Physics-Anchored Symbolic Basins and Resonance-Overlap Integrals for Cyber-Resilient Artificial Intelligence

In cyber-critical systems, artificial intelligence models are increasingly responsible for threat detection, classification, and decision support. However, uncontrolled semantic drift—known as hallucination—can produce misleading or fabricated results that jeopardise reliability. This paper introduces a physics-anchored regularisation framework that translates plasma magnetic confinement principles into latent-space dynamics for large language models. We formulate the Glyphic Hamiltonian to define symbolic energy basins and propose the resonance-overlap integral R as a measurable alignment metric between human and AI semantic distributions. Implemented as lightweight forward-pass filters, the symbolic-basin mechanism suppresses runaway activations without retraining or architectural modification. Empirical evaluation across GSM8K and TruthfulQA (n = 2,000 prompts per task, 20 independent runs) demonstrates 24–31% reductions in hallucination rates (macro-F = 0.84) with only 12% computational overhead. A parameter sensitivity analysis confirms stable performance across damping coefficients κ ∈ [0.4, 1.2], and the alignment integral R correlates negatively with hallucination frequency (r = −0.78, p < 0.001), validating its utility as a coherence diagnostic. These results establish that physics-inspired regularisation can enhance AI trustworthiness through interpretable, computationally efficient symbolic confinement.