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[2602.20159] A Very Big Video Reasoning Suite
Summary
The paper introduces the Very Big Video Reasoning (VBVR) Dataset, a large-scale resource for studying video reasoning capabilities, featuring over one million video clips and 200 reasoning tasks.
Why It Matters
This research addresses the gap in video reasoning studies by providing a comprehensive dataset and evaluation framework, enabling advancements in understanding how AI can reason about spatiotemporal data. The findings could significantly impact fields like computer vision and AI, enhancing model capabilities in real-world applications.
Key Takeaways
- VBVR Dataset is three orders of magnitude larger than existing datasets.
- The dataset includes 200 curated reasoning tasks for comprehensive evaluation.
- VBVR-Bench offers a framework for reproducible and interpretable evaluations.
- The study reveals early signs of generalization in video reasoning tasks.
- Public availability of data and models supports further research in AI.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.20159 (cs) [Submitted on 23 Feb 2026] Title:A Very Big Video Reasoning Suite Authors:Maijunxian Wang, Ruisi Wang, Juyi Lin, Ran Ji, Thaddäus Wiedemer, Qingying Gao, Dezhi Luo, Yaoyao Qian, Lianyu Huang, Zelong Hong, Jiahui Ge, Qianli Ma, Hang He, Yifan Zhou, Lingzi Guo, Lantao Mei, Jiachen Li, Hanwen Xing, Tianqi Zhao, Fengyuan Yu, Weihang Xiao, Yizheng Jiao, Jianheng Hou, Danyang Zhang, Pengcheng Xu, Boyang Zhong, Zehong Zhao, Gaoyun Fang, John Kitaoka, Yile Xu, Hua Xu, Kenton Blacutt, Tin Nguyen, Siyuan Song, Haoran Sun, Shaoyue Wen, Linyang He, Runming Wang, Yanzhi Wang, Mengyue Yang, Ziqiao Ma, Raphaël Millière, Freda Shi, Nuno Vasconcelos, Daniel Khashabi, Alan Yuille, Yilun Du, Ziming Liu, Bo Li, Dahua Lin, Ziwei Liu, Vikash Kumar, Yijiang Li, Lei Yang, Zhongang Cai, Hokin Deng View a PDF of the paper titled A Very Big Video Reasoning Suite, by Maijunxian Wang and 55 other authors View PDF HTML (experimental) Abstract: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 da...
