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    <abstract>Linear attention reduces the quadratic cost of softmax attention by maintaining a recurrent fast-weight state, but it consistently lags on in-context retrieval and long-context tasks. Existing remedies act on the write side of memory through gating, delta updates, or kernel feature maps, but the read step is left unchanged: every past key contributes additively to the output, so useful targets are diluted by the bulk of stored vectors. We borrow one specific piece of softmax’s geometry to construct a cheap read-time contraction of the query. A second-order Taylor expansion of the softmax log-partition at the isotropic-attention point gives a local quadratic model whose curvature coincides with the running key covariance, a quantity that can be maintained with the same recurrent/chunkwise mechanism as the linear-attention state. The associated linear operator contracts the query along the high-variance directions of memory before it reads the state. We call this mechanism Curvature-Conditioned Query (CCQ). CCQ modifies only the read step and is composable with any linear-attention backbone. Attached to GLA and Gated DeltaNet, it improves perplexity, zero-shot downstream accuracy, S-NIAH retrieval at and beyond the training context, length-extrapolation perplexity from 4K to 20K, and LongBench accuracy.</abstract>
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%0 Conference Proceedings
%T Don’t Read Everything: A Curvature-Conditioned Query for Linear Attention
%A Le, Dong
%A Nguyen, Thong Thanh
%A Nguyen, Cong-Duy T.
%A Luu, Anh Tuan
%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 le-etal-2026-dont
%X Linear attention reduces the quadratic cost of softmax attention by maintaining a recurrent fast-weight state, but it consistently lags on in-context retrieval and long-context tasks. Existing remedies act on the write side of memory through gating, delta updates, or kernel feature maps, but the read step is left unchanged: every past key contributes additively to the output, so useful targets are diluted by the bulk of stored vectors. We borrow one specific piece of softmax’s geometry to construct a cheap read-time contraction of the query. A second-order Taylor expansion of the softmax log-partition at the isotropic-attention point gives a local quadratic model whose curvature coincides with the running key covariance, a quantity that can be maintained with the same recurrent/chunkwise mechanism as the linear-attention state. The associated linear operator contracts the query along the high-variance directions of memory before it reads the state. We call this mechanism Curvature-Conditioned Query (CCQ). CCQ modifies only the read step and is composable with any linear-attention backbone. Attached to GLA and Gated DeltaNet, it improves perplexity, zero-shot downstream accuracy, S-NIAH retrieval at and beyond the training context, length-extrapolation perplexity from 4K to 20K, and LongBench accuracy.
%U https://aclanthology.org/2026.emnlp-main.1153/
%P 26439-26458

Markdown (Informal)

[Don’t Read Everything: A Curvature-Conditioned Query for Linear Attention](https://aclanthology.org/2026.emnlp-main.1153/) (Le et al., EMNLP 2026)

• Don’t Read Everything: A Curvature-Conditioned Query for Linear Attention (Le et al., EMNLP 2026)

ACL

• Dong Le, Thong Thanh Nguyen, Cong-Duy T Nguyen, and Anh Tuan Luu. 2026. Don’t Read Everything: A Curvature-Conditioned Query for Linear Attention. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 26439–26458, Budapest, Hungary. Association for Computational Linguistics.