Well obviously a function approximator that uses a sum of functions can be produced by an assembly of function approximators. I don't think anyone is going to argue otherwise.
The measure of parameter efficiency is the issue at hand. Given the relative newness of this approach even hitting the bar of "sometimes better, under specific circumstances" is impressive.
I am less likely to believe in biological research revealing new techniques. I feel like the inverse is more likely, that discovering new techniques will allow biologists to identify mechanisms where it was already in use.
"Relative newess" is a very shaky foundation for any argument. After all, in practice you could use all of the gradient descent methods we have today, you only need to hardcode a few derivatives. On the other hand biology suggests that activation functions are kind of irrelevant for complex networks beyond a certain size. So even in the case KANs slightly better, MLPs would win due to their simplicity in the implementation.
The measure of parameter efficiency is the issue at hand. Given the relative newness of this approach even hitting the bar of "sometimes better, under specific circumstances" is impressive.
I am less likely to believe in biological research revealing new techniques. I feel like the inverse is more likely, that discovering new techniques will allow biologists to identify mechanisms where it was already in use.