The Minimal Continuity Record: Candidate Constitutional Invariants for Reconstructable AI Governance and Long-Term Stewardship

Artificial intelligence governance has advanced significantly in areas such as risk management, transparency, explainability, auditability, and regulatory compliance. While these approaches improve oversight, they do not fully address a more fundamental question: what minimum conditions must remain intact for AI systems to remain legitimately governable across time, changing implementations, and evolving institutional contexts? This paper proposes the Minimal Continuity Record, a constitutional methodology for identifying and evaluating implementation-independent conditions required for long-term AI governability. It distinguishes between a constitutional layer and an operational layer, introduces candidate constitutional invariants, and presents methods for validation through case studies, conformance evaluation, stress testing, and independent review. The paper argues that constitutional standing should emerge through evidence, reproducibility, and independent validation rather than assertion, and outlines a research agenda for constitutional AI governance grounded in stewardship, reconstructability, and long-term institutional responsibility.