Operational Meta-Governance for AI Systems: A Public Conceptual Framework for Meta-Governance and Three Supporting Technology Domains

This paper presents a public conceptual framework for operational meta-governance in AI systems. The framework positions meta-governance as an external judgment layer that coordinates how governance mechanisms interact before an AI-generated candidate becomes an operational commitment. It is supported by three technology domains: 1. audit-ready graph-based experience memory;2. dynamic adaptive boundary control; and3. multi-timescale decision-loop orchestration. The paper distinguishes logs from reusable experience memory, explains why AI outputs should not be reduced to immediate accept-or-reject decisions, and introduces HOLD as a legitimate intermediate state for preserving unresolved candidates without prematurely adopting or discarding them. The framework is intended to improve auditability, human oversight, operational control, and the ability to manage uncertainty across heterogeneous AI systems. It does not disclose implementation details such as algorithms, thresholds, schemas, state-transition specifications, APIs, deployment topology, source code, or patent-claim mappings. The English version is authoritative. The Japanese version is provided as a reference translation. The copyrightable text and figures in this publication are licensed under the Creative Commons Attribution 4.0 International license. This publication does not grant any patent, trademark, know-how, implementation, product, service, or other intellectual property rights beyond the rights expressly granted under CC BY 4.0.