The paper reframes associative recall in linear attention as a spherical packing problem. This geometric formulation offers a way to study associative retrieval capacity in this type of mechanism.
Building on that approach, the work introduces Kernelized Linear Attention Activations (KATA). The method uses feature maps derived from a self-dual homogeneous cone; the available summary gives no quantitative results and does not establish that the approach outperforms alternatives.
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