Invariant-Driven Structural Learning for Language-Modeling

Exploration of an alternative approach to language modeling where learning is guided by structural
invariants instead of minimizing loss in fixed architectures. We introduce a self-organizing
probabilistic automaton that can dynamically change its internal structure, including states,
responsibilities, and routing, when constraints are violated.