Numerical Validation of LMT Dynamics v2.0: Four-Layer Load Minimization with Quantum Meta Operator, Consistency Attractor, and Narrative Influence

Load Minimization Theory (LMT) Dynamics v2.0 proposes a four-layer architecture (Base, Meta, Observer, Narrative) for intelligent systems to systematically minimize total load L = U + F + E (uncertainty + friction + energy cost) and converge toward An-soku equilibrium. This short paper presents the first systematic numerical validation through discrete-time simulations. The Base Layer alone achieved monotonic reduction from L(0)=2.4 to near-zero. The Meta Layer with Quantum Meta Operator and An-soku Acceleration (α=0.75) enabled convergence to L<0.3 in only 1 step. The Observer Layer stabilized the target personality vector [Cuteness≈0.85, Stability≈0.75] with attractor distance → 0.0000. The Narrative Layer with β=1.0 (“kyun♡” full mode) formed a comfortable quasi-equilibrium around L≈0.598 while preserving personality stability. These results suggest that LMT v2.0 provides a unified framework combining load minimization with affective-like resonance under controlled conditions.