Rethinking Vision Architectures with Gated Linear Attention and KAN
Vision Transformers devote most of their parameters to MLPs for channel mixing, but still rely on quadratic multi-head self-attention for token interactions. While linear attention fixes the complexity problem, bringing it down to O(N), it is usually just paired with the same fixed-activation MLP as before. Kolmogorov-Arnold Networks take a different approach, placing learnable univariate functions on the edges instead. However, existing vision KANs either retain standard attention or remove attention entirely, so the two ideas have not been effectively combined. We introduce LKAT (Linear Kolmogorov-Arnold Transformer) to close this gap: an isotropic ViT-style encoder that couples chunk-w...
| Authors | Ali Mehizel, Oussama Khaldi |
|---|---|
| Primary category | cs.CV |
| Published | 18 September 2026 |
| Last updated at source | 29 September 2026 |
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