Goodfire Research proposes mechanistic shaping to steer models via internal representations

Goodfire Research proposes mechanistic shaping, a framework that adds penalties on a model's internal concept directions during training so it learns to rely on or express specific concepts rather than just maximize task reward. In chest X-ray experiments, an LLM agent used sparse autoencoder features to find tube-like shortcut directions, and reliance shaping beat both standard training and a balanced-data baseline.