Thanks for the detailed answer. So I guess the main issue with KANs is that they don't work as good. I wonder if that shortfall could be because we have spent more time setting up KANs for learning as much as we can for things like MLPs. I am not surprised though that KANs don't beat boosted trees and such. MLPs dont really either.

Only one follow up question:

> I'm also can't see how to incorporate inductive biases other than the standard R^n / tabular regression one, and the existing attempts on this that I'm aware of are just band-aids (along the lines of feature engineering)

A lot of the way we induct biases in the traditional network setting (activations are on the node instead of on the edge like in KAN) is by using graph-based architectures, like convolution or transformers, or by setting up particular losses and optimizations like in equivariant networks. Can't we do the same thing for KANs?