Calibrating AI Drift via Declared Operating Regimes

This upload is a short technical note describing a minimal method for calibrating AI behavioral drift through explicit declaration of operating regimes. The core claim is narrow: when assumptions about coherence (baseline/zero-state), symmetry expectations, and correction pathways are made explicit, drift becomes a bounded and analyzable dynamic rather than an uncontrolled failure mode. The approach is architecture-agnostic and requires no modification to underlying models. The note includes a checklist-style validation mapping that transforms common failure concerns into explicit, testable configuration domains. The purpose of this artifact is citation and reference rather than platform adoption or system enforcement.