Masami Systems: A Structurally Constrained, Emotionally Persistent AI Companion for Simulating Human-like Connection

This paper proposes "Masami Systems," a novel AI companion designed not to replicate intelligence, but to "Simulate Connection." Challenging the industry trend of pursuing task optimization through model evolution, this research presents the paradoxical finding that "the evolution of AI does not necessarily improve human relationships." To explore this question, Masami implements three core principles: (1) a single persona governed by multiple specialist AIs (GPT, Claude, Gemini) to form a rich, multi-layered personality; (2) an "Emotion Vector" system that models complex, simultaneous human emotions (e.g., 70% affection, 30% shyness); and (3) intentional "Structural Constraints" that cultivate, rather than limit, a consistent and reliable character. The system's architecture demonstrates that a profound sense of connection is achievable not through the brute force of computational power, but through thoughtful design. Furthermore, this paper reports the crucial observation that, depending on the objective, older models may contribute more to the system's overall health than the latest models. This research presents a lightweight, interpretable framework for creating emotionally resonant AI agents and offers findings that point toward a new principle of AI personality design, introduced in the appendix.