OpenCoF: Learning to Reason Through Video Generation
Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences. Recent video generation models offer a reasoning path distinct from previous Chain-of-Thought (CoT): reasoning can unfold through temporally connected frames, known as Chain-of-Frame (CoF) reasoning. However, existing video generators are primarily trained on general video corpora, still lacking diverse supervision and dedicated designs for CoF reasoning. To address this gap, we introduce OpenCoF, a framework comprising the OpenCoF-17K dataset, a reasoning video dataset spanning 11 task families, and Wan-CoF, a fine-tuned video model for studying whether ...
| Authors | Xinyan Chen, Ziyu Guo, Renrui Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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