A Survey on Kolmogorov-Arnold Network

This review study explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov–Arnold Networks (KAN), a neural network model inspired by the Kolmogorov–Arnold representation theorem. KANs set themselves apart from traditional neural networks by employing learnable, spline-parameterized functions rather than fixed activation functions, allowing for flexible and interpretable representations of high-dimensional functions. The review explores Kan’s architectural strengths, including adaptive edge-based activation functions that enhance parameter efficiency and scalability across varied applications such as time series forecasting, computational biomedicine, and graph learning.…