A survey that groups efficient video diffusion methods into four paradigms—step distillation, efficient attention, model compression, and cache/trajectory optimization—and outlines open challenges for practical use.
Generalized neighborhood attention: Multi-dimensional sparse attention at the speed of light
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A fixed-support SVD tensor factorization with tiling and bipartite matching yields an explicit zero-error achievable rate K/N for multi-user non-linear distributed computing under mild dimensionality conditions.
Cosmos-Predict2.5 unifies text-to-world, image-to-world, and video-to-world generation in one model trained on 200M clips with RL post-training, delivering improved quality and control for physical AI.
citing papers explorer
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Efficient Video Diffusion Models: Advancements and Challenges
A survey that groups efficient video diffusion methods into four paradigms—step distillation, efficient attention, model compression, and cache/trajectory optimization—and outlines open challenges for practical use.
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Multi-User Non-Linearly Separable Distributed Computing
A fixed-support SVD tensor factorization with tiling and bipartite matching yields an explicit zero-error achievable rate K/N for multi-user non-linear distributed computing under mild dimensionality conditions.
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World Simulation with Video Foundation Models for Physical AI
Cosmos-Predict2.5 unifies text-to-world, image-to-world, and video-to-world generation in one model trained on 200M clips with RL post-training, delivering improved quality and control for physical AI.