Author

Fan-Xu Meng

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#artificial intelligence Preprint Oct 2026

LatentIndex: Cross-Layer Sharing with Layer-Specific Selection for Sparse Attention

Sparse attention reduces core-attention computation, but its indexers still incur repeated selection work and per-layer key-cache storage. Reusing selected indices across layers reduces this overhead but constrains multiple layers to the same token set. We introduce LatentIndex, which extends the latent-sharing princip...

Zhao-Hui Wang, Zhi-Xin Pan, Fan-Xu Meng et al. · 0 citations

#artificial intelligence Mar 2026

HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention

This work proposes HISA (Hierarchical Indexed Sparse Attention), a plug-and-play replacement for the indexer that rewrites the search path from a flat token scan into a two-stage hierarchical procedure: a block-level coarse filtering stage that scores pooled block representations to discard irrelevant regions, followed...

Yufei Xu, Fan-Xu Meng, Fan Jiang et al. · arXiv.org · 10 citations · ⚡1