A Very Big Video Reasoning Suite
AI 摘要
论文提出了一个大规模视频推理数据集VBVR，并构建了可验证的评估框架VBVR-Bench，用于研究视频推理能力。
主要贡献
- 构建了大规模视频推理数据集VBVR
- 提出了可验证的评估框架VBVR-Bench
- 进行了视频推理能力的大规模扩展研究
方法论
构建大规模数据集，设计规则驱动的评分器，进行模型扩展性实验，分析推理能力。
原文摘要
Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture, enabling intuitive reasoning over spatiotemporal structure such as continuity, interaction, and causality. However, systematically studying video reasoning and its scaling behavior is hindered by the lack of large-scale training data. To address this gap, we introduce the Very Big Video Reasoning (VBVR) Dataset, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks following a principled taxonomy and over one million video clips, approximately three orders of magnitude larger than existing datasets. We further present VBVR-Bench, a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, enabling reproducible and interpretable diagnosis of video reasoning capabilities. Leveraging the VBVR suite, we conduct one of the first large-scale scaling studies of video reasoning and observe early signs of emergent generalization to unseen reasoning tasks. Together, VBVR lays a foundation for the next stage of research in generalizable video reasoning. The data, benchmark toolkit, and models are publicly available at https://video-reason.com/ .