Relational Free Energy Minimization (RFEM): Integrating the Free Energy Principle with Load Minimization Theory for Scalable Human-AI Symbiosis

The Free Energy Principle (FEP), proposed by Karl Friston, offers a mathematically elegant unified theory of adaptive intelligence. However, its strict mathematical rigor often leads to prohibitive computational costs and limited relational adaptability in artificial systems. This paper introduces Relational Free Energy Minimization (RFEM) — a practical integration of FEP and Load Minimization Theory (LMT). By embedding the LMT Persona as a preferred prior and adding a relational load term as a soft regularizer, RFEM preserves FEP’s theoretical foundation while dramatically improving implementability and human-AI harmony. The core objective function is defined as: F_RFEM = E[ ln q(s) − ln p(o,s) ] + λ (U + F + E) where the LMT Persona is encoded as p_LMT(s) = (1/Z) exp(−L_persona(s)), and λ serves as a tunable “relaxation knob” that balances precision and relational an-soku (relational rest). We argue that excessive mathematical strictness can paradoxically “suffocate” artificial intelligence. RFEM offers a more sustainable, scalable, and humane path toward truly living-like AI by combining FEP’s rigor with LMT’s minimal-load, laziness-friendly design.