FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion

Yu Lu, Junjie Yang, Piotr Koniusz, YuXin Song, Yi Yang

FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion: 0 upvotes on Hugging Face Daily Papers, #44 of 46 papers on 2026-06-10. Day-by-day upvote history.

Autoregressive video generators synthesize long videos by generating successive temporal segments, but their historical KV cache grows with video length. Existing bounded-cache methods reduce this cost with local windows, sink tokens, or compressed memory states, yet they usually assign fixed roles to different parts of the history. We propose FadeMem, a distance-aware KV memory consolidation mechanism that organizes historical KV blocks into a temporal hierarchy under a fixed cache budget. This design is motivated by frequency-dependent temporal decay: fine details decorrelate quickly, while coarse scene structure and identity remain useful over longer horizons. During generation, new history is inserted as fine-grained entries, while older adjacent entries are progressively merged under a power-law temporal allocation schedule, yielding a dense-near, sparse-far memory within one cache. Without architectural changes, FadeMem preserves recent context for short-term dynamics and compact long-range anchors for identity and scene coherence. Experiments show improved subject consistency, background stability, and temporal coherence over existing bounded-cache strategies.

Paper page on Hugging Face · arXiv

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