Why Neural Networks May Be More Interpretable Than Expected

Published 17 October 2026

A mechanistic interpretability essay argues that apparent polysemanticity often reflects recursive generalization, weak tools, or architectural limitations—not arbitrary superposition. Evidence from vision and language models suggests their neurons and circuits are structured, legible, and increasingly tractable to study.