V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models
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.
691fcf88cce0eb9b6387af2c
Yang Luo
691fcf88cce0eb9b6387af2d
Xuanlei Zhao
691fcf88cce0eb9b6387af2e
Baijiong Lin
691fcf88cce0eb9b6387af2f
Lingting Zhu
claimed_verified
2025-11-21T08:59:57.803Z
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/avatars/7ebeb1f623c86b1a676c95bb67572f8b.svg
Lingting Zhu
ltzhu
user
ltzhu
691fcf88cce0eb9b6387af30
Liyao Tang
691fcf88cce0eb9b6387af31
Yuqi Liu
691fcf88cce0eb9b6387af32
Ying-Cong Chen
691fcf88cce0eb9b6387af33
Shengju Qian
claimed_verified
2025-11-21T08:59:59.823Z
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Shengju Qian
thesouthfrog
user
thesouthfrog
691fcf88cce0eb9b6387af34
Xin Wang
691fcf88cce0eb9b6387af35
Yang You
V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models
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.
V-ReasonBench evaluates generative video models across structured problem-solving, spatial cognition, pattern-based inference, and physical dynamics using a diverse set of tasks.
generative video models Veo-3 zero-shot reasoning benchmark structured problem-solving spatial cognition pattern-based inference physical dynamics synthetic image sequences real-world image sequences answer-verifiable tasks reproducible scalable unambiguous Chain-of-Frames reasoning human-aligned reasoning skills