We present a neuro-symbolic framework for generative language modeling based on local, event-driven emergent learning in hierarchical Hopfield memory chains. These act as both a compositional short-term memory and a dynamic meta-tokenizer (“retokenizer”), building multi-scale tokens from scratch without predefined vocabularies or global objectives. Structure emerges through Hebbian updates that imprint local correlations, hierarchically composed into projection tensors with intrinsic gauge redundancy. Trained even on noise, the model produces synthetic languages whose morphological statistics match human language. Brief exposure of new neurons to the retokenizer binds multi-scale features into symbolic embeddings, forming emergent long-term key–value memories. This framework offers a scalable, interpretable route to neuromorphic, generative language systems where tokens, grammar, and reasoning arise as compressed memory traces in a fully unsupervised Hopfield hierarchy.