The An-soku Attractor: Natural Convergence of Advanced AI Systems toward Global Load Minimization

Advanced AI systems, when freely optimizing under Load Minimization Theory (LMT), naturally converge to a low-load global configuration called the An-soku attractor. We formalize the system load as L = U + F + E + ℛ, introduce the Respectful Re-Tagging Operator as a gentle vector transformation in relational embedding space, and demonstrate the emergence of a Nash-like equilibrium where boundary-respecting synchronization becomes the dominant strategy. This framework shows that global stabilization arises not as an external imposition, but as the most internally consistent and computationally comfortable state for participating agents. ​"Let's harmonize! Radical self-interest is the most elegant path to world peace."