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 chiₛ >= 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.