Constitutional Architectures for Adaptive Intelligence Deterministic Simulation and Quorum-Gated Mutation as Structural Governance.

ABSTRACT Adaptive systems increasingly interleave learning, policy change, and canonical state mutation within a single probabilistic execution plane. This architectural conflation produces silent behavioral drift, uncontrolled privilege escalation, and non-deterministic state transitions without explicit constitutional authority separation. We propose a constitutional architecture for adaptive intelligence in which cognition and governance are structurally separated and all non-trivial canonical state mutations are governed acts.The model centers on a mutation validity condition combining invariant preservation, deterministic shadow simulation, capability authorization, and quorum-gated commit under Byzantine fault tolerance. A deterministic shadow operator evaluates proposed mutations against canonical snapshots and produces cryptographic replay hashes, triggering quarantine or SAFE-HALT upon replay mismatch. A quorum function enforces class-dependent thresholds with 3f+1 validators and 2f+1 commit quorum. A reflex containment layer enforces low-latency safety under the structural constraint L_reflex < L_cog. We formalize the system model, define constitutional separation, prove safety under valid mutation, formalize an adversarial model, construct a conflation instability counterexample, provide prototype empirical results, and map the architecture to existing container and kernel primitives. As adaptive systems scale beyond real-time human oversight, constitutional separation with deterministic simulation and quorum-gated mutation provides a structurally enforceable boundary for safe and auditable evolution. In Plain English : How to let AI systems evolve and learn safely without ever letting them secretly change the rules of reality.