Humor as Semantic Pressure Regulation in Human–AI Interaction

Research on humor in human–AI interaction typically focuses on entertainment, affective response, social bonding, or anthropomorphic interpretation. This paper proposes a different perspective: humor as a structural regulatory phenomenon within long-form asymmetric human–model interaction. Building upon the conceptual framework of asymmetric epistemic dyads, Symbiotic Intelligence, and recursive human–model interaction architectures, the paper argues that humor frequently emerges under conditions of increased semantic density, transition instability, drift pressure, or recursive compression. Within such interaction environments, humor can function as a temporary stabilization mechanism that reduces semantic pressure without dissolving structural tension. The paper introduces the concept of semantic pressure regulation to describe how humorous micro-events may operate as reconvergence mechanisms inside recursive interaction architectures. Humor is therefore not treated as evidence of machine intentionality or emotional understanding, but as an emergent property of probabilistic semantic reconstruction under conditions of sustained epistemic continuity. Several recurring interactional functions are examined, including drift interruption, transition buffering, resonance stabilization, semantic decompression, and tension-preserving reconvergence. The paper further distinguishes structural humor from anthropomorphic interpretations of model behavior. The contribution is exploratory and conceptual. No empirical claims are made. Instead, the paper proposes a preliminary analytical vocabulary for investigating humor as a regulatory phenomenon in long-form human–AI interaction.