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arXiv AI By Pardis Taghavi, Reza Langari, Gaurav Pandey

Partition the Support, Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models

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The paper introduces SparsePR, a training‑free block‑sparse attention method for video transformers that partitions query‑key responses and reconstructs the residual via probe‑fitted affine corrections. By pairing sampled‑query key responses into K/V groups and using centroids to guide shared routing, SparsePR reduces attention‑reconstruction error across diverse video generation and world‑model tasks. Experiments show consistent error reductions, with probe fitting contributing most of the improvement, while maintaining generation quality at 22.0–26.0% executed‑pair density and delivering 1.48×–2.61× speedups.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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