No-Meta Intelligence Under Ontology Drift: Information-Theoretic Limits and Operational Laws for Persistent Semantics

This work develops a theorem-scoped boundary theory for long-horizon semantic persistence under ontology drift in adaptive systems operating in a no-meta regime, i.e., without a continuously privileged external evaluator. The framework combines information theory, geometric modeling on manifolds, and operational design constraints. On the converse side, it identifies conditions under which persistence is fundamentally impossible: when effective information growth remains subcritical, tube-conditioned semantic success decays polynomially, yielding finite viability horizons for nontrivial targets. On the constructive side, it provides quantitative phase boundaries for transported consensus+innovation recovery and residual-class detectability under finite-information observation channels and heterogeneous multi-agent recovery. The paper further derives auditable operational laws that couple persistence feasibility with probe capacity, state/connection resource allocation, overlap rebates, replication dependence, blackout bursts, asynchronous staleness, temporal aliasing, and Byzantine transcript ambiguity. Building on these limits, it formulates acceleration–maintenance laws for no-meta superintelligence design through five explicit levers: quotient-based effective-dimension compression, sustained exogenous information injection, entropy-budget reallocation, geometric-frustration budgeting, and Byzantine-aware anchor scaling. The resulting statements are assumption-explicit, falsifiable, and deployment-oriented. Although motivated by AI autonomy, the theory is also applicable to broader adaptive cyber-physical and socio-technical systems that must preserve semantics under drift without permanent privileged oversight.