Cache Aligned Distillation and Drift Correction for Autoregressive Video World Models

Few-step autoregressive video world models must remain responsive to actions while repeatedly conditioning on their own generated history. Few-step distillation enables interactivity in generated worlds, but its errors can accumulate as lost motion, drifting latent statistics, and damaged scene structure. Recent work has improved teacher causality and reduced the training–inference mismatch; yet the ODE stage has not trained a causal student to use its KV cache directly when learning from a causal teacher's rollouts. Our ODE stage trains a four-step student through its inference-time KV-cache path against recorded teacher rollouts, using spatially local Gaussian KL supervision in place of pointwise regression. Autoregressive models are also commonly trained with token normalisation, channel normalisation, and spatial normalisation; the lack of temporal normalisation, however, causes latent values to explode or vanish as rollouts progress. We therefore introduce temporal normalisation to constrain latent statistics against an evolving real-scene reference. Finally, we observe causal autoregressive degradation in the form of highly structured, non-Gaussian, data-dependent noise, and we introduce an online Causal AutoRegressive Noise (CARN) network to model and correct this degradation. On 32 held-out contexts and nine distinct actions, we evaluate 30-second continuations against five open interactive world models. At six seconds, 70.8% of our rollouts remain clean across all six evaluation axes, compared with 24.7% on average externally; relocation is 6.3% versus 55.6%, while directional-control failure remains comparable at 21.1% versus 20.7%. Across longer rollouts, no model dominates every axis: our model preserves actions, high-frequency detail and comparatively low relocation, but accumulates style degradation and geometry drift rather than modes that are harder to recover from such as early collapse or relocation.