at https://github.com/Hokhim2/CVBench.

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