Codex Admissibility Governance™ Priority and Authorship Record Kemar Armando Morrison

This work provides an architectural solution to long-horizon drift by preventing invalid internal states from ever being instantiated. Codex Admissibility Governance™: An Architectural Elimination of Long-Horizon Drift in Adaptive AI Systems Long-horizon drift and hallucination in contemporary AI systems arise from a structural flaw: systems are allowed to instantiate invalid internal states before those states are verified. This work resolves that flaw. We introduce Codex Admissibility Governance™, a model-agnostic control-plane architecture that enforces pre-instantiation authorization on every state transition. The system strictly separates proposal from permission. Proposed internal states are evaluated against invariant constraints prior to commitment. Non-admissible transitions are deterministically rejected or projected. They do not instantiate. Because invalid states never become real, drift does not accumulate. Hallucinations do not compound. Long-horizon divergence is structurally prevented. This mechanism operates independently of model weights, datasets, prompts, alignment layers, or fine-tuning. It functions at the execution layer and is applicable to large language models, agentic systems, multimodal architectures, and continuous-control frameworks. We provide a runnable reference implementation and empirical contrast between ungated and governed execution. Results demonstrate bounded evolution across extended horizons, deterministic suppression of instability, and full auditability of authorization decisions without disclosure of proprietary internal semantics. Drift is not an unavoidable property of adaptive intelligence. It is the result of architectures that permit unauthorized state transitions. Codex eliminates drift at the architectural level.