Dynamically Scaled Activation Steering (DSAS) adaptively modulates steering in generative models
DSAS is a method-agnostic framework that decouples when to steer from how to steer by computing context-dependent scaling factors to modulate the strength of existing activation-steering transformations across layers and inputs. The authors report that DSAS improves the trade-off between toxicity mitigation and utility preservation, adds minimal compute overhead, improves interpretability by highlighting tokens that require steering, and can be jointly optimized with steering functions; the paper was accepted to the UniReps workshop at NeurIPS 2025 and the authors say code will be made available on GitHub.