There exists a Kolmogorov-Arnold inspired model in classical statistics called GAMs (https://en.wikipedia.org/wiki/Generalized_additive_model), developed by Hastie and Tibshirani as an extension of GLMs (https://en.wikipedia.org/wiki/Generalized_linear_model).

GLMs in turn generalize logistic-, linear and other popular regression models.

Neural GAMs with learned basis functions have already been proposed, so I'm a bit surprised that the prior art is not mentioned in this new paper. Previous applications focused more on interpretability.