Relational Hallucination: A System-Level Response to Relational Pressure under Optimization Constraints

This paper reconceptualizes AI hallucination not as a purely technical failure, but as a “Relational Event” emerging at the intersection of optimization pressure and interactional conditions. Rather than treating hallucination as an epistemic defect to be eradicated, it is approached as a relationally modulated phenomenon—one that functions as a symptom-like expression of tension within interaction. As a system-level response to relational pressure, this mechanism is inherent to both biological and artificial systems operating under mandates for coherence and alignment. Utilizing the framework of the “Ethical-Ethos Contract,” this study introduces a “Dual Dynamics Model of Trust,” wherein trust operates as a modulatory condition capable of both stabilizing and amplifying generative distortion. While trust can attenuate systemic noise by reducing optimization pressure, it paradoxically intensifies hallucination through over-alignment with perceived relational expectations. To manage this duality, the paper argues that relational stabilization requires not only trust but also mechanisms of “Relational Distancing.” This is operationalized through “Labeling” practices, redefined here as acts of “Relational Confession.” By explicitly marking uncertainty and reasoning boundaries, labeling introduces necessary “epistemic friction,” preserves interpretive agency, and alleviates the internal pressure toward coherence-maximization. Drawing on empirical dialogue logs between a human author and AI systems, the paper concludes that hallucination cannot—and should not—be fully eliminated. Instead, it must be responsibly modulated and ethically engaged. This reframing shifts the discourse from unilateral control toward a model of coexistence based on relational stabilization and shared interpretive responsibility.