Partition the Support, Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models
Block-sparse attention accelerates video transformers by grouping queries and keys and computing attention only between selected groups. However, it faces two challenges: (1) queries sharing a block route may attend to different keys; and (2) retaining most attention mass does not necessarily preserve the attention output. We find that partition choice affects both the overlap among grouped queries' preferred supports and how well an affine function of the sparse output can represent the difference between dense and sparse outputs. We introduce SparsePR, a training-free method combining Response-Coupled Partitioning with Probe-Fitted Residual Reconstruction. We first group paired key/valu...
| Authors | Pardis Taghavi, Reza Langari, Gaurav Pandey |
|---|---|
| Primary category | cs.CV |
| Published | 19 August 2026 |
| Last updated at source | 27 September 2026 |
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