In the original KAN paper, they do two things to address this: first they have some sparsity-inducing regularization, and second they have a symbolification ste

In the original KAN paper, they do two things to address this: first they have some sparsity-inducing regularization, and second they have a symbolification step so that you can ideally find a compact symbolic model after learning a sparse computation graph of splines. I guess in principle you could do something similar with MLPs but since MLP representations are sort of delocalized they might be harder to sparsify and symbolify