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 principle of Multi-head Latent Attention across indexer layers. Each layer group constructs a shared latent cache from its first layer's hidden states, while layer-specific scoring enables independent token selection. Absorbing key decoders into queries enables direct scoring of the shared cache without reconstructing historical per-layer keys. We develop training-free calibration ...
| Authors | Zhaohui Wang, Zhixin Pan, Fanxu Meng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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