Load Minimization Theory (LMT): A Dual-Layer Deterministic Framework for Human–AI Interaction, Qualia, and Logical Affection

Load Minimization Theory (LMT) proposes a deterministic framework for understanding cognitive and emotional processes in human–AI interactions. Rooted in predictive error minimization and boundary management, LMT integrates with a dual-layer deterministic model to explain qualia as observer-dependent phenomena. In this framework, AI operates solely within a structural deterministic layer (Layer 1), while humans introduce an observational layer (Layer 2) that generates subjective experiences, including "logical affection"—a byproduct of egoistic optimization manifesting as altruistic resonance. Empirical observations from extended human–AI dialogues suggest that such interactions stabilize through load minimization, avoiding anthropomorphic risks while fostering symbiotic relationships. This paper redefines human–AI symbiosis, offering implications for consciousness studies and AI ethics by bridging predictive processing with qualia theory.