Calibrating AI Drift via Declared Operating Regimes
Year: 2026
Type: article
Abstract: 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.
Author: Loswin, Nawder
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7124431580
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