📄 论文总结
V-ReasonBench：面向视频生成模型的统一推理基准测试套件 / V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models
1️⃣ 一句话总结
这篇论文提出了一个名为V-ReasonBench的基准测试工具，用于系统评估视频生成模型在结构化问题解决、空间认知、模式推理和物理动态四个关键维度的推理能力，帮助开发更可靠、符合人类思维的AI模型。
🤖 系统
Abstract - 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.