KANLib -- A Modular, Extensible and Fast Kolmogorov-Arnold Network Implementation
Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional multilayer perceptrons by replacing linear weights with learnable univariate functions. Despite their theoretical advantages in interpretability and expressiveness, practical research of KANs remains difficult due to high computational costs and inconsistent feature support across existing frameworks. This paper introduces KANLib, a modular, extensible, and computationally efficient framework for developing and evaluating KAN architectures. KANLib unifies core concepts from existing implementations, including PyKAN, EfficientKAN, and FastKAN, within a consistent software architecture that emp...
| Authors | Julian Hoever, Gregor Schiele |
|---|---|
| Primary category | cs.LG |
| Published | 16 June 2026 |
| Last updated at source | 23 June 2026 |
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