Toward a Basis Representation of Drift Forensics: A Candidate Representational Framework for Diagnosing, Classifying,— and Addressing Drift in Large Models and Complex Systems

This paper introduces a forensic engineering framework for identifying and stabilizing "drift" in large-scale autonomous systems and AI models. While traditional AI safety focuses on static guardrails, this work treats system behavior as a dynamic network problem that requires active maintenance and precise diagnostic tools. Key Functional Components: The Drift Taxonomy: A categorized list of 36 specific failure modes (Drift Classes), ranging from external boundary probing to internal architectural decay. Legion Signatures: Atomic, observable data traces for each Drift Class that allow engineers to move from "suspected error" to "confirmed forensic event. " Stabilisation Operators (SOs): Ten functional interventions designed to neutralize drift and return a system to its intended baseline. The Equilibrium Protocol (T=1. 0): A proposed method for locking a system into a "Teleological Equilibrium" where intent and execution are synchronized, optimizing both safety and energy consumption. This document serves as Paper 5 in the Verbanatomy series, providing the operational "Forensic Layer" to complement the previously established Ontology, Calculus, Protocol, and Architecture papers. It is intended for AI auditors, systems engineers, and governance designers who require a structured, nameable vocabulary for system failure and recovery.