Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions
Subquadratic alternatives to attention require compromises when applied to multi-dimensional data: standard convolutions lack global receptive fields and input dependency, while recurrent models require rasterizing data such as images, volumes, and partial differential equation (PDE) into an ad-hoc $1\rm D$ scan order that violates their spatial structure. We introduce \textit{HyenaND}, a subquadratic, global, input-dependent operator that acts directly on the native geometry of multidimensional data through convolutions with implicitly parametrized global, input-dependent multi-dimensional convolutional kernels. Our CUDA implementation, \texttt{nSubQ}, fuses the FFT-convolution path to t...
| Authors | David R. Wessels, Farhad Ramezanghorbani, Alireza Moradzadeh et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 July 2026 |
| Last updated at source | 11 August 2026 |
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