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kolmogorov-arnold-networks-guide

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Papers and tutorials on KAN learnable activation networks

skills/domains/ai-ml/kolmogorov-arnold-networks-guide/SKILL.md

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# Kolmogorov-Arnold Networks (KAN) Guide

## Overview

Kolmogorov-Arnold Networks (KANs) are a novel neural network architecture that places learnable activation functions on edges (weights) instead of fixed activations on nodes. Based on the Kolmogorov-Arnold representation theorem, KANs use B-spline functions as learnable edge activations, achieving better accuracy and interpretability than MLPs with fewer parameters in certain domains. This collection tracks the rapidly growing KAN literature.

## Core Concept

```
Traditional MLP:
  x → [fixed activation(linear transform)] → y
  Activations on nodes, weights on edges

KAN:
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