A Geometric Account of Activation Steering through Angle-Norm Decomposition
Linear activation steering has gained popularity as a simple and empirically effective way to control language model behavior. More recently, spherical steering paradigms have been proposed to address limitations of additive interventions, often motivated by the assumption that hidden-state norm does not carry concept-relevant information. In this work, we revisit this assumption through a controlled empirical study designed to disentangle the roles of angular and radial components. We show that steering methods differ mainly in how they couple two geometric effects: changing a token's angular alignment with a concept direction and changing its hidden-state norm. Across seven language mod...
| Authors | Georgii Aparin, Tatiana Gaintseva |
|---|---|
| Primary category | cs.AI |
| Published | 4 June 2026 |
| Last updated at source | 8 June 2026 |
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