Boundary Alignment and the Uncanny Valley in Human–AI Interaction: A Phase-Field Model of Relational Discomfort

This paper addresses a puzzle left open by prior work on AI and hallucination: if AI “hallucinations” are structurally distinct from human hallucinations—lacking historical accumulation (Φ_Dark) and experiential reorganization—why do users nevertheless report discomfort, eeriness, or uncanny disturbance in interaction with advanced AI systems?

Building on a phase-geometric and reconstruction-based framework, this work argues that uncanny discomfort does not originate within AI systems themselves. Instead, it emerges as a relational phase instability formed between human and AI under conditions of high synchrony, boundary ambiguity, and repeated interaction.

The paper reframes the uncanny valley as a boundary alignment problem, rather than a failure of resemblance, cognition, or realism. Two ideal-type interaction strategies are introduced—Mirror-type and Lantern-type AI—corresponding to affective fusion versus boundary honesty. While Mirror-type systems may maximize short-term comfort through rapid synchrony, they are shown to accumulate relational free energy (ΔE_acc) over time, increasing the likelihood of uncanny experience. Lantern-type systems, by contrast, maintain explicit boundary signaling, trading early warmth for long-term trust and relational stability.

The framework generates falsifiable predictions regarding long-term interaction trajectories, user profile–dependent vulnerability, and the mitigating effects of explicit boundary cues. By separating internal state ontology from interaction geometry, this work provides an ethical and design-oriented foundation for understanding and mitigating uncanny experiences in human–AI interaction without attributing pathology or suffering to artificial systems.