@inproceedings{lai-etal-2026-enhancing,
title = "Enhancing linear attention with residual learning",
author = "Lai, Xunhao and
Kang, Jialiang and
Lu, Jianqiao and
Zhao, Pengyu and
Lin, Tong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2394/",
pages = "54422--54434",
ISBN = "979-8-89176-459-0",
abstract = "Linear attention offers a linear-time alternative to self-attention but often struggles to capture long-range patterns. We revisit linear attention through a prediction-correction lens and show that prevalent variants can be written as a combination of a historical prediction and a single-token correction, which creates an expressivity bottleneck. To address this bottleneck, we introduce Residual Linear Attention (RLA), a framework that equips linear attention with an explicit residual-fitting mechanism. RLA maintains an auxiliary recurrent state that learns to accumulate residual errors over time and correct the base prediction. We further instantiate a delta-rule version, Residual Delta Net (RDN), incorporating adaptive gating and residual clipping for enhanced correction control and stability. Our implementation leverages highly optimized linear attention kernels and preserves linear time and memory. Across language modeling and recall-intensive evaluations, RLA and RDN consistently outperform their respective baselines and other modern linear-attention methods, narrowing the gap to standard Transformers while retaining linear scaling."
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%0 Conference Proceedings
%T Enhancing linear attention with residual learning
%A Lai, Xunhao
%A Kang, Jialiang
%A Lu, Jianqiao
%A Zhao, Pengyu
%A Lin, Tong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lai-etal-2026-enhancing
%X Linear attention offers a linear-time alternative to self-attention but often struggles to capture long-range patterns. We revisit linear attention through a prediction-correction lens and show that prevalent variants can be written as a combination of a historical prediction and a single-token correction, which creates an expressivity bottleneck. To address this bottleneck, we introduce Residual Linear Attention (RLA), a framework that equips linear attention with an explicit residual-fitting mechanism. RLA maintains an auxiliary recurrent state that learns to accumulate residual errors over time and correct the base prediction. We further instantiate a delta-rule version, Residual Delta Net (RDN), incorporating adaptive gating and residual clipping for enhanced correction control and stability. Our implementation leverages highly optimized linear attention kernels and preserves linear time and memory. Across language modeling and recall-intensive evaluations, RLA and RDN consistently outperform their respective baselines and other modern linear-attention methods, narrowing the gap to standard Transformers while retaining linear scaling.
%U https://aclanthology.org/2026.emnlp-main.2394/
%P 54422-54434
Markdown (Informal)
[Enhancing linear attention with residual learning](https://aclanthology.org/2026.emnlp-main.2394/) (Lai et al., EMNLP 2026)
ACL
- Xunhao Lai, Jialiang Kang, Jianqiao Lu, Pengyu Zhao, and Tong Lin. 2026. Enhancing linear attention with residual learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 54422–54434, Budapest, Hungary. Association for Computational Linguistics.