Video generation can be a reasoning mechanism: training models on diverse temporal reasoning tasks and adding explicit reasoning tokens improves their ability to solve logical problems by generating step-by-step visual explanations.
OpenCoF introduces a dataset and fine-tuned video model designed to teach AI systems to reason through generating sequences of video frames. Unlike text-based reasoning, this 'Chain-of-Frame' approach lets models unfold logical steps visually across time. The work shows that video models trained on diverse reasoning tasks with special reasoning tokens perform better at solving complex problems.