The paper introduces Stratified Sparse Attention (SSA), a framework that blends deterministic top‑k retrieval with score‑guided sampling to reduce the gap between sparse and dense attention in large language models. SSA attends to high‑scoring tokens deterministically while sampling the remaining attention mass using proxy scores, thereby recovering more of the attention distribution. Experiments on benchmarks such as RULER, LongBench, LOFT, and AIME show that SSA outperforms existing sparse methods across models ranging from 3B to 27B parameters, achieving significant accuracy gains and up to 2.96× decode speedup with optimized CUDA kernels.
By Sahil Joshi, Prithvi Dixit, Aryan Katiyar, Agniva Chowdhury, Joseph E. Gonzalez, Anshumali Shrivastava, Aditya Desai, Kumar Krishna Agrawal
arXiv:2607. 09052v1 Announce Type: new Abstract: Block sparse attention is a hardware friendly way to alleviate the key-value (KV) cache read bottleneck in large language models (LLMs).
By Alexander Tian, Aditya Ghai, Sanjit Neelam, Zaal Vasania, Akshay Mishra
ClusterAttention is a training‑free technique that speeds up bidirectional attention by recursively clustering keys and queries into fixed‑size, power‑of‑two blocks, enabling block‑sparse attention to match dense attention latency on GPUs. The method derives error bounds for sparse attention, showing tighter clusters can reduce error when compensated via centroids, and demonstrates significant speedups—up to six‑fold on large tabular data and 1.8× on video generation—while preserving over 99% of dense accuracy.
By Kasper Nordenram, Amelie Dittmann
The paper introduces Pre-hoc Sparsity (PrHS), a method that selects key-value (KV) cache entries before attention scoring to avoid posterior bias in large language model inference. By bounding mutual‑information loss through the dropped attention mass, PrHS offers explicit accuracy control and implements three orthogonal selectors across time, depth, and layer. Experiments on LLaMA and Mistral models show that PrHS cuts retrieval overhead by over 90%, achieves higher sparsity than HShare, and delivers significant speedups and reduced FLOPs on NVIDIA A100 GPUs while maintaining near‑dense accuracy.
By Yifei Gao, Lei Wang, Rong-Cheng Tu, Qixin Zhang, Jun Cheng, Dacheng Tao
Block Sparse Flash Attention (BSFA) is a drop‑in replacement for FlashAttention that speeds up long‑context inference by pruning about 50% of computation and memory transfers. It selects the top‑k most important value blocks for each query using exact query‑key similarities and calibrated per‑layer, per‑head thresholds, requiring only a one‑time training‑free calibration. On Llama‑3.1‑8B, BSFA delivers up to 1.13× speedup on LongBench with a 1.1% accuracy drop and up to 1.24× on Needle‑in‑a‑Haystack retrieval with a 1% drop, while the attention kernel itself accelerates by up to 1.38×.
By Daniel Ohayon, Itay Lamprecht, Itay Hubara, Israel Cohen, Daniel Soudry, Noam Elata
arXiv:2606. 31519v1 Announce Type: new Abstract: Long-context Large Language Model inference is severely bottlenecked by the massive Key-Value (KV) cache, yet existing sparse attention methods often suffer from static fixed-budget (Top-k) retrieval or rely on proxy scores that are computationally expensive and biased.
By Wenhao Li, Jinhao Dong, Hailin Zhang, Wenhang Shi, Wei Lu, Xiaoyong Du
Sparse attention reduces the KV-cache traffic that dominates long-context LLM decoding, but practical methods largely operate at coarse granularity. Token-level selection offers finer control, yet pre...
arXiv:2508. 18224v3 Announce Type: replace-cross Abstract: Recent advances in sparse attention mechanisms have demonstrated strong potential for reducing the computational cost of long-context training and inference in large language models (LLMs).
By Ran Yan, Youhe Jiang, Zhuoming Chen, Haohui Mai, Beidi Chen, Binhang Yuan
arXiv:2607. 27692v1 Announce Type: cross Abstract: Top-$K$ sparse attention reduces the cost of Softmax and value aggregation by attending to only a small subset of key--value (KV) entries.
By Wenshuai Yao, Wenyong Zhou, Hanyong Shao, Yizhe Chen, Zhiyuan Ning, Yuannuo Feng, Ru Huang, Kechao Tang
The paper introduces FlashAttention with Statistics, a method that computes attention‑based detector features without materializing the full attention matrix, thereby reducing memory usage and latency. By accumulating statistics during the FlashAttention pass or via sparse lookups, the approach adds only a small overhead—16% at 2K tokens and 1.4% at 35K tokens on an H100 GPU—and stores just 22.5 KiB per response token. Experiments show that frozen Qwen3 proxy models, even as small as 0.6 B parameters, can serve as strong hallucination detectors, outperforming larger zero‑shot judges and specialized grounding checkers on several benchmarks, with attention features further boosting performance.
By Andrei Volodichev, Maksim Savichev, Nikita Sviridov, Artem Yuliy, Dmitry Simakov, Alexey Zaytsev
RBS-Attention introduces a training‑free, radius‑bounded sparse prefill strategy for long‑context large language models, addressing the mean dilution problem where a block centroid can miss highly relevant tokens. The method employs two complementary selection branches: a centroid base branch that captures average relevance and a rescue branch that uses the maximum key‑block radius to flag under‑estimated blocks. Experiments on Qwen3 models demonstrate significant speedups—over 20× in standalone prefill‑attention and nearly 6× in end‑to‑first‑token time—while maintaining competitive accuracy compared to dense attention.
By Chuxu Song, Jiuqi Wei, Zhencan Peng
Joint Subspace Sparse Attention (JSSA) is a new method that reduces KV‑cache traffic in long‑context LLM decoding by selecting tokens at the token level while preserving query–key structure. It learns a shared low‑dimensional query–key subspace through a tractable spectral solution and implements a gather‑free indexed‑attention kernel that supports per‑KV‑head selections. Across various attention architectures, JSSA maintains near full‑KV accuracy, avoids generation‑length inflation, and can achieve up to 6.43× decoding throughput on a single GPU for 128K context, even at 700B‑parameter scale.
By Jiwon Song, Yoongon Kim, Jae-Joon Kim