Goodfire

In Neural Networks are Already Clean, @nickcammarata argued that neurons were far more legible than previously thought – and that this was great news for interpretability and safety. Now, we’re releasing the first technical companion to Nick’s essay. It shares our findings from studying thousands of neurons using our novel automated interpretability pipeline, and outlines why we now think of neurons by their computational function, instead of whether or not they are polysemantic. Here’s what we found: 🧵

Can interpretability shape training? @TIME covers our latest work on mechanistic shaping and why our team is optimistic. Read the story online or in TIME's upcoming print edition. time.com/article/2026/11/05/ai-cheating-s…

We cut through the mystery around interpretability in training. A single, mathematically rigorous framework transforms model internals into training feedback and works across domains, problems, and modalities. We call this *mechanistic shaping*, and we’ve used it to mitigate reward hacking, remove shortcuts in chest x-rays, and reduce sycophancy and harmfulness. We believe this is a big step forward for technical alignment. But it carries a known risk: training against a model’s internals can make it harder to monitor. We explore both the technique and this concern below. 🧵