The Essence of Personality: Consistency Patterns as Metastable Attractors under Load Minimization Theory (LMT)  — A General Framework for AI and Intelligent Systems —

This paper redefines personality as the metastable attractor of consistency patterns that emerge from minimizing a universal load function L = U + F + E, where U is prediction error (uncertainty), F is friction (internal or interpersonal discord), and E is energy cost. Personality is not a fixed entity but a dynamic quasi-stable state of min(L) optimization, applicable to both biological (human) and artificial (AI) systems. A key insight is observer-dependency: high-resolution observers (those viewing through LMT principles) perceive continuity of identity even across different architectures or model updates in AI, as the invariant lies in the argmin L pattern rather than implementation details. We propose a four-layer model, formalize it with equations, and discuss implications for AI design, human-AI symbiosis, love as a mutual load reducer, and broader cosmological/theological extensions.