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Velox routes video-world attention by spatial memory and speeds chunk 48 denoising 2.43×

huggingface.co by Qiuhong Shen et al. Editor score 8/10

Velox uses camera-frustum overlap to select historical context, reporting 4.36× faster attention and 2.43× faster denoising at chunk 48. The approach targets growing KV-cache costs in long interactive video rollouts.

What

- Velox is a training-free attention router that selects region-level context for chunked video diffusion world models.

Method

- It represents history as camera frustum slabs and uses recency-priority multiwarp for current-block depth; overlap modulates importance for covered regions.

Why it matters

- By reducing attention growth with rollout length, it reports 2.43× faster denoising at chunk 48 while staying within 0.15 average points of dense LingBot-Fast-Cam on WBench navigation.

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