RGCC-X⁺ V3: Universal Cross-Model Adaptive Orchestration for Hallucination Mitigation with Bayesian Cost Calibration

RGCC-X⁺ V3 introduces a universal control-theoretic orchestration framework for mitigating hallucination in large language models (LLMs). Building upon a recursive geometric contraction model of epistemic drift, V3 extends prior versions with three principal advances: (i) Bayesian auto-calibration of the cost–risk coefficient λ, enabling deployment-time economic convergence without labelled supervision; (ii) a formally bounded cross-model transfer mechanism ensuring statistically consistent hallucination reduction across model families; and (iii) a conversation graph stability detector capable of identifying coordinated multi-turn adversarial sequences.Evaluated on a 624-question benchmark spanning three LLM families and eight stress categories, RGCC-X⁺ V3 achieves 35–43% hallucination reduction without model fine-tuning. A three-way ANOVA reveals no significant Model × RGCC interaction (p = 0.154), supporting cross-model robustness within tested families. Bayesian λ calibration converges within ~118 turns, reducing steady-state deployment cost from 1.28× to 1.19× baseline.Together, V1–V3 establish a mathematically grounded, model-agnostic regulation layer for hallucination mitigation that integrates stability analysis, adaptive routing, and economic self-calibration into a unified orchestration framework.