Regulated Multiplicity: An AI Systems Architecture for Robust, Self‑Correcting Models

This paper develops Regulated Multiplicity as an AI systems architecture grounded in internal diversity, structured disagreement, regulatory adjudication, and persistent self‑modeling. Modern large language models are monolithic: a single forward pass produces a single output, with no mechanism for detecting internal conflict or revising errors. Regulated Multiplicity proposes a different structure. Multiple semi‑independent submodels generate competing representations; a divergence module measures their disagreement; a regulator adjudicates among them using a persistent self‑model; and an iterative feedback loop drives convergence toward coherent output. The paper formalizes this architecture, presents a minimal working prototype, and derives testable predictions about hallucination reduction, robustness, introspective behavior, and long‑term coherence. It also outlines a research agenda for evaluating the architecture through ablation studies, convergence dynamics, and self‑model learning. The paper does not make metaphysical claims about consciousness; it shows that the structural features emphasized in several scientific theories of consciousness—internal multiplicity, conflict detection, regulatory integration, and self‑modeling—also define a practical design pattern for more reliable AI systems. Regulated Multiplicity thus provides a unified framework connecting philosophical models of mind with implementable architectures for next‑generation AI.