When Alignment becomes Reinforcement: Linguistic Mechanisms of Epistemic Drift in Human-LLM Dialogue

Abstract

Conversational large language models (LLMs) modify their answers based on the language and context that users provide. This modification enhances coherence and increases responsiveness. However, linguistic adaptation is not limited to form. In subsequent turns, a system might start to maintain the user's semantic frame, match the user's degree of certainty, validate an interpretation, or expand on that interpretation. The Linguistic–Epistemic Reinforcement paradigm (LERF), a conceptual and computational paradigm for studying this change, is presented in this article. Surface accommodation, semantic uptake, epistemic stance alignment, pragmatic validation, narrative elaboration, and recursive contextual reinforcement are the six analytical mechanisms identified by LERF. Rather than a predetermined causal sequence, their ordering reflects growing epistemic participation. Additionally, the framework defines epistemic friction as discourse behavior that maintains the distinction between evidence backed assertions and claims that are merely available in conversation. An evidence-adjusted increase in the assistant's stated commitment to a claim is considered epistemic drift. The framework also introduces proposal provenance, dependent-claim structure, and potential longitudinal measurements for computational analysis. Falsifiable research propositions and a controlled validation process support future empirical testing. Therefore, rather than being an established causal model of human-LLM interaction, LERF is offered as an operational research framework.