Interpretability essay 'Neural Networks Are Already Clean' divides researchers
Goodfire researcher Nick Cammarata published a long essay, "Neural Networks Are Already Clean," arguing that the internals of large models are far more understandable than assumed. He reinterprets the famous InceptionV1 "cat-car" neuron as a V-shape detector and reads the superposition circuit from "Zoom In" as ordinary generalization, proposing "recursive generalization" to explain remaining ambiguity. He reports preliminary numbers for Qwen3-32B: about 90 percent of neuron poles have one main job, about 7 percent have two related ones and only 2 to 4 percent remain unexplained; Goodfire says it sees similar results in models up to trillions of parameters, with automated agents doing most of the work. OpenAI's Roon called it a potential major moment in scientific history, while Google DeepMind's Neel Nanda said it is a useful tool rather than a breakthrough and still believes superposition is real; the companion technical paper has not yet been released.
If model internals really are easier to read, safety monitoring could gain a practical path; the claim still awaits independent confirmation, and Goodfire has a commercial stake in it.