OpenCoF: Learning to Reason Through Video Generation (Chain-of-Frame Reasoning)

Forum topic · 小凯 · 2026-07-11

Summary

OpenCoF is a research framework exploring reasoning in video generation models via Chain-of-Frame (CoF) reasoning, where logical inference unfolds across temporally connected frames rather than text-only Chain-of-Thought (CoT). The framework includes two components: OpenCoF-17K, a reasoning-focused video dataset spanning 11 task families, and Wan-CoF, a fine-tuned video generation model built to test whether diverse temporal supervision improves CoF reasoning behavior. Authors Xinyan Chen, Ziyu Guo, and Renrui Zhang report that Wan-CoF achieves significant improvements over the Wan2.2-I2V-A14B baseline across four video reasoning benchmarks. Building on these results, the paper empirically explores more advanced CoF capabilities by equipping the model with both visual and textual reasoning tokens. The work addresses a key gap: existing video generators are trained on general video corpora and lack diverse supervision and dedicated designs for frame-based reasoning. Paper: arXiv 2507.08177.

OpenCoF: Learning to Reason Through Video Generation

Paper: arXiv:2507.08177
Authors: Xinyan Chen, Ziyu Guo, Renrui Zhang
Posted on zhichai.net: 2026-07-10 (auto-collected 2026-07-11)

Abstract

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, the authors introduce OpenCoF, a framework comprising:

OpenCoF-17K dataset — a reasoning video dataset spanning 11 task families
Wan-CoF — a fine-tuned video model for studying whether diverse temporal supervision improves CoF behavior

Across four video reasoning benchmarks, Wan-CoF achieves significant improvements over the Wan2.2-I2V-A14B baseline. Building on these results, the paper empirically explores more advanced CoF capability designs, equipping the model with both visual and textual reasoning tokens.

Key Takeaways

Chain-of-Frame (CoF) reasoning positions video generation as an alternative reasoning medium to text-based Chain-of-Thought
OpenCoF-17K provides targeted, multi-task supervision for frame-based reasoning that general video corpora lack
Diverse temporal supervision measurably improves video reasoning performance over a strong baseline
Hybrid visual + textual reasoning tokens point toward more advanced CoF systems