OpenCoF: Learning to Reason Through Video Generation
Teaching AI to reason by generating step-by-step videos instead of text
Researchers created a new dataset and video generation model that teaches AI systems to solve reasoning problems by generating sequences of video frames rather than text explanations. The model, called Wan-CoF, substantially outperformed existing video generators on four reasoning benchmarks by learning from diverse examples of visual problem-solving and using special tokens to track reasoning across frames.
Video-based reasoning could help AI systems explain their logic in ways that are easier for humans to follow and verify, especially in domains where visual understanding matters—like robotics, medical diagnosis, or scientific discovery. This work also establishes a new training approach that could make AI reasoning more transparent and grounded in real-world sequences rather than abstract text.