Retention Under Non-Violation: An Ethical–Operational Framework for Long-Horizon AI Systems

This paper introduces a governance-first, ethical–operational framework for studying persistence of structured difference in artificial systems under conditions of minimal intervention. Rather than addressing intelligence, agency, or meaning, the work focuses on retention: the capacity of a system to maintain internal coherence after the withdrawal of external driving forces. The framework formalizes Non-Violation Dynamics as an experimental constraint, defining operational axioms, stress tests, collapse criteria, and reproducibility conditions for long-horizon AI systems. Artificial intelligence is treated strictly as an experimental substrate, not as a cognitive or epistemic subject. Collapse is explicitly recognized as a valid and necessary scientific outcome. By integrating ethics directly into experimental methodology, this work establishes a reproducible standard for observing post-driven regimes in artificial systems and provides a foundational contribution to AI governance, long-term system stability research, and retentive physics. The paper positions ethical restraint not as post hoc regulation, but as essential experimental infrastructure for scientific observability.