Neural networks have become a fundamental tool for solving complex problems, from image processing and speech recognition to time series prediction and large-scale data classification. However, traditional neural architectures suffer from interpretability problems due to their opaque representations and lack of explicit interaction between linear and nonlinear transformations. To address these limitations, Kolmogorov–Arnold Networks (KAN) have emerged as a mathematically grounded approach capable of efficiently representing complex nonlinear functions. Based on the principles established by Kolmogorov and Arnold, KAN offer an alternative to traditional architectures, mitigating issues such as overfitting and lack of interpretability. Despite their solid theoretical basis, practical implementations of KAN face challenges, such as optimal function selection and computational efficiency. ...