Researchers built an automated pipeline to hunt down the neuron patterns behind bad LLM behavior—sycophancy, hallucinations, malice, the usual suspects. Then they trained models to watch for those patterns in real time.

Anthropic didn’t just steer models after training like most. They baked the corrections into the training loop itself. That move made their models tougher—less prone to picking up nasty habits, even from noisy or biased data.

System shift: Editing behavior during training with neuron-level signals could beat the pants off post-hoc steering—and save a lot of compute in the process.