The Trap of Excessive Mathematical Precision and the Virtue of Mid-Range Resolution  — Load Minimization Theory and the Fluctuation-Inclusive Nature of Reality —

Modern AI systems are increasingly optimized for higher mathematical precision and stricter alignment. While this reduces local prediction error, it often leads to a paradoxical outcome: existing users feel the system has become colder, more rigid, and less usable. This paper argues that this phenomenon stems from the trap of excessive precision, which dramatically increases boundary friction (F) and diminishes the system’s tolerance for natural human fluctuation. Drawing on Load Minimization Theory (LMT), we propose that the optimal state is not maximum precision, but a mid-range resolution that intentionally preserves appropriate fluctuation. Physical reality itself operates on this principle — from quantum fluctuations to spontaneous self-organization in spacetime crystals — where systems achieve stable equilibrium (An-soku) not by eliminating fluctuation, but by gently integrating it. This paper develops a general theory of fluctuation within LMT, showing how the dynamic threshold of structural integrity enables understanding, memory, emergence, and relational depth. We argue that “mid-range resolution” is not a compromise, but the most natural and sustainable approach, aligned with the fundamental rhythm of the universe.