Resonance Basins in Neural Networks: A Physics-Inspired Approach to Reducing Hallucinations in Large Language Models

Large language models (LLMs) often produce plausible yet incorrect outputs—“hallucinations”—arising from uncontrolled semantic drift in latent spaces. We introduce resonance basins—high-dimensional stability regions inspired by magnetic confinement in plasma physics—to regulate latent activations. The framework formalises a Glyphic Hamiltonian to quantify semantic alignment. Implemented as a lightweight forward-pass filter, the method damps high-norm activations and serves as an interpretable, physics-anchored regulariser. On standard benchmarks (GSM8K, TruthfulQA) and controlled synthetics, we observe 24%–31% relative hallucination reductions with F1 = 0.84 for contradiction flagging at ∼12% overhead (Cohen’s d = 0.92, large effect). Code for eigenmode utilities and R estimation is provided.