Future Forcing: Future-aware Training-free KV Cache Policy for Autoregressive Video Generation
Autoregressive (AR) video generation has emerged as a promising paradigm for long-horizon video synthesis, where each frame is generated conditioned on previously generated tokens. To accelerate inference, the KV cache is used to avoid redundant recomputation across generation steps. Nevertheless, its growth with generation length introduces increasing memory and error accumulation, limiting the scalability of AR models to even longer sequences. Existing KV cache compression methods mitigate this issue by selectively retaining only video tokens deemed important. However, most existing methods assess token importance using short-horizon signals derived from the current or historical genera...
| Authors | Jiayi Luo, Qiyan Liu, Tengyang Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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