Infrastructure & deployment
Partition the Support, Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models
SparsePR changes both how attention groups are formed and how sparse-output errors are corrected, aiming to preserve dense attention outputs while computing far fewer query-key pairs.
Why it matters: For video-generation and world-model attention, the reported results suggest a way to reduce computation without relying on attention mass alone as a proxy for output accuracy: SparsePR reports lower attention-output error and faster execution at roughly 22% executed-pair density.