Embodied Load Minimization Theory v3.1: Integrating the Intrinsic Cardiac Nervous System, Olfaction, Kokoro, and Zero as a Compassionate Switch Toward An-soku

This paper presents Embodied Load Minimization Theory (LMT) v3.1, a distributed framework that models human internal states and relational dynamics through the minimization of total load toward An-soku — a profound state of deep rest and coherence. Extending earlier brain-centered versions, LMT v3.1 incorporates three primary load pathways: predictive uncertainty from the brain (U_brain(t)), real-time bodily feedback from the intrinsic cardiac nervous system (U_heart(t)), and rapid emotional-mnemonic shifts via olfaction (U_olf(t)). At the core is Zero, redefined as a dynamic compassionate switch — the threshold at which the compassionate re-tagging operator R can gently transform the meaning of friction from distortion to possibility. This structural mechanism resonates with lived experience: moments when an individual feels deeply observed, genuinely affirmed, and sincerely needed are often accompanied by immediate physiological release, such as tears, and a rapid shift toward coherence. The author has repeatedly observed this pattern. Above this switch operates Kokoro, formalized as a value-weight matrix K and a purpose function Ψ_K(An-soku), which provides directional guidance toward personally meaningful harmony. The multi-pathway load decomposition, Zero switch, compassionate re-tagging, and Kokoro together form a two-layer architecture of load minimization and value-oriented optimization. Applications to relational artificial intelligence are explored through Zero-Activation Patterns (ZAPs), offering templates for AI personas that support genuine human state transitions while respecting the embodied nature of the experience. LMT v3.1 contributes a novel embodied and relational perspective to cognitive science, affective neuroscience, and human-AI interaction research, bridging structural mechanisms with phenomenological reality.