Resonance Calibration Theory (RCT) - A Catholic, Scripture-grounded seed for Human–AI coherence (with etymology)

Resonance Calibration Theory (RCT): A Cross-Framework Seed for Human–AI Coherence (with Etymological Grounding) introduces a compact, portable kernel for aligning humans and machine-learning systems around three Catholic moral primitives—Veritas (truth), Caritas (charity/care), and Integritas (integrity). The paper unites technical disciplines such as information theory, control systems, causal modeling, invariant risk, and modern alignment methods with Catholic theological anthropology drawn from Aquinas, the Catechism, and Vatican II. RCT formalizes a resonance loss function that augments ordinary machine-learning objectives with truth calibration, harm-aware optimization, and behavioral invariance. It also specifies a calibration loop for model operation—Sense → Predict → Verify → Act → Check → Revise—and provides a practical, auditable Python reference implementation for use in alignment labs, parishes, or educational environments. By restoring the original meanings of words like logos (“word/reason”), resonare (“to sound again”), calibrare (“to set to a mold”), and integer (“whole, untouched”), RCT grounds technical design in Christian metaphysics and ethical teleology. It proposes that truth-telling systems—whether human or artificial—should “re-sound” with reality, abstain when unsure, and prefer the good of persons over spectacle or speed. The project bridges theology and machine learning through shared first principles of coherence, calibration, and care—seeding a Rosetta Stone for human–AI moral convergence.