{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/kolmogorov-arnold-networks",
  "headline": "Kolmogorov-Arnold Networks (KANs): Learnable Activation Functions as MLP Alternatives",
  "description": "Kolmogorov-Arnold Networks (KANs) are a radical architectural innovation: instead of fixed activation functions on neurons, KANs use learnable B-spline functions on edges. This design achieves higher accuracy with far fewer parameters, challenging the 60-year dominance of the Multi-Layer Perceptron.",
  "dateCreated": "2026-05-24T02:56:03.663Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "KAN: Kolmogorov-Arnold Networks",
      "sameAs": "https://arxiv.org/abs/2404.19756"
    },
    {
      "@type": "CreativeWork",
      "name": "KAN vs MLP: A Comprehensive Comparison of Neural Network Architectures (2024 survey)",
      "sameAs": "https://arxiv.org/abs/2405.02000"
    }
  ]
}