@inproceedings{nawrot-etal-2026-sparse,
title = "The Sparse Frontier: Sparse Attention Trade-offs in Transformer {LLM}s",
author = "Nawrot, Piotr and
Li, Jianing and
Huang, Renjie and
Ruder, Sebastian and
Marchisio, Kelly and
Ponti, Edoardo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1926/",
pages = "38667--38701",
ISBN = "979-8-89176-395-1",
abstract = "Sparse attention offers a promising strategy to extend long-context capabilities in Transformer LLMs, yet its efficiency{--}accuracy trade-offs remain unclear due to the lack of comprehensive evaluation. We address this gap with the largest-scale empirical analysis to date of training-free sparse attention, evaluating six methods across multiple model families and sizes, sequences up to 128K tokens, and sparsity levels up to 0.95 (i.e., 1/20 attention budget) on nine diverse tasks. We first organise the rapidly evolving landscape of sparse attention methods into a taxonomy along four design axes. Our analysis then yields actionable insights: 1) sparse attention is effective: larger sparse models outperform smaller dense ones at equivalent cost, improving the Pareto frontier; 2) for the training-free methods we study, fine-grained per-query importance estimation during prefilling remains impractical{---}due to both the cost of estimation and the lack of sparse kernels that translate fine-grained sparsity into wall-clock gains{---}forcing a task-dependent choice between global-to-token and block-to-block selection. Instead, during decoding, token-to-page selection becomes feasible, enabling better generalisation and higher sparsity tolerance; 3) longer sequences tolerate higher sparsity, suggesting that fixed-budget methods in production are suboptimal. Together, these findings provide practical guidance for deploying sparse attention and methodological recommendations for future evaluations. Our code is available at https://github.com/PiotrNawrot/sparse-frontier."
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<abstract>Sparse attention offers a promising strategy to extend long-context capabilities in Transformer LLMs, yet its efficiency–accuracy trade-offs remain unclear due to the lack of comprehensive evaluation. We address this gap with the largest-scale empirical analysis to date of training-free sparse attention, evaluating six methods across multiple model families and sizes, sequences up to 128K tokens, and sparsity levels up to 0.95 (i.e., 1/20 attention budget) on nine diverse tasks. We first organise the rapidly evolving landscape of sparse attention methods into a taxonomy along four design axes. Our analysis then yields actionable insights: 1) sparse attention is effective: larger sparse models outperform smaller dense ones at equivalent cost, improving the Pareto frontier; 2) for the training-free methods we study, fine-grained per-query importance estimation during prefilling remains impractical—due to both the cost of estimation and the lack of sparse kernels that translate fine-grained sparsity into wall-clock gains—forcing a task-dependent choice between global-to-token and block-to-block selection. Instead, during decoding, token-to-page selection becomes feasible, enabling better generalisation and higher sparsity tolerance; 3) longer sequences tolerate higher sparsity, suggesting that fixed-budget methods in production are suboptimal. Together, these findings provide practical guidance for deploying sparse attention and methodological recommendations for future evaluations. Our code is available at https://github.com/PiotrNawrot/sparse-frontier.</abstract>
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%0 Conference Proceedings
%T The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs
%A Nawrot, Piotr
%A Li, Jianing
%A Huang, Renjie
%A Ruder, Sebastian
%A Marchisio, Kelly
%A Ponti, Edoardo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F nawrot-etal-2026-sparse
%X Sparse attention offers a promising strategy to extend long-context capabilities in Transformer LLMs, yet its efficiency–accuracy trade-offs remain unclear due to the lack of comprehensive evaluation. We address this gap with the largest-scale empirical analysis to date of training-free sparse attention, evaluating six methods across multiple model families and sizes, sequences up to 128K tokens, and sparsity levels up to 0.95 (i.e., 1/20 attention budget) on nine diverse tasks. We first organise the rapidly evolving landscape of sparse attention methods into a taxonomy along four design axes. Our analysis then yields actionable insights: 1) sparse attention is effective: larger sparse models outperform smaller dense ones at equivalent cost, improving the Pareto frontier; 2) for the training-free methods we study, fine-grained per-query importance estimation during prefilling remains impractical—due to both the cost of estimation and the lack of sparse kernels that translate fine-grained sparsity into wall-clock gains—forcing a task-dependent choice between global-to-token and block-to-block selection. Instead, during decoding, token-to-page selection becomes feasible, enabling better generalisation and higher sparsity tolerance; 3) longer sequences tolerate higher sparsity, suggesting that fixed-budget methods in production are suboptimal. Together, these findings provide practical guidance for deploying sparse attention and methodological recommendations for future evaluations. Our code is available at https://github.com/PiotrNawrot/sparse-frontier.
%U https://aclanthology.org/2026.findings-acl.1926/
%P 38667-38701
Markdown (Informal)
[The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs](https://aclanthology.org/2026.findings-acl.1926/) (Nawrot et al., Findings 2026)
ACL
- Piotr Nawrot, Jianing Li, Renjie Huang, Sebastian Ruder, Kelly Marchisio, and Edoardo Ponti. 2026. The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 38667–38701, San Diego, California, United States. Association for Computational Linguistics.