Epistemic Field Theory: Predicting and Governing Hallucination in Large Language Models via Multi-Model Consensus

Abstract Large language models hallucinate at rates that undermine reliability in high-stakes applications. Mitigation strategies based on repeated sampling or majority voting implicitly assume error independence across samples. We introduce Epistemic Field Theory (EFT), which predicts hallucination probability from multi-model consensus. EFT defines a consensus field σ ∈ [0, 1] over query space and derives the predictor P(H) = (1 − σ)·η, where η is a model-specific noise coefficient. Across 13,728 human-validated responses (Cohen’s κ = 0.87) from four frontier models in three professional domains, σ predicts hallucination with AUC = 0.787, outperforming majority voting (0.518), SelfCheck methods (0.358–0.377), and self-reported confidence (0.461). Hallucination counts exhibit systematic overdispersion (ρ = 1.50), with empirical majority-failure rates 2.96× higher than independence predicts. Epistemic grounding reduces hallucination rates but not error correlation, revealing frequency and structure as independent dimensions of the hallucination problem.