Recursive Meta-Governance: A Formal Framework for Self-Stabilizing Institutions in the Superintelligence Era

The impending arrival of superintelligent AI systems poses an unprecedented challenge to human institutions: how can governance structures that oversee self-improving agents remain aligned with evolving human values when those agents will rapidly and irreversibly surpass their regulators in capability? This paper introduces Recursive Meta-Governance (RMG), a formal framework that embeds self-stabilizing, provably aligned meta-level institutions capable of governing lower-level systems—including AI agents—through endogenous recursion. Drawing on mechanism design, category theory, typed lambda calculus, and the scalable oversight literature, we define a recursive language for governance protocols, establish a minimal axiom system, and prove key properties: stability, alignment preservation under bounded capability growth, compositional modularity, and non-corruptibility under adversarial coalition pressure. We demonstrate applicability through lightweight formal simulations (freely executable Python pseudocode) and four conceptual case studies: the EU AI Act (Regulation (EU) 2024/1689), the NIST AI Risk Management Framework, corporate board governance, and the failure modes of decentralized autonomous organizations. Unlike static external oversight models, RMG creates an adaptive, self-correcting governance layer that co-evolves with the systems it regulates, guided at every step by formally verified alignment invariants. This work establishes the foundational theory for a new field we term recursive institutional engineering, offering a mathematically grounded pathway to safe long-term human flourishing amid transformative AI. All analysis is conducted with zero-budget tools (public literature, free Google Colab pseudocode, Overleaf/LATEX), making it fully replicable by any independent researcher.