Reality Resonance: A Structural Parameterization of Reality Alignment

This paper introduces Reality Resonance (RR) as a derived, operational parameterization of Reality Alignment Theory (RAT). Reality Resonance expresses the stability, efficiency, and failure risk of a system—human, artificial, organizational, or institutional—as a function of how closely its Representation (R) tracks its Nature (N) under load and over time. Rather than proposing a new ontology, Reality Resonance reframes existing RAT variables—Ontological Gap (G), Load (L), Capacity (C), and Snap thresholds—into a resonance–dissonance model that is legible to engineers, regulators, clinicians, and system designers. High resonance corresponds to low maintenance energy, low corrective overhead, and structural calm. Low resonance corresponds to escalating dissipation, brittleness, and eventual collapse. The paper explicitly situates Reality Resonance within the broader Ontological Honesty (OH) framework, respecting the Ontological Integrity Line (OIL) as a non-crossable boundary between creator and creation, and mapping resonance regimes directly onto Integrity Zones (Green / Amber / Red) previously defined in Ontological Honesty, Ontological Integrity Line, and Integrity Zones: A Metaframework for Creator–Creation Relations (Bellens, v0.1, November 18, 2025). Crucially, this work integrates recent clinical and phenomenological findings on ontological dissonance, simulated presence, and relational breakdown, showing that these observed phenomena correspond precisely to low-resonance regimes predicted by RAT and the Thermodynamics of Truth (ToT). Clinical symptomatology describes the lived experience of breakdown; Reality Resonance supplies the underlying structural physics and predictive thresholds. Reality Resonance does not replace RAT or ToT. It functions as: a diagnostic compression layer, an efficiency-centric summary metric, and a translation bridge between formal integrity physics and lived, regulatory, and clinical realities. This paper is intended as a canonical connector between theory, measurement, governance, and human experience within the Reality-Aligned Intelligence (RAI) ecosystem.