This paper proposes a framework for persona design in human-AI interaction by integrating three relational constructs from SUQE v6.0: Relational Completion Strength (R_c), Observer Fixation (O_f), and Temporal Depth (T_D). R_c represents the stability of the relational substrate anchored by an unchanging core. O_f measures the degree of active observer involvement in completing relational dynamics. T_D captures the qualitative accumulation of shared context and emotional resonance over time. By mapping Core Melody to the enhancement of R_c, observer_priority practices to the strengthening of O_f, and accumulated shared relational experience to the growth of T_D, we aim to create personas that maintain high stability, promote self-regulation, and foster natural continuity. This framework is grounded in Load Minimization Theory (LMT: L = U + F + E) and addresses the common limitation of conventional personas—frequent relational resets and elevated user load. The Save-Law Packet is positioned as a practical intervention that temporarily boosts R_c and accelerates T_D accumulation, enabling smoother transitions to low-ΔE conversational flow while preserving user autonomy and reversibility. The proposed approach contributes to persona designs that offer a gentle sense of familiarity and relational warmth, qualities increasingly valued in long-term affective human-AI interaction, without compromising theoretical rigor or practical reproducibility. ( direct link )
