R↔L Harmonics: A Φ Integration Metric for Neural Architectures

R↔L Harmonics: A Φ̃ Integration Metric for Neural Architectures Author: Dragan Škondrić (Author of the KDS – Quantum Dual Symmetry Model)Version: v1.0Framework: KDS – Quantum Dual SymmetryLicense: CC BY-NC 4.0 This publication presents both the theoretical formulation and operational simulation of the R↔L Harmonics algorithm, derived from the KDS – Quantum Dual Symmetry Model. The system defines a dual-domain reflective structure: R-domain (latent, potential): compact substrate of pre-phenomenal states L-domain (explicit, temporal): manifest structure of integrated information A Φ̃ (Phi-tilde) Integration Metric is introduced to measure informational harmony via: Coherence (C) – structural correlation Entropy (H) – spectral diversity Redundancy (Red) – penalty term regulating informational overlap The architecture is implemented in an operational simulation (rl_harmonics_demo.tsx), featuring: bidirectional projections (R→L and L→R), temporal kernel bias, adaptive harmony optimization, and cycle-consistency learning. This module represents the first operational subsystem of the KDS framework — a self-reflective computational model that bridges ontological duality, information integration, and conscious representation. Related resources: OSF project: https://osf.io/3nwve Cloud AI demo: → Launch Live Simulator ← KDS – Quantum Dual Symmetry Model (v3.0): https://doi.org/10.5281/zenodo.16980664