AI_Coherence_Framework_v1_1

This paper proposes a framework for investigating coherence measurement and regulation in AI systems. It connects a candidate Coherence Index (C-Index) with the Embodied Coherence Regulation (ECR) architecture, a proposed loop for monitoring system behavior, classifying drift, selecting interventions, and checking recovery. The framework draws on concepts from feedback control, information theory, and physical coherence. The expression C = 1 − (ΣN/f), previously termed the "Law of Coherence," is presented here as a heuristic relating an operationally defined burden of contradiction and drift to corrective activity — not as a derived physical law. Its units, range, and boundary conditions remain open (see Part 1.4). Numerical thresholds (0.85/0.60) are illustrative operating bands, not empirically calibrated values. Three specialized branches are discussed, each with its own evidentiary status stated per its primary publication rather than inherited from the umbrella framework: ECR-H (hallucination detection, a detailed measurement theory explicitly unvalidated at scale per its own source), ECR-D (drift monitoring, fully specified with reference pseudocode but reporting no empirical validation), and EAAI (privacy-preserving ephemeral memory architecture, including the ECR-P privacy reflex documented in EAAI v1.4). This paper reports no completed sandbox or integrated evaluation of its own. A separate VR/AR study is referenced but not independently verified here pending its methods and sourcing. Comparisons drawn to human cognition, organizations, and faith practices serve as interpretive motivation, not evidence of shared physical mechanisms across domains. Version 1.1 supersedes "Unified Theory of AI Coherence" v1.0, correcting internal arithmetic errors in worked examples, replacing unification language with proposal-and-evidence framing throughout, and adding an evidence-status table distinguishing definitions, design proposals, reported results, and open hypotheses.