Don't Read Everything: A Curvature-Conditioned Query for Linear Attention
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...
| Authors | Dong Le, Thong Nguyen, Cong-Duy Nguyen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 31 May 2026 |
| Last updated at source | 30 August 2026 |
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