University of California, Berkeley（加州大学伯克利分校）；Nanyang Technological University（南洋理工大学）；Northeastern University（东北大学）；University of Tübingen（图宾根大学）；Johns Hopkins University（约翰霍普金斯大学）；University of Michigan（密歇根大学）；University of Southern California（南加州大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）；Stanford University（斯坦福大学）；University of Texas at Austin（得克萨斯大学奥斯汀分校）；University of California, Los Angeles（加州大学洛杉矶分校）；Cornell University（康奈尔大学）；San Jose State University（圣何塞州立大学）；University of California, Irvine（加州大学尔湾分校）；Technical University of Munich（慕尼黑技术大学）；University of California, San Diego（加州大学圣地亚哥分校）；Imperial College London（伦敦帝国学院）；University of Wisconsin--Madison（威斯康星大学麦迪逊分校）；University of Edinburgh（爱丁堡大学）；Hong Kong University of Science（香港科学大学）；New York University（纽约大学）；Auburn University（阿伯丁大学）；Columbia University（哥伦比亚大学）；University of Bristol（布里斯托大学）；University of Waterloo（滑铁卢大学）；The Chinese University of Hong Kong（香港中文大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Oxford（牛津大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
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… 展开作者
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
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/?v=vbvr .
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite
State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所计算机体系结构国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Baidu（百度）；Alibaba（阿里巴巴）；Huawei（华为）；Tencent（腾讯）；Beijing Academy of Frontier Sciences and Technology（北京前沿科学技术研究院）
A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts
DISI, University of Trento（特伦托大学DISI学院）；DI, University of Pisa（比萨大学DI学院）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；University of Padova（帕多瓦大学）；University of Edinburgh（爱丁堡大学）；CIMeC, University of Trento（特伦托大学CIMeC中心）
VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
Nanyang Technological University（南洋理工大学）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Diego（加州大学圣地亚哥分校）；The University of Tokyo（东京大学）；The Chinese University of Hong Kong（香港中文大学）；University of Michigan（密歇根大学）；Johns Hopkins University（约翰霍普金斯大学）；University of California, Davis（加州大学戴维斯分校）；University of California, Los Angeles（加州大学洛杉矶分校）；Carnegie Mellon University（卡内基梅隆大学）；Columbia University（哥伦比亚大学）；University of Toronto（多伦多大学）
NI-UDA: Graph Adversarial Domain Adaptation from Non-shared-and-Imbalanced Big Data to Small Imbalanced Applications
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与通信工程学院）；School of Electrical and Information Engineering, Huaihua University（怀化学院电气与信息工程学院）；University of Macau（澳门大学）
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing
University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard University（哈佛大学）；Rush University Medical Center（拉什大学医学中心）
KFinEval-Pilot: A Comprehensive Benchmark Suite for Korean Financial Language Understanding
Korea Financial Telecommunications and Clearings Institute（韩国金融电信与清算院）；Teddysum Inc.（泰迪萨姆公司）；SELECTSTAR Inc.（SELECTSTAR公司）；Konyang University（建阳大学）；Seoultech（首尔科技大学）；KAIST（韩国科学技术院）
BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks
ServiceNow（ServiceNow公司）；Mila（米拉计算科学研究所）；École de Technologie Supérieure（高等技术学院）；University of Waterloo（滑铁卢大学）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；York University（约克大学）；University of British Columbia（不列颠哥伦比亚大学）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Polytechnique Montréal（蒙特利尔理工学院）
How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs
Mohamed Bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；ETH Zurich（苏黎世联邦理工学院）；Google（谷歌公司）；TU Munich（慕尼黑工业大学）；Linköping University（林雪平大学）；Australian National University（澳大利亚国立大学）
