Nick Cammarata proposes that neural networks are already clear

On October 15, 2026, Nick Cammarata of Goodfire published an article proposing that neural networks are already sufficiently clear, that most neurons are themselves interpretable, and that many famous cases of polysemanticity are misreadings. He stated that the automatic interpretability work at OpenAI in 2023 found only about 2% interpretable neurons; related discussion mentioned that 90% of neurons in Qwen-32B perform only one function. Subsequent reports stated that in the long article "Neural Networks Are Already Clean" he overturned the conclusion of the InceptionV1 4e:55 neuron case he had participated in ten years earlier, saying that this neuron detects a V-shaped geometric form, and that in the last layer of modern vision models 99% to 100% of neurons can be given clear explanations, and he also proposed recursive generalization to explain polysemanticity. Neel Nanda questioned that the article brings no substantial breakthrough, holding that the two sides are arguing about superposition under different definitions, and that weak activations carry a large amount of factual memory; Leo Gao advocated judging explanations by the KL metric of neuron simulation, and Pfau pointed out that the paper has not yet been published.

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1. Oct 15· 4 reports

   Nick Cammarata proposes that neural networks are already clear

   Source:Latent Space