Pause Is Not Recovery: Containment, Revocation, Rollback, Recovery, and Fresh Readmission in Recursive AI R&D

Stopping an AI workload is not the same as restoring a system to a trustworthy state. This paper develops a structural framework for containment, revocation, rollback, recovery, and fresh readmission in recursive and highly automated AI research and development environments. The framework distinguishes six control states that are often collapsed in incident-response practice: pause, containment, revocation, rollback, recovery, and readmission. Each performs a different function and carries a different authority meaning. A pause stops or suspends a workload. Containment restricts propagation and limits the affected failure domain. Revocation removes previously valid authority. Rollback restores a prior technical state. Recovery reconstructs an operationally usable state while preserving evidence of the incident. Readmission determines whether that recovered state may once again participate in privileged operation. The central claim is that none of these states should silently imply the next. Pause is not containment. Containment is not revocation. Revocation is not rollback. Rollback is not recovery. Recovery is not readmission. Readmission is not restoration of historical authority. This distinction becomes critical in recursive AI R&D because automated systems may recreate agents, restore checkpoints, rotate credentials, reconstruct environments, reproduce model artifacts, or resume research loops faster than human operators can inspect the full history of an incident. The paper develops evidence-preserving rollback semantics, stale-authority detection, generation-bound revocation, quarantine boundaries, recovery evidence bundles, fresh readmission criteria, staged resume procedures, incident lineage, failure-domain locality, and successor-system re-entry rules. It further proposes adversarial tests for snapshot restoration, credential resurrection, hidden authority persistence, cross-compartment contamination, incomplete containment, evidence discontinuity, automatic reactivation, and premature readmission. The architecture is designed so that recovery does not rewrite history and a technically restored system cannot represent itself as a system that never failed. The paper concludes the six-paper series by connecting measurement, state-transition control, independent verification, human authority, and post-incident control into one coherent lifecycle for machine-speed AI R&D governance. This publication is intentionally limited to public research-level abstractions. It does not disclose unpublished patent claim language, confidential claim charts, private source locators, provider-specific production parameters, non-public test vectors, or other confidential implementation details. Structural Paper Series - Paper 06 Final Publication Edition v2.1 Research Program on Deterministic Infrastructure and Human-Centered AI Coordination Transition Intelligence Institute, Switzerland Institutional Establishment in Preparation Foundational Research Signature: The Second Waters