MPHD-IV: Formal Architecture for Symbolic Meta-Audit

Abstract This technical report introduces MPHD-IV, a formal architecture for Symbolic Meta-Audit designed for artificial and institutional decision systems. While traditional governance frameworks remain largely confined to syntactic monitoring and behavioral compliance, MPHD-IV proposes a supervisory meta-layer centered on symbolic coherence, structural alignment, and ontological consistency across decisional flows. The architecture operates through a Triadic Audit Loop composed of Axis Calibration, Drift Detection, and Structural Re-anchoring, enabling preventive governance by identifying decisional displacement before functional collapse or systemic failure occurs. Within AI contexts, the framework reframes phenomena such as hallucinations as expressions of symbolic collapse rather than isolated computational errors. This report establishes the formal and architectural basis for the M.A.S. (Meta-Audit System) framework. Institutional validation was conducted through a pilot application in a complex governance environment, where symbolic realignment restored decisional stability without additional infrastructural modifications.