Functorial Neural Architectures from Higher Inductive Types

ArXi:2603.16123v1 Announce Type: cross Neural networks systematically fail at compositional generalization -- producing correct outputs for novel combinations of known parts. We show that this failure is architectural: compositional generalization is equivalent to functoriality of the decoder, and this perspective yields both guarantees and impossibility results.