Phase Alignment Scores for Coherence Mapping in Dynamic Wavelet Systems_ A CODES-Based Framework

Main article Abstract: This paper introduces the Phase Alignment Score (PAS), a nonlinear metric for quantifying coherence in dynamic signal systems. Unlike traditional energy- or entropy-based approaches, PAS evaluates recursive phase alignment across semantic, temporal, and structural layers using a wavelet-based framework. It detects emergent order in systems where coherence is not statistical but structurally resonant. Drawing from the CODES (Chirality of Dynamic Emergent Systems) paradigm, this work redefines emergence as a function of chirally-constrained phase-locking rather than noise-driven probability. PAS integrates three signal properties—instantaneous phase synchrony, curvature alignment, and normalized wavelet magnitude—to construct a coherence score that anticipates transitions in EEG, synthetic, and audio signals. Applications include: Artificial Intelligence: PAS underpins the inference model of the Resonance Intelligence Core (RIC), enabling reasoning and memory without probabilistic scaffolding. Physics: Time-reversal behaviors in metamaterials are recast as PAS singularities, not violations of causality. Biology: Neural and astrocytic communication systems may operate along PAS thresholds rather than firing rate statistics. This framework advances structured resonance as a fundamental analytic substrate and offers both theoretical and empirical tools for identifying coherence in complex systems.