OpenCoF: Learning to Reason Through Video Generation
Score
78首次将视频生成与推理学习系统结合，提出Chain-of-Frame范式，信息增量较高。
论文结构完整，实验设计严谨，数据集开源，方法有理论分析。
提供开源数据集和模型，可直接用于视频推理任务，但当前性能距离实用仍有差距。
2025年7月最新论文，紧跟视频生成与推理结合的前沿热点。
arXiv预印本，来自可信学术来源，代码和数据集已开源，可验证。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper is solid in methodological innovation, dataset construction, and experimental validation, with open-source resources enhancing reproducibility, though direct comparison with pure text reasoning is missing.
AI commentary
「This is one of the most exciting multimodal reasoning papers I've seen recently. It breaks the traditional text CoT framework and leverages the inherent temporal advantage of video generation for reasoning, which is highly inspiring. If you are interested in embodied intelligence, multimodal reasoning, or video generation, this is a must-read. The only drawback is that the benchmarks are still small-scale; I look forward to future extensions to more complex scenarios.」
Quick overview
OpenCoF is a new framework for reasoning-oriented video generation, comprising the OpenCoF-17K dataset covering 11 task families and the fine-tuned Wan-CoF model. Unlike traditional Chain-of-Thought, OpenCoF performs reasoning through temporally connected frames (Chain-of-Frame), achieving significant gains over the Wan2.2-I2V-A14B baseline on four video reasoning benchmarks. The paper also explores visual and textual reasoning tokens to capture low-level visual cues and high-level semantic priors for spatial and temporal reasoning. Results show that stronger video reasoning requires broad temporal supervision and explicit mechanisms for organizing intermediate reasoning states. The dataset, model, and code are open-sourced.
- Introduces Chain-of-Frame (CoF) reasoning paradigm using temporally connected video frames instead of text Chain-of-Thought.
- Builds OpenCoF-17K dataset and Wan-CoF model covering 11 reasoning task families.
- Adds visual and textual reasoning tokens to handle low-level visual cues and high-level semantic priors.
- Significantly outperforms baseline on four video reasoning benchmarks and open-sources all resources.
Full text
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