Governing Autonomous Learning in Nested Learning Frameworks: A State Machine-Based Engineering Methodology

Nested Learning (NL) reformulates models as multi-level nested optimization problems, supporting continual learning, meta-learning, and self-modification capabilities, while simultaneously introducing potential safety risks. This paper proposes an engineering-grade governance methodology-the Nested Learning Governance State Machine (NLGSM)-that employs an external state machine management system to enforce strict constraints on autonomous learning modules, ensuring they cannot directly control production outputs, influence critical decision paths, or possess unconstrained write permissions. The governance framework adopts a declarative transition table architecture and an Event-Decision-Action three-layer pipeline, defining eight states: Learning, Validation, Frozen, Release, Rollback, Safe Halt, Diagnosis, and Recovery Plan, and implements the core governance principle of "knowledge must be approved before entering production" through Governed Artifacts. This paper clarifies the central role of humans in defining state transition rules, auditing learning outcomes, managing knowledge lifecycles, and approving production memory integration. As an engineering methodology framework, this paper provides complete architectural design, interface specifications, and reference implementations, offering an actionable pathway for the safe engineering of autonomous learning AI systems.