@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://preview.aclanthology.org/ingest-acl/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."
}