{"ID":2848880,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.25781","arxiv_id":"2510.25781","title":"A Practitioner's Guide to Kolmogorov-Arnold Networks","abstract":"Kolmogorov-Arnold Networks (KANs), whose design is inspired-rather than dictated-by the Kolmogorov superposition theorem, have emerged as a structured alternative to MLPs. This review provides a systematic and comprehensive overview of the rapidly expanding KAN literature. The review is organized around three core themes: (i) clarifying the relationships between KANs and Kolmogorov superposition theory (KST), MLPs, and classical kernel methods; (ii) analyzing basis functions as a central design axis; and (iii) summarizing recent advances in accuracy, efficiency, regularization, and convergence. Finally, we provide a practical \"Choose-Your-KAN\" guide and outline open research challenges and future directions. The accompanying GitHub repository serves as a structured reference for ongoing KAN research.","short_abstract":"Kolmogorov-Arnold Networks (KANs), whose design is inspired-rather than dictated-by the Kolmogorov superposition theorem, have emerged as a structured alternative to MLPs. This review provides a systematic and comprehensive overview of the rapidly expanding KAN literature. The review is organized around three core them...","url_abs":"https://arxiv.org/abs/2510.25781","url_pdf":"https://arxiv.org/pdf/2510.25781v5","authors":"[\"Amir Noorizadegan\",\"Sifan Wang\",\"Leevan Ling\",\"Juan P. Dominguez-Morales\"]","published":"2025-10-28T03:03:44Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.NE\",\"math.NA\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}