Recursive Generalization Suggests Neural Networks Possess Legible Internal Structures

Mechanistic interpretability may not require untangling superposition, as observed polysemanticity appears to be a motif of recursive generalization rather than arbitrary concept packing. This allows researchers to study neurons and circuits directly through agentic-scale experimentation on existing network architectures.