Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention
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. However, their core attention layers become a major bottleneck at inference time: as generation progresses, the KV cache grows, causing both increasing latency and escalating GPU memory, which in turn restricts usable temporal context and harms long-range consistency. In this work, we study redundancy in autoregressive video diffusion and identify three persistent sources: n
Advances in AI research are continuously pushing the boundaries of what is computationally feasible for complex tasks like high-fidelity video generation and world modeling.
This development addresses critical bottlenecks in current video generation and streaming AI models, enabling more efficient and scalable long-form synthesis and interactive AI applications.
The ability to generate long-form, consistent video and create interactive neural game engines becomes significantly more practical and less resource-intensive.
- · AI research labs
- · Gaming industry
- · Content creators
- · Video streaming platforms
- · Companies reliant on static content
- · Inefficient video generation methods
- · High-latency interactive AI systems
More sophisticated and real-time interactive AI experiences become widely accessible.
The cost and computational demands for generating high-quality, long-form video content decrease significantly, democratizing access to advanced synthesis capabilities.
The development of truly dynamic and adaptive AI world models could accelerate the path towards general AI agents interacting with complex virtual environments.
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Read at arXiv cs.AI