Replica Fragmentation and Glassy Dynamics in Parity Learning

We study how independently trained Transformer neural networks reconstruct a binary string from its local domain walls. Runs sharing the data and training protocol can realize different functions. We treat them as replicas and measure truth alignment $m$, prediction confidence $q_{\mathrm{self}}$, a