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Attn-Forcing is a new framework for streaming video generation that preserves full-context attention contributions while operating under bounded memory and computation. It introduces learnable visual latents as attention proxies, updated via a Proxy Update Module to incorporate historical visual context, and employs an Attention Matching Loss to explicitly supervise the proxies using full-context attention. Experiments show that Attn-Forcing consistently improves video generation quality and outperforms existing baseline methods.
Focused Forcing is a training‑free method for selecting key‑value (KV) cache entries in autoregressive video diffusion models. It selects historical frames per generated frame by combining attention and diversity scores, and allocates larger budgets to more important heads. The approach yields up to 1.48× faster generation while improving visual quality and text alignment across several generation paradigms.
arXiv:2610.10429v1 Announce Type: new Abstract: Autoregressive video generation requires denoising the current frames while writing their key-value representations as context for future predictions....
Recent advancements in MLLM-based long-form video understanding have mitigated inference-time computational cost and limited context lengths by selecting query-relevant frames. However, existing approaches predominantly rely on external proxy scorers and rigid heuristic rules, inevitably suffering from misalignment with the target MLLM's intrinsic evidence and failing to accommodate the non-uniform spatiotemporal information density.
arXiv:2602. 01801v2 Announce Type: replace-cross Abstract: Autoregressive video diffusion models enable streaming generation, opening the door to long-form synthesis, video world models, and interactive neural game engines.
arXiv:2606. 16353v1 Announce Type: cross Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets.