Cache Homeostasis: Diagnosing and Controlling Feature Drift in Autoregressive Video Diffusion

Autoregressive video diffusion supports streaming generation by producing video segments sequentially and reusing historical features through a key–value cache (KV Cache). However, cached feature statistics can drift during long rollouts, potentially affecting video quality. To understand this problem, we study how cached value features evolve during long video generation. We find that their energy becomes increasingly concentrated in a small subset of feature directions. Crucially, this change is mainly driven by the growth of a component shared across tokens, rather than by the disappearance of differences between them. To distinguish these effects, we derive an exact decomposition that separates the contributions of shared energy, alignment, and centered variation. Experiments show that the shared component explains most of the observed concentration increase and that its growth is associated with lower aesthetic quality. Guided by this finding, we propose Cache Homeostasis, a training-free method that regulates newly written cache features relative to an early reference. Its lightweight version controls the shared component, while its full version also regulates excess energy in selected centered directions. Experiments demonstrate that reference-directed intervention suppresses abnormal cache growth, while the full version improves text consistency over the lightweight version, which introduces little computational cost.