Architectural Transferability in Bounded AI: Five Conditions for Regulated Decision Domains

End-to-end machine learning deployments in regulated decision contexts face an in-tolerance for hallucinated outputs that current explainability methods address only af-ter the fact. Bounded AI denotes a form of prevention in which an AI/ML system’s outputs are constrained, by architectural design, to remain within a defined feasible region, while outputs outside that region are mathematically excluded. This study es-tablishes the conditions under which a bounded AI architectural pattern can be trans-ferred to other regulated decision domains. Establishing a clear distinction between transparency and prevention is an essential component for bounded-AI problems that operate in regulated environments. Transparency informs what an AI/ML model did. Prevention ensures the model cannot do certain things in the first place. This paper develops the structural transferability of a deterministic-first/learned- second archi-tecture to other high-stakes decision domains. The architecture’s domain-neutral type signatures are formalized and five conditions that destination domain must satisfy for the transfer to succeed are stated. Examples of three structural transfers are considered in depth: (a) predictive maintenance in aerospace and manufacturing; (b) energy-grid management and battery storage; (c) credit underwriting. For each transfer, the decay analogue, the conditional scaling factor, the SimDec joint-state inputs, the hallucina-tion-removal property, and the regulatory stakes are identified. Closure proofs that the rule sets exclude well-defined classes of hallucinated outputs from the feasible region are presented. An illustrative market-surveillance example that compares this new AI infrastructure with the Nasdaq SMARTS system is also included. The mar-ket-surveillance section positions the bounded architecture function-by-function against the SMARTS alerting paradigm, as complementary rather than as a competing infrastructure. The framework presented is both prospective and diagnostic. Given a candidate destination domain, the framework determines whether the architectural pattern will transfer and if not, identifies the structural prerequisite the domain fails. The architecture becomes domain-neutral through the act of transfer, not before it.