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

Large Language Models and autonomous agents exhibit a catastrophic failure mode — epistemic drift ("hallucination") — arising from their reliance on high-dimensional probability distributions without geometric grounding. We present the Anti-Drift Cognitive Control Loop (ADCCL), a non-stochastic governance layer that replaces probabilistic approximations with geometric hard-logic. By enforcing a Sovereign Boundary of chi_s >= 0.9539 — the geometric ground state of a 240-dimensional Stiefel manifold — we demonstrate that AI reasoning trajectories can be bounded within a stable topological manifold, structurally eliminating hallucination. States falling below threshold are regularized via the Schott Energy Derivative or halted by Non-Maskable Interrupt. The ADCCL is implemented in the 17-crate Rust ecosystem (chyren-adccl, chyren-metacog) and empirically validated against the Trinity 2.0 Dataset (1,250 spectral signals), demonstrating systemic coherence maintenance at 141.99x Information Tension within the geometric boundary. This constitutes the first architecture where AI alignment is a structural invariant rather than a probabilistic aspiration.