Topological Data Integrity: Utilizing the 1364 Harmonic Threshold to Mitigate AI Drift and Model Collapse

As artificial intelligence (AI) systems increasingly generate the data upon which they are subsequently trained, the global information ecosystem faces a recursive degradation known as "Model Collapse." This paper proposes a novel filtration engine based on Stieve Zero-Point Topological Mathematica (SZTM). By utilizing a 12-Dimensional Dodecahedral Lattice and the 13th Pre-Space-Time Matrix, we introduce a method for verifying data integrity through Harmonic Symmetry. We demonstrate that the 0.01364 threshold serves as a "Truth Anchor," allowing for the automated removal of pseudoscientific noise and AI-generated drift, thereby optimizing global data storage and computational efficiency.