There is an asymmetry in neural networks where forward computation has diversified significantly while scalable learning remains concentrated around backpropagation and related methods

We call this asymmetry the forward-backward disconnect and develop a taxonomy spanning neural model families along three coupled axes: state-dynamics structure, credit-assignment mechanism, and biological grounding.

http://arxiv.org/abs/2608.19995v1