What question did this study set out to answer?

The research aims to establish a definition of completion integrity for AI systems, ensuring safe transitions between workflows.

May 26, 2026Open Access

Decision-OS V12: Completion Integrity

Key Points

The research aims to establish a definition of completion integrity for AI systems, ensuring safe transitions between workflows.
Introduced three core requirements: Past Fidelity, No Hidden Burden, Future Restartability.
Defined six failure modes and a PASS/DELAY/BLOCK Completion Gate.
Presented the Minimal Completion Record and updated frameworks for operational reference.
Completion integrity prevents unresolved issues from being transferred to future AI states.
Framework effectively minimizes risks of false completion that can disrupt self-evolution.
Integrations improve the contextual awareness of completion requirements, ensuring operational robustness.

Abstract

Decision-OS V12 introduces Completion Integrity as a closure condition for self-recursive and self-evolving AI systems. The central problem is False Completion: a state that appears complete under local judgment, but passes unresolved burden, hidden assumptions, missing stop conditions, trajectory drift, or contaminated completion judgment to a future self in a form that cannot be safely reconnected, verified, corrected, or re-anchored. As AI workflows increasingly migrate across tools, models, and sessions, “completion” by one AI often means the next AI starts from a broken state. What to preserve, what to compress, how to judge closure, and how to safely restart are becoming operational problems rather than theoretical ones. This paper argues that completion should not mean proven correctness or artifact-level polish. Completion should mean future-restartable closure: an update is complete only when the next model, session, toolchain, or capability state can inherit enough control structure to restart safely. The framework defines three core requirements: Past Fidelity, No Hidden Burden, and Future Restartability. It also introduces six failure modes, a PASS/DELAY/BLOCK Completion Gate, a Minimal Completion Record, and the Error Conversion Principle. Version 2 integrates Completion Context Contamination as a sixth failure mode, adds the Fresh Evaluation Context Requirement to the Completion Gate, adds completioncontext to the Minimal Completion Record, and updates references and figure consistency. The title and core thesis remain unchanged. The paper should be read as a conceptual and operational framework rather than an empirical benchmark or complete implementation. Its purpose is to prevent unfinished, uncertain, misaligned, or improperly judged work from being falsely closed in a way that breaks future self-evolution. In the Decision-OS lineage, V12 connects V10 Survival-Bounded Planning and V11 Reconnectable Forgetting to a closure layer: how a future self can continue without hidden-context reconstruction. Companion artifact: A minimal GitHub companion artifact is available here: https: //github. com/shin4141/decision-os-v12-completion-integrity It includes the Minimal Completion Record schema, PASS/DELAY/BLOCK examples, checklist, lightweight validator, and CI validation. The artifact is provided as an operational reference, not as a full AI agent or complete decision engine.

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