How does the Rubik's Cube Matrix (RCM) work as a Single Agent Structural Intelligence Game?

The Rubik’s Cube Matrix (RCM) is a novel decision-making framework that represents decision variables as a 3D tensor and decision operations as discrete transformations (rotations/permutations) on a cube-like structure. RCM integrates information compression (φ via PCA/Autoencoder), local weighting (W), Bayesian hyperparameter optimization, and efficient discrete search (A* with symmetry reduction) to produce interpretable and measurable decision trajectories. We prove empirical convergence in O(1/√T) and search complexity reduction by a symmetry factor under Lipschitz continuity and admissible heuristics. Initial evaluation on synthetic tensors shows RCM achieves a strong efficiency-interpretability trade-off (optimality deviation <4%, measurable ε_emp).