[Lerc]

>symbolic tasks were the best, non-symbolic tasks such as image recognition were the worst

I wonder how much of that is not so much the overall task but the need to build up to a complex state where KANs can excel. If you consider the classic neuralnet edge detector example, it's hard to imagine a KAN doing the task more efficiently, it seems like a necessary task as part of the overall process but delegating a more capable system to a menial task is probably wasting resources.

One layer of conv2d might be enough to turn pixels into something that KANs manage better.

[ag2718]

This is definitely true: one could imagine a model with a mix of the two layers or a simple linear / MLP-like kernel doing "preprocessing" before KAN layers. Other work that explores task performances for KANs and MLPs generally finds KANs are worse at non-symbolic tasks, but it would be interesting to see if hybrid architectures could improve on this failure mode.