@inproceedings{sun-etal-2026-rectified,
title = "Rectified Sparse Attention for Efficient Long-Sequence Generation",
author = "Sun, Yutao and
Ye, Tianzhu and
Dong, Li and
Xia, Yuqing and
Chen, Jian and
Gao, Yizhao and
Cao, Shijie and
Wang, Jianyong and
Wei, Furu",
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.348/",
pages = "7023--7034",
ISBN = "979-8-89176-395-1",
abstract = "Efficient long-sequence generation is a critical challenge for Large Language Models. While recent sparse decoding methods improve efficiency, they suffer from KV cache misalignment, where approximation errors accumulate and degrade generation quality. In this work, we propose Rectified Sparse Attention (ReSA), a simple yet effective method that combines block-sparse attention with periodic dense rectification. By refreshing the KV cache at fixed intervals using a dense forward pass, ReSA bounds error accumulation and preserves alignment with the pretraining distribution. Experiments across math reasoning, language modeling, and retrieval tasks demonstrate that ReSA achieves near-lossless generation quality with significantly improved efficiency. Notably, ReSA delivers up to 3.77x end-to-end speedup under decoding at 256K sequence length, making it a practical solution for scalable long-context inference."
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<abstract>Efficient long-sequence generation is a critical challenge for Large Language Models. While recent sparse decoding methods improve efficiency, they suffer from KV cache misalignment, where approximation errors accumulate and degrade generation quality. In this work, we propose Rectified Sparse Attention (ReSA), a simple yet effective method that combines block-sparse attention with periodic dense rectification. By refreshing the KV cache at fixed intervals using a dense forward pass, ReSA bounds error accumulation and preserves alignment with the pretraining distribution. Experiments across math reasoning, language modeling, and retrieval tasks demonstrate that ReSA achieves near-lossless generation quality with significantly improved efficiency. Notably, ReSA delivers up to 3.77x end-to-end speedup under decoding at 256K sequence length, making it a practical solution for scalable long-context inference.</abstract>
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%0 Conference Proceedings
%T Rectified Sparse Attention for Efficient Long-Sequence Generation
%A Sun, Yutao
%A Ye, Tianzhu
%A Dong, Li
%A Xia, Yuqing
%A Chen, Jian
%A Gao, Yizhao
%A Cao, Shijie
%A Wang, Jianyong
%A Wei, Furu
%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 sun-etal-2026-rectified
%X Efficient long-sequence generation is a critical challenge for Large Language Models. While recent sparse decoding methods improve efficiency, they suffer from KV cache misalignment, where approximation errors accumulate and degrade generation quality. In this work, we propose Rectified Sparse Attention (ReSA), a simple yet effective method that combines block-sparse attention with periodic dense rectification. By refreshing the KV cache at fixed intervals using a dense forward pass, ReSA bounds error accumulation and preserves alignment with the pretraining distribution. Experiments across math reasoning, language modeling, and retrieval tasks demonstrate that ReSA achieves near-lossless generation quality with significantly improved efficiency. Notably, ReSA delivers up to 3.77x end-to-end speedup under decoding at 256K sequence length, making it a practical solution for scalable long-context inference.
%U https://aclanthology.org/2026.findings-acl.348/
%P 7023-7034
Markdown (Informal)
[Rectified Sparse Attention for Efficient Long-Sequence Generation](https://aclanthology.org/2026.findings-acl.348/) (Sun et al., Findings 2026)
ACL
- Yutao Sun, Tianzhu Ye, Li Dong, Yuqing Xia, Jian Chen, Yizhao Gao, Shijie Cao, Jianyong Wang, and Furu Wei. 2026. Rectified Sparse Attention for Efficient Long-Sequence Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 7023–7034, San Diego, California, United States. Association for Computational Linguistics.