Neural Networks Are Already Clean
Summary
Nick Cammarata argues that modern neural networks are largely understandable through direct study of their mechanisms and data flows, challenging the idea that polysemanticity is pervasive or intractable. He introduces recursive generalization as a dominant pattern and suggests interpretability can be advanced with exploratory, mechanism-focused approaches, tools like residual streams and attribution graphs, and a shift away from monosemanticity-centered viewpoints. The piece outlines practical implications for AI safety, model auditing, and future research directions in interpretability.