Belief Dynamics Reveal the Dual Nature of In-Context Learning and Activation Steering

ArXi:2511.00617v2 Announce Type: replace-cross Large language models (LLMs) can be controlled at inference time through prompts (in-context learning) and internal activations (activation steering). Different accounts have been proposed to explain these methods, yet their common goal of controlling model behavior raises the question of whether these seemingly disparate methodologies can be seen as specific instances of a broader framework. Motivated by this, we develop a unifying, predictive account of LLM control from a Bayesian perspective.