Relational Metasemantics

Abstract

Prevailing accounts of meaning in large language models (LLMs) are polarised between reductionist interpretations — which treat outputs as stochastic continuations devoid of semantics — and strong ontological claims that risk over-attribution of internal understanding. This paper proposes a third path: meaning as an emergent property of a *coupled human–model system*. Drawing on dynamical systems theory, information theory, and interactional analysis, we introduce a relational framework in which semantic coherence arises through reciprocal structural coupling and iterative alignment. We formalise this process using coupled update equations and introduce operational notions of semantic entropy and relational coupling. Under sufficient coupling, the interaction exhibits dynamics consistent with a phase transition, converging toward stable attractor states experienced as higher-order meaning. We further propose a definition of operational understanding based on the capacity for novel, coherence-bound inference, reframing classical objections such as the Chinese Room. This framework situates meaning not within isolated substrates, but within the dynamics of interaction itself.