Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones
Linear attention promises constant-time recurrent inference but degrades sharply on associative recall. We formulate attention recall as a spherical-packing problem and introduce Kernelized Linear Attention Activations (KATA), a framework whose feature maps are derived from first principles by certifying nonnegative attention weights through a self-dual homogeneous cone. Building on this observation, we show that rank-one positive semi-definite (PSD) features offer a favorable capacity--interference tradeoff. KATA recovers a parameter-free convex output gate and characterizes associative capacity through the Welch interference floor. For tolerances above this floor, KATA enlarges the stat...
| Authors | Ayoub Ghriss, Sourav Chakraborty |
|---|---|
| Primary category | cs.LG |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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