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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2602.20159 |
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| _version_ | 1866911465837953024 |
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| author | Wang, Maijunxian Wang, Ruisi Lin, Juyi Ji, Ran Wiedemer, Thaddäus Gao, Qingying Luo, Dezhi Qian, Yaoyao Huang, Lianyu Hong, Zelong Ge, Jiahui Ma, Qianli He, Hang Zhou, Yifan Guo, Lingzi Mei, Lantao Li, Jiachen Xing, Hanwen Zhao, Tianqi Yu, Fengyuan Xiao, Weihang Jiao, Yizheng Hou, Jianheng Zhang, Danyang Xu, Pengcheng Zhong, Boyang Zhao, Zehong Fang, Gaoyun Kitaoka, John Xu, Yile Xu, Hua Blacutt, Kenton Nguyen, Tin Song, Siyuan Sun, Haoran Wen, Shaoyue He, Linyang Wang, Runming Wang, Yanzhi Yang, Mengyue Ma, Ziqiao Millière, Raphaël Shi, Freda Vasconcelos, Nuno Khashabi, Daniel Yuille, Alan Du, Yilun Liu, Ziming Li, Bo Lin, Dahua Liu, Ziwei Kumar, Vikash Li, Yijiang Yang, Lei Cai, Zhongang Deng, Hokin |
| author_facet | Wang, Maijunxian Wang, Ruisi Lin, Juyi Ji, Ran Wiedemer, Thaddäus Gao, Qingying Luo, Dezhi Qian, Yaoyao Huang, Lianyu Hong, Zelong Ge, Jiahui Ma, Qianli He, Hang Zhou, Yifan Guo, Lingzi Mei, Lantao Li, Jiachen Xing, Hanwen Zhao, Tianqi Yu, Fengyuan Xiao, Weihang Jiao, Yizheng Hou, Jianheng Zhang, Danyang Xu, Pengcheng Zhong, Boyang Zhao, Zehong Fang, Gaoyun Kitaoka, John Xu, Yile Xu, Hua Blacutt, Kenton Nguyen, Tin Song, Siyuan Sun, Haoran Wen, Shaoyue He, Linyang Wang, Runming Wang, Yanzhi Yang, Mengyue Ma, Ziqiao Millière, Raphaël Shi, Freda Vasconcelos, Nuno Khashabi, Daniel Yuille, Alan Du, Yilun Liu, Ziming Li, Bo Lin, Dahua Liu, Ziwei Kumar, Vikash Li, Yijiang Yang, Lei Cai, Zhongang Deng, Hokin |
| contents | 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/ . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20159 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Very Big Video Reasoning Suite Wang, Maijunxian Wang, Ruisi Lin, Juyi Ji, Ran Wiedemer, Thaddäus Gao, Qingying Luo, Dezhi Qian, Yaoyao Huang, Lianyu Hong, Zelong Ge, Jiahui Ma, Qianli He, Hang Zhou, Yifan Guo, Lingzi Mei, Lantao Li, Jiachen Xing, Hanwen Zhao, Tianqi Yu, Fengyuan Xiao, Weihang Jiao, Yizheng Hou, Jianheng Zhang, Danyang Xu, Pengcheng Zhong, Boyang Zhao, Zehong Fang, Gaoyun Kitaoka, John Xu, Yile Xu, Hua Blacutt, Kenton Nguyen, Tin Song, Siyuan Sun, Haoran Wen, Shaoyue He, Linyang Wang, Runming Wang, Yanzhi Yang, Mengyue Ma, Ziqiao Millière, Raphaël Shi, Freda Vasconcelos, Nuno Khashabi, Daniel Yuille, Alan Du, Yilun Liu, Ziming Li, Bo Lin, Dahua Liu, Ziwei Kumar, Vikash Li, Yijiang Yang, Lei Cai, Zhongang Deng, Hokin Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multimedia Robotics 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/ . |
| title | A Very Big Video Reasoning Suite |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multimedia Robotics |
| url | https://arxiv.org/abs/2602.20159 |