The Anti-Drift Cognitive Control Loop (ADCCL): A Geometric Foundation for Hallucination-Free AI

Background & Motivation: Large Language Models and autonomous agents exhibit a catastrophic failure mode known as epistemic drift ("hallucination"). This arises from a reliance on high-dimensional probability distributions without geometric grounding, making alignment a probabilistic aspiration rather than a structural guarantee. Methods & Formal Framework: We present the Anti-Drift Cognitive Control Loop (ADCCL), a non-stochastic governance layer. By enforcing a Sovereign Boundary of chi_s >= 0.9539 — derived from the holonomy of a 240-dimensional Stiefel manifold — we bound AI reasoning trajectories within a stable topological manifold. Results & Contributions: The ADCCL system, implemented in the 17-crate Rust ecosystem (chyren-adccl, chyren-metacog), provides: Structural Hallucination Elimination: States falling below threshold are regularized via the Schott Energy Derivative. Empirical Validation: Proven coherence maintenance at 141.99x Information Tension using the Trinity 2.0 Dataset (1,250 signals). Non-Maskable Interrupts: Automated halting of drift trajectories that violate geometric bounds. Implications & Future Work: This constitutes the first architecture where AI alignment is a structural invariant, enabling high-integrity sovereign intelligence orchestration.