This work introduces the Regulatory Intelligence Paradigm, a formal viability-first framework for synthetic cognition that defines intelligence as the capacity of a system to maintain internal coherence and homeostatic stability under cognitive stress, rather than as task accuracy, reward maximization, or statistical optimization. The monograph formalizes the Regulatory Intelligence Paradigm as a regulatory physics for synthetic systems, specifying its geometric foundations, stability constraints, falsification criteria, and explicit non-claims. Intelligence is treated as an internally governed process constrained by homeostatic boundaries and stability conditions, rather than as an externally optimized objective. SpiralBrain v3.0 is presented as a reference implementation used to render the paradigm’s regulatory dynamics observable, measurable, and falsifiable. It functions as a deterministic, non-learning neurosymbolic instrument executed locally on standard hardware, enabling empirical study of paradigm-level behavior without positioning the system as a benchmark model or performance-oriented agent.