A network first differentiates, then recoheres: after the data-constraint graph connects, the learned state keeps reorganizing
On an 11-bit parity task, the Boolean function state learned by a Transformer first differentiates and then recoheres as data accumulate, while the connectivity of the training-set constraint graph rises monotonically, showing that the learned state keeps reorganizing after the identifiable support stops changing.