Token-level sparse attention mechanisms, exemplified by DeepSeek Sparse Attention (DSA), achieve fine-grained key selection by scoring every historical key for each query through a lightweight indexer, then computing attention only on the selected subset. While the downstream sparse attention itself scales favorably, the indexer must still scan the entire prefix for every query, introducing an per-layer bottleneck that grows prohibitively with context length. We propose 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: (1) a block-level coarse filtering stage that scores pooled block representations to discard irrelevant regions, followed by (2) a token-level refinement stage that applies the original indexer exclusively within the retained candidate blocks. HISA preserves the identical token-level top-sparse pattern consumed by the downstream Sparse MLA operator and requires no additional training. On kernel-level benchmarks, HISA achieves up to speedup at 64K context. On Needle-in-a-Haystack and LongBench, we directly replace the indexer in DeepSeek-V3.2 and GLM-5 with our HISA indexer, without any finetuning. HISA closely matches the original DSA in quality, while substantially outperforming block-sparse baselines.
@article{arxiv.2603.28458,
title = {HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention},
author = {Yufei Xu and Fanxu Meng and Fan Jiang and Yuxuan Wang and Ruijie Zhou and Zhaohui Wang and Jiexi Wu and Zhixin Pan and Xiaojuan Tang and Wenjie Pei and Tongxuan Liu and Di Yin and Xing Sun and Muhan Zhang},
journal= {arXiv preprint arXiv:2603.28458},
year = {2026}
}