Zvi reviews Goodfire's new work on neuron interpretability: most neurons correspond to only one concept, but alignment is far from solved

LessWrong author Zvi lays out the polarized reactions to Goodfire researcher Nick Cammarata's article "Neural networks are already clean," arguing that it is an important update on neuron interpretability, but still far from solving interpretability and alignment. Cammarata says the cause of polysemanticity in early models is not superposition, but several ordinary and mostly fixable problems, plus what he calls "recursive generalization"; a preliminary audit of Qwen3-32B estimates that about 90% of neurons have only one primary function, about 7% have two related functions, and 2% to 4% are not yet understood, with similar results on trillion-parameter models such as Kimi K3. OpenAI researchers including Dean W. Ball and roon say its significance is comparable to "Attention Is All You Need," while Neel Nanda, Leo Gao, Joshua Achiam and Eliezer Yudkowsky argue that progress is limited and that the explanations themselves still need more rigorous evaluation.