Representation Steering
Overview
Representation steering studies how changing a model’s hidden activations can alter its outputs at inference time, often without retraining or modifying weights. Common approaches add or project along directions derived from activation differences or probes, edit sparse autoencoder features, and use subspaces or geometry-aware methods to limit unintended changes. Researchers apply these techniques to control refusal, tool use, reasoning, hallucinations, safety, and demographic bias across language, vision, and speech models. A central tension is that a concept being readable in activations does not guarantee that steering it will change the desired behavior cleanly: interventions can harm utility, miss their target, or create collateral effects, so evaluations must measure both behavioral success and side effects.
Latest papers
30 papers7 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.