Learning to Walk the Floor: Operational Policies for Gradual Self-Governance in Persistent AI Systems

This paper is part of an editorial sequence on governed persistence in persistent AI systems. It follows the minimal architectural layer and develops the next method-oriented layer: how a persistent system may learn to operate within a minimally governed persistence regime without reintroducing the persistence failures that the architecture was meant to prevent. Sequence context: A Structural Stability Architecture for Persistent AI Systems (Zenodo DOI: 10.5281/zenodo.19444524) Mathematical Companion to A Structural Stability Architecture for Persistent AI Systems A Minimal Architecture for Gradual Self-Governance in Persistent AI Systems (Zenodo DOI: 10.5281/zenodo.19558537) Learning to Walk the Floor: Operational Policies for Gradual Self-Governance in Persistent AI Systems (this record) Abstract: A minimally governed persistence regime is not sufficient by itself to control long-horizon drift in persistent AI systems. Once persistent and transient state are separated, writeback is mediated, operational modes exist, degradation signals become observable, and recovery is constrained, a new problem appears: how the system learns to operate within that regime without reintroducing the very persistence failures the architecture was meant to prevent. This paper addresses that next layer. Rather than treating self-governance as an all-at-once property, we develop the idea of learned persistence discipline: the progressive acquisition of writeback caution, propagation restraint, mode-sensitive intervention, and recovery discipline under bounded persistence risk. The aim is not to replace minimal architecture, but to explain how a persistent system may gradually become more competent at using it. We argue that such learning must itself be staged and governed. A persistent system should not begin with full discretion over writeback, propagation, or recovery. Instead, early operation should be conservative, with stricter admissibility, narrower propagation, and slower recovery. On that basis, governance-sensitive behavior may be refined progressively through feedback tied to invalid writeback, instability recurrence, over- and under-intervention, and contamination spread. The result is a second bridge concept. If the previous paper identified the minimal architectural floor needed for gradual self-governance to become plausible, the present paper asks how a system may learn to walk on that floor. The contribution is therefore method-oriented rather than purely architectural: not a universal solution to drift, and not a complete implementation blueprint, but an operational framework for learned persistence discipline within a minimally governed regime. This record is followed in the same editorial sequence by *A Runtime Specification for Governed Persistence in Persistent AI Systems* (DOI: 10.5281/zenodo.19558986). It is also included in the collected editorial unit *Governed Persistence in Persistent AI Systems: Collected Papers (April 2026)* (DOI: 10.5281/zenodo.19559205), which serves as a navigational entry point to the sequence as a whole.