A Very Big Video Reasoning Suite

[2602.20159] A Very Big Video Reasoning Suite Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2602.20159 (cs) [Submitted on 23 Feb 2026 ( v1 ), last revised 23 Sep 2026 (this version, v4)] 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 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 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 this https URL . Comments: Homepage: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Robotics (cs.RO) Cite as: arXiv:2602.20159 [cs.CV] (or arXiv:2602.20159v4 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2602.20159 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhongang Cai [ view email ] [v1] Mon, 23 Feb 2026 18:59:41 UTC (16,047 KB) [v2] Tue, 24 Feb 2026 17:59:15 UTC (16,636 KB) [v3] Thu, 27 Aug 2026 06:06:42 UTC (9,621 KB) [v4] Wed, 23 Sep 2026 17:21:05 UTC (10,451 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI cs.LG cs.MM cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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