In-Context Learning as Query-Conditioned Hypothesis Selection

In-Context Learning (ICL) is the ability of Large Language Models (LLMs) to alter their input–output mapping by conditioning on demonstrations in the prompt, without parameter updates. Beyond operational differences, ICL is different from Empirical Risk Minimization in the generalization behavior, calling for distinct explanatory frameworks. Theoretical accounts explain ICL as different forms of adaptation to the demonstrations, ranging from Bayesian inference over hypotheses given the demonstrations, to implicit execution of a learning algorithm that fits a predictor to them. We propose modeling ICL as case-based, transductive adaptation in which LLM predictions reflect query-conditioned switching among predictive modes, rather than executing a fixed implicit learner. We formulate this theoretically and test in controlled regression tasks by analyzing predicted distributions for branch structure (multiple modes), discovering query-dependent predictive mass shifts and the causal verification of modes through activation steering. This perspective reconciles disparate theories of ICL by interpreting different implicit learner interpretations as accounts of behavior at query-demonstration regimes.