Double-P: Hierarchical Top-P Sparse Attention for Long-Context LLMs

• URL: http://arxiv.org/abs/2602.05191v1
• Date: Thu, 05 Feb 2026 01:37:10 GMT
• Title: Double-P: Hierarchical Top-P Sparse Attention for Long-Context LLMs
• Authors: Wentao Ni, Kangqi Zhang, Zhongming Yu, Oren Nelson, Mingu Lee, Hong Cai, Fatih Porikli, Jongryool Kim, Zhijian Liu, Jishen Zhao,
• Abstract summary: Long-context inference becomes central to large language models.<n>Top-p sparse attention directly preserves attention mass and provides stronger accuracy guarantees.<n>Existing top-p methods fail to jointly optimize top-p accuracy, selection overhead, and sparse attention cost.
• Score: 45.84463775890072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As long-context inference becomes central to large language models (LLMs), attention over growing key-value caches emerges as a dominant decoding bottleneck, motivating sparse attention for scalable inference. Fixed-budget top-k sparse attention cannot adapt to heterogeneous attention distributions across heads and layers, whereas top-p sparse attention directly preserves attention mass and provides stronger accuracy guarantees. Existing top-p methods, however, fail to jointly optimize top-p accuracy, selection overhead, and sparse attention cost, which limits their overall efficiency. We present Double-P, a hierarchical sparse attention framework that optimizes all three stages. Double-P first performs coarse-grained top-p estimation at the cluster level using size-weighted centroids, then adaptively refines computation through a second top-p stage that allocates token-level attention only when needed. Across long-context benchmarks, Double-P consistently achieves near-zero accuracy drop, reducing attention computation overhead by up to 1.8x and delivers up to 1.3x end-to-end decoding speedup over state-of-the-art fixed-budget sparse attention methods.

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