FuncCode: Compressing Kolmogorov--Arnold Networks in Function Space with Hardware-Aware Quantization
Kolmogorov--Arnold Networks (KANs) replace scalar edge weights with learnable univariate functions, increasing flexibility but also parameter memory because each edge stores multiple coefficients, often together with a separate base branch. We introduce FuncCode, a basis-agnostic compression approach that forms shared codebooks from sampled edge responses, codes the basis and base branches independently, and exports the resulting codebooks and per-edge indices in a quantized, bit-packed format. Across spline and polynomial KANs, sampled edge responses exhibit $13$--$35\%$ lower effective rank than their coefficient representations. Further replicated controls show that function-space clus...
| Authors | Kazi Ahmed Asif Fuad, Lizhong Chen |
|---|---|
| Primary category | cs.LG |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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