AI-Native Meta-Architecture: A Formal Reference Architecture for Constitutional Human–AI Co-Cognition

The AI-Native Meta-Architecture is a constitutional reference architecture that provides structural clarity, normative stability, and scalable autonomy across AI-mediated workflows. Rather than proposing a new model or algorithm, this work reframes AI alignment as an architectural systems engineering problem, following ISO/IEC/IEEE 42010. The architecture separates: Human meaning formation (upstream) Bounded AI orchestration (execution) Cybernetic constitutional governance (downstream) By forcing invariants through a strict schema compiler, the architecture ensures that autonomous systems execute only within verifiable, invariant-preserving boundaries. Core contributions include: C1 — Reference Architecture: A 12-node Directed Acyclic Execution Graph (DAEG) mapping the full lifecycle of Human–AI co-cognition. C2 — Formal Mathematical Model: Definitions of state spaces, invariant posets, compiler functions, and formal proofs of invariant preservation and bounded termination. C3 — Machine-Readable Constitution: A canonical JSON Schema (v4.0.0) translating human invariants into executable constraints. C4 — Cybernetic Governance Model: A second-order meta-loop enabling constitutional amendments, meaning re-examination, and adaptive governance. C5 — Evaluation Protocol: A Design Science validation protocol covering invariant stability, escalation correctness, meta-loop convergence, and compilation determinism. C6 — Reference Implementation: A reproducible implementation including the schema compiler, escalation engine, meta-operator, and example execution traces. This release includes the full v4.0 PDF, formal proofs, JSON schema, glossary, evaluation protocol, and canonical TikZ/SVG figures (Invariant Hierarchy and Schema Compilation Pipeline). All materials are published under CC BY 4.0.