[Submitted on 17 Sep 2026]
Title:RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language Models
View a PDF of the paper titled RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language Models, by Chuxu Song and 2 other authors
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Abstract:Long-context large language model inference is increasingly limited by prefill, where dense self-attention processes the entire prompt before generation begins. Sparse block selection can reduce this cost, but a block centroid may hide a highly relevant token among many irrelevant ones. We call this failure mode mean dilution and propose RBS-Attention, a training-free sparse-prefill method with two complementary selection branches. A centroid base branch captures average relevance, while a rescue branch uses the maximum key-block radius and its prompt-, layer-, and head-dependent distribution to identify blocks at risk of underestimation. Independently thresholding the two branches and combining their masks controls the contribution of rescue blocks while preserving regular block-sparse FlashAttention execution. On H100 GPUs, RBS-Attention achieves 20.65$\times$ standalone prefill-attention speedup, 11.92$\times$ vLLM prefill-attention speedup, and 5.97$\times$ end-to-end time-to-first-token speedup at 128K on Qwen3-30B-A3B-Instruct-2507-FP8. On the dense Qwen3-32B model, it obtains 88.65 overall RULER accuracy versus 89.52 for dense attention; LongBench-v2, InfiniteBench, and Video-MME provide additional quality evaluation. Supporting experiments measure actual retention, compare selectors at matched density, and characterize block-size, threshold, and memory behavior. Together, these results support radius-adaptive dual-branch selection as an effective approach to long-context prefill.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.20971 [cs.AI]
(or arXiv:2609.20971v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.20971
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jiuqi Wei [view email] [v1] Thu, 17 Sep 2026 18:28:29 UTC (458 KB)
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