V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models

• URL: http://arxiv.org/abs/2511.16668v1
• Date: Thu, 20 Nov 2025 18:59:42 GMT
• Title: V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models
• Authors: Yang Luo, Xuanlei Zhao, Baijiong Lin, Lingting Zhu, Liyao Tang, Yuqi Liu, Ying-Cong Chen, Shengju Qian, Xin Wang, Yang You,
• Abstract summary: V-ReasonBench is a benchmark designed to assess video reasoning across four key dimensions.<n> Evaluations of six state-of-the-art video models reveal clear dimension-wise differences.<n>Overall, V-ReasonBench offers a unified and reproducible framework for measuring video reasoning.
• Score: 52.97290143922252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress in generative video models, such as Veo-3, has shown surprising zero-shot reasoning abilities, creating a growing need for systematic and reliable evaluation. We introduce V-ReasonBench, a benchmark designed to assess video reasoning across four key dimensions: structured problem-solving, spatial cognition, pattern-based inference, and physical dynamics. The benchmark is built from both synthetic and real-world image sequences and provides a diverse set of answer-verifiable tasks that are reproducible, scalable, and unambiguous. Evaluations of six state-of-the-art video models reveal clear dimension-wise differences, with strong variation in structured, spatial, pattern-based, and physical reasoning. We further compare video models with strong image models, analyze common hallucination behaviors, and study how video duration affects Chain-of-Frames reasoning. Overall, V-ReasonBench offers a unified and reproducible framework for measuring video reasoning and aims to support the development of models with more reliable, human-aligned reasoning skills.

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