Demystifing Video Reasoning

Recent advances in video generation have revealed an un-expected capability: diffusion-based video models can perform reason-ing through Chain-of-Frames (CoF), suggesting that reasoning unfoldssequentially across video frames. In this work, we revisit this assump-tion and uncover a different mechanism. Through systematic analysis,we show that video reasoning primarily develops along the diffusion de-noising steps instead, where early steps exhibit multiple hypotheses thatgradually converge. We term this mechanism Chain-of-Steps (CoS),which is validated through qualitative analysis and probing tests. Ourinvestigation reveals several intriguing emergent behaviors: 1) Modelsdemonstrate a form of persistent working memory that supports tasksrequiring consistent reference, such as object permanence. 2) They canself-correct intermediate mistakes during generation instead of com-mitting to incorrect trajectories. 3) Early steps function differently fromlater steps, exemplified by a perception before action phenomenon.Moreover, analysis of Diffusion Transformer layers shows that middlelayers conduct key reasoning procedures. Motivated by these insights,we propose Training-Free Ensemble (TFE), a simple strategy thatintegrates reasoning paths by merging latents from identical models withdifferent random seeds at inference time. This approach encourages theexploration of diverse reasoning trajectories and improves reasoning per-formance. Together, our findings provide the first systematic dissectionof the mechanisms underlying video reasoning and offer practical insightsfor developing more capable video reasoning models.