Dynamically Scaled Activation Steering

Researchers Alex Ferrando de las Morenas, Xavier Suau Cuadros, Jordi Gonzàlez Sabaté, and Pau Rodríguez Lopez introduced Dynamically Scaled Activation Steering (DSAS), a method-agnostic framework that decouples when to steer from how to steer in generative models, according to a paper published in September 2026. DSAS adaptively modulates the strength of existing steering transformations across layers and inputs, intervening strongly only when undesired behavior is detected, and consistently improves the Pareto front for the trade-off between toxicity mitigation and utility preservation when combined with existing steering methods. The authors also applied DSAS to a text-to-image diffusion model to modulate specific concepts, reporting minimal computational overhead and improved interpretability, with code to be released on GitHub.

content type paper https://machinelearning.apple.com/research/ published September 2026 Dynamically Scaled Activation Steering AuthorsAlex Ferrando de las Morenas†, Xavier Suau Cuadros, Jordi Gonzàlez Sabaté†, Pau Rodríguez Lopez Activation steering has emerged as a powerful method for guiding the behavior of generative models towards desired outcomes such as toxicity mitigation. However, most existing methods apply interventions uniformly across all inputs, degrading model performance when steering is unnecessary. We introduce Dynamically Scaled Activation Steering DSAS , a method-agnostic steering framework that decouples when to steer from how to steer. DSAS adaptively modulates the strength of existing steering transformations across layers and inputs, intervening strongly only when undesired behavior is detected. At generation time, DSAS computes context-dependent scaling factors that selectively adjust the strength of any steering method. We also show how DSAS can be jointly optimized end-to-end together with the steering function. When combined with existing steering methods, DSAS consistently improves the Pareto front with respect to steering alone, achieving a better trade-off between toxicity mitigation and utility preservation. We further demonstrate DSAS’s generality by applying it to a text-to-image diffusion model, showing how adaptive steering allows the modulation of specific concepts. Finally, DSAS introduces minimal computational overhead while improving interpretability, pinpointing which tokens require steering and by how much. The code will be available in Github. ExpertLens: Activation Steering Features Are Highly Interpretable November 7, 2025 research area Methods and Algorithms https://machinelearning.apple.com/research/?domain=Methods%20and%20Algorithms , research area Speech and Natural Language Processing https://machinelearning.apple.com/research/?domain=Speech%20and%20Natural%20Language%20Processing Workshop at NeurIPS https://machinelearning.apple.com/research/?event=NeurIPS%20Workshop This paper was accepted at the Workshop on Unifying Representations in Neural Models UniReps at NeurIPS 2025. Activation steering methods in large language models LLMs have emerged as an effective way to perform targeted updates to enhance generated language without requiring large amounts of adaptation data. We ask whether the features discovered by activation steering methods are interpretable. We identify neurons responsible for specific… STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants November 8, 2023 research area Speech and Natural Language Processing https://machinelearning.apple.com/research/?domain=Speech%20and%20Natural%20Language%20Processing conference EMNLP https://machinelearning.apple.com/research/?event=EMNLP Equal Contributors In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn. We propose STEER, a steering detection model that predicts whether a follow-up turn is a user’s attempt to steer the previous command. Constructing a training dataset for steering use cases poses challenges due to the cold-start problem. To overcome this, we…