[Paper] A Very Big Video Reasoning Suite

Source: arXiv - 2602.20159v1

Overview

A new dataset called Very Big Video Reasoning (VBVR) pushes video AI beyond just recognizing objects and actions. By providing over 1 million curated video clips across 200 reasoning tasks, the authors give researchers a massive playground to test and train models that can reason about continuity, interaction, and causality in videos—abilities that are crucial for real‑world AI systems.

Key Contributions

• VBVR Dataset: ~1 M video clips covering 200 carefully designed reasoning tasks, three orders of magnitude larger than any existing video reasoning benchmark.
• Taxonomy‑Driven Task Design: A principled hierarchy (e.g., temporal continuity, physical interaction, causal inference) that ensures coverage of core reasoning skills.
• VBVR‑Bench Evaluation Suite: Combines rule‑based scorers, human‑aligned metrics, and reproducible pipelines to provide transparent, verifiable performance numbers.
• Large‑Scale Scaling Study: Systematic experiments showing how model size, data volume, and training regime affect video reasoning, with early evidence of emergent generalization to unseen tasks.
• Open‑Source Release: Dataset, benchmark code, and baseline models are publicly available at https://video-reason.com/, encouraging community contributions.

Methodology

1. Task Taxonomy Construction – The team first identified fundamental reasoning primitives (e.g., “object permanence,” “cause‑effect,” “temporal ordering”) and built a hierarchical taxonomy. Each leaf node became a concrete task with a clear success criterion.
2. Data Curation at Scale – Using a mix of automated video mining (YouTube, stock footage) and human verification, they assembled >1 M clips. Scripts enforce spatiotemporal consistency (e.g., ensuring the same objects appear throughout a clip).
3. Benchmark Design (VBVR‑Bench) – Instead of relying solely on black‑box model scores, they implemented rule‑based scorers (e.g., checking whether a predicted temporal order matches ground‑truth) and calibrated them against a small set of human judgments to guarantee alignment.
4. Scaling Experiments – They trained several transformer‑based video models (from 100 M to 2 B parameters) on varying fractions of the dataset, measuring performance across all 200 tasks.
5. Generalization Evaluation – A held‑out “unseen‑task” split tests whether knowledge learned on one reasoning category transfers to novel categories.

Results & Findings

• Performance Grows Log‑Linearly with both model size and data volume, mirroring trends seen in language models.
• Emergent Generalization: Models trained on 70 % of the tasks achieve >60 % accuracy on completely unseen tasks, suggesting the emergence of transferable reasoning primitives.
• Rule‑Based Scorers Provide Fine‑Grained Insight: They expose failure modes (e.g., models excel at temporal ordering but lag on causal inference) that pure accuracy metrics hide.
• Baseline Gap: Even the largest 2 B‑parameter model falls short of human‑aligned scores on many tasks, highlighting ample room for improvement.

Practical Implications

• Robust Video Understanding for Products – Applications like autonomous driving, video surveillance, and AR/VR can benefit from models that understand “what will happen next” rather than just “what is happening now.”
• Better Data Efficiency – The scaling curves give engineers concrete guidance on how much data and compute are needed to reach a target reasoning capability.
• Benchmark‑Driven Development – VBVR‑Bench’s transparent scoring lets teams iterate quickly, diagnose specific reasoning weaknesses, and benchmark against a community standard without costly human annotation loops.
• Foundation for Multimodal Agents – Reasoning over video is a key ingredient for agents that combine vision, language, and action (e.g., embodied AI assistants).

Limitations & Future Work

• Domain Bias – The source videos are largely curated from publicly available platforms, which may under‑represent industrial or safety‑critical domains.
• Rule‑Based Scorer Coverage – While helpful, rule‑based metrics cannot capture every nuance of human judgment; some tasks still rely on limited human validation.
• Compute Requirements – Training the largest models demands substantial GPU clusters, which may be prohibitive for smaller labs.
• Future Directions: Extending the taxonomy to include social reasoning (e.g., intent inference), integrating interactive simulation environments for closed‑loop learning, and exploring efficient fine‑tuning methods to lower the compute barrier.

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The VBVR suite opens the door to the next generation of video AI—systems that don’t just see, but reason about what they see.

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

Paper Information

• arXiv ID: 2602.20159v1
• Categories: cs.CV, cs.AI, cs.LG, cs.MM, cs.RO
• Published: February 23, 2026
• PDF: Download PDF