OpenCoF: Learning to Reason Through Video Generation

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Xinyan Chen, Ziyu Guo, Renrui Zhang, Dongzhi Jiang, Hongsheng Li
• Venue: arXiv: cs.CV
• Published: 2026-07-09
• Collected: 2026-07-11
• URL: http://arxiv.org/abs/2607.08763v1
• PDF: https://arxiv.org/pdf/2607.08763v1
• DOI: UNKNOWN

Plain-Language Brief

The paper introduces OpenCoF, a framework that includes a new dataset and a fine-tuned video model for improving Chain-of-Frame (CoF) reasoning in video generation. The authors argue that existing models lack specific training for CoF reasoning and are primarily trained on general video corpora. The new framework aims to address this gap by providing diverse temporal supervision and explicit mechanisms for organizing intermediate reasoning states. The fine-tuned model, Wan-CoF, shows significant improvements over the baseline in four video reasoning benchmarks.

What the Document Says

• The paper presents OpenCoF, a framework consisting of the OpenCoF-17K dataset and Wan-CoF, a fine-tuned video model for improving CoF reasoning.
• OpenCoF-17K is a diverse reasoning video dataset spanning 11 task families.
• Wan-CoF shows significant improvements over the baseline in four video reasoning benchmarks.
• The authors explore advanced designs for CoF capabilities, including visual and textual reasoning tokens that capture low-level visual cues and high-level semantic priors.

Key Concepts Mentioned

• Chain-of-Frame (CoF) reasoning
• video generation
• OpenCoF-17K dataset
• Wan-CoF
• video modeling
• temporal supervision
• visual and textual reasoning tokens

Claims Made by the Document

• Existing video generators lack dedicated designs for CoF reasoning.
• Diverse temporal supervision improves CoF behavior.
• Stronger video reasoning requires both broad temporal supervision and explicit mechanisms for organizing intermediate reasoning state.

Evidence Used

• The authors argue that existing models lack specific training for CoF reasoning due to being primarily trained on general video corpora.
• Wan-CoF achieves considerable gains over the baseline in four video reasoning benchmarks.
• Results suggest that stronger video reasoning requires both broad temporal supervision and explicit mechanisms for organizing intermediate reasoning state.

Methods / Approach

• Introducing a new dataset and fine-tuning a video model to improve CoF reasoning
• Exploring advanced designs for CoF capabilities with visual and textual reasoning tokens

Limitations Stated by the Authors

The authors do not discuss potential limitations or challenges in the collection or curation of the OpenCoF-17K dataset.

Limitations Noted by the Agent

Agent interpretation — not from the source.

It is unclear from the provided text whether the authors have considered or addressed potential biases in the data or the model's performance across different task families.

Open Questions

• What are the specific designs implemented to equip the model with visual and textual reasoning tokens?
• How does the model perform across all 11 task families in the OpenCoF-17K dataset?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper focuses on the introduction of the OpenCoF framework, its components, and the performance of Wan-CoF. It provides some insights into how visual and textual reasoning tokens contribute to the model's performance but does not go into detail about their specific design.

Raw Abstract / Excerpt

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 diverse temporal supervision improves CoF behavior. Across four video reasoning benchmarks, Wan-CoF achieves considerable gains over the Wan2.2-I2V-A14B baseline. Building on this, we empirically explore more advanced designs for CoF capabilities, i.e., equipping the model with visual and textual reasoning tokens. This mechanism respectively captures low-level visual cues and high-level semantic priors for spatial and temporal reasoning. Through performance comparisons and attention analysis, we examine how these tokens contribute across model depth, denoising steps, space, and time. Our results suggest that stronger video reasoning requires both broad temporal supervision and explicit mechanisms for organizing intermediate reasoning state. We open-source the dataset, model, and code to facilitate future research on reasoning-oriented video generation.