DeCoPrune: Efficient KV-Cache Pruning for Autoregressive Video Diffusion via Denoising Consistency

Autoregressive video diffusion naturally supports streaming generation and interactive control, but its KV cache grows continuously with the generated history. Existing compression strategies either discard history using fixed windows or select tokens through local attention and similarity signals, which do not directly measure whether the current chunk contributes information beyond the retained context. We introduce DeCoPrune, a training-free method that treats cache compression as a denoising-consistency problem. We find empirically that denoising difficulty provides a useful proxy for a token's value in long-term retention: tokens with larger step-to-final discrepancies tend to carry visual evidence that is less predictable from the retained context. Based on this finding, DeCoPrune measures each current-chunk token's denoising difficulty using the discrepancy between its intermediate clean prediction and final denoised value, retaining high-discrepancy tokens in the long-term cache while pruning those with low discrepancy. To evaluate information retention, we introduce CMBench, comprising 58 approximately one-minute generated or real-world context episodes and 116 Reappear or Revisit continuation tasks that require recalling events or objects shown earlier in the context. Experiments with LingBot World v2 show that DeCoPrune achieves a DINO score of 0.6701 on a 0–1 scale with an 85.43% reduction in cumulative historical KV token counts and a 4.14× continuation-generation speedup over FullKV. Its head-specialized variant further reaches 0.6783 at an 86.19% pruning ratio, approaching the 0.6803 score of FullKV and exceeding the evaluated compression baselines at similar budgets. These results indicate that denoising consistency can serve as a model-intrinsic signal for retaining long-range information while reducing autoregressive inference cost.