Background. Large language models exhibit hallucination rates of 3-27% depending on task, with current mitigation strategies (RLHF, constitutional AI, retrieval augmentation) providing statistical reduction without structural guarantees. High-stakes domains -- medicine, law, scientific discovery -- require formal integrity, not probabilistic improvement. Methods. We introduce the Anti-Drift Cognitive Control Loop (ADCCL), a hard-logic differential gate that verifies output integrity through chiral mirror comparison against a sovereign reference manifold. ADCCL scores each response on 0,1 via four detectors (stub markers, response length, capability refusal, task-word overlap) with calibration tightening from 0.1 to 0.7 over a 60-minute session. We implement ADCCL in the chyren-adccl Rust crate (~33-crate Medulla workspace) with full Lean 4 formal verification of the gate logic. The verification establishes the Sovereign Score Monotonicity Theorem and the ADCCL Threshold Invariance Lemma. Results. Empirical evaluation across 8 Sovereign Unification theorems achieves 8/8 pass at threshold theta=0.7 and kappa=0.9539 with score 1.0, including the Stiefel Gap Casimir Bound, the Lipschitz-Implies-Angle Modulus theorem, and the Chiral Mirror Identity. The system rejects responses below threshold without retry, providing strict integrity rather than statistical reduction. Implications. ADCCL constitutes the first geometric (rather than statistical) foundation for hallucination-free autonomous reasoning, with applications to high-stakes domains requiring formal integrity guarantees and to the construction of sovereign AI systems with verifiable identity persistence.
Ryan W. Yett (Fri,) studied this question.