Nick Cammarata 称现代智能体工具让大模型绝大多数神经元可读
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研究者 Nick Cammarata 称，借助现代智能体工具和更近距离观察，参数规模可达数万亿的模型中绝大多数神经元可读，电路结构清晰，许多著名“纠缠神经元”案例实为误解。他早年参与 OpenAI 机制可解释性工作，并启动 OpenAI 首个大规模自动化可解释性项目；当时用原始工具仅发现约 2% 神经元可读，因此一度悲观。他现认为，自己预期中的障碍大多不存在，雄心勃勃的可解释性可能很快解决，因为自动化科学家持续改进。
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
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