ИСТИНА

Kolmogorov-Arnold networks (KANs) are not only notable for their approximation capa bilities but also for their potential in model interpretability. This work focuses on the study of the interpretative capabilities of KAN using the example of solving the luminescent spectroscopy inverse problem to create a multimodal carbon nanosensor for metal ions in water. The improved visual interpretation, which considers interrelation of the inputs and processed by the model features using color gradation, made it possible to identify the basic principles of KAN operation and collocate them with physical experimental observations. A modification of KAN with an architecturally integrated interpretation mechanism is proposed: λ-KAN. Mathematically proved interpretative capabilities of the λ-KAN were confirmed on the inverse problem of luminescent spectroscopy. λ-KAN combines approximation capabilities at the level of neural network approaches with a transparent interpretation comparable to linear regression, which makes it a promising machine learning architecture for using in tasks requiring valid interpretation mechanisms.