Attend Locally, Remember Linearly: Linear Attention as Cross-Frame Memory for Autoregressive Video Diffusion
arXiv:2605.16579v2 Announce Type: replace-cross Abstract: Autoregressive (AR) video diffusion is a powerful paradigm for streaming and interactive video generation. However, its reliance on softmax self-attention leads to quadratic compute complexity in sequence length and memory usage due to key-value caching, which limits its scalability to long video horizons. Existing remedies (e.g., sparse attention and KV-cache compression) reduce per-step cost but still rely on a linearly growing cache or irreversibly discard past context, and thus fail to address linear memory growth and streaming cont
The continuous drive for more efficient and scalable AI models, especially in highly demanding applications like video generation, makes this research timely as current methods hit computational bottlenecks.
This development addresses a critical limitation in autoregressive video diffusion, enabling longer and more complex video generation, which is crucial for advanced AI agents and immersive digital experiences.
By overcoming the quadratic complexity of traditional attention mechanisms, this research allows for more scalable video generation, paving the way for applications previously limited by computational resources and memory.
- · AI research institutions
- · Video generation platforms
- · Content creators
- · Metaverse developers
- · Companies relying on less efficient video generation architectures
- · Hardware developers unable to adapt to new computational demands
More realistic and longer-form AI-generated videos become feasible, enhancing applications from entertainment to simulation.
This improved efficiency in video generation could accelerate the development and deployment of sophisticated AI agents that interact with and generate continuous visual information.
The reduced computational overhead for video AI might lead to a broader democratization of advanced video content creation, potentially impacting media industries and digital economies significantly.
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Read at arXiv cs.LG