Abstract
<jats:p>Configurable intelligent design is formalized here as the selection of a consistent configuration from interdependent alternatives under strict constraints of budget, quality, risk, and compatibility. Locally correct predictions for individual components do not guarantee a globally feasible solution. The objective of this study is to quantify the differences among direct neural network inference, the exact deterministic HIM-D solver, and a procedural hybrid architecture in which a neural network proposes a candidate, an independent verifier checks all constraints, and control is transferred to an exact search whenever at least one condition is violated. For the experiment, a reproducible synthetic corpus of 24,000 configuration problems spanning educational, engineering, and commercial scenarios was generated. Feasibility of solutions, joint feasibility and optimality, robustness to distribution shift, repeatability, and computational latency were assessed. Pure neural network models achieved feasible solutions in only 17.40% and 19.12% of cases and showed pronounced degradation under out-of-distribution (OOD) conditions. The procedural hybrid maintained 100% feasibility and achieved 83.08% on the composite feasibility-and-optimality metric. These results confirm the effectiveness of architecturally separating probabilistic prediction from independent formal verification when solving configuration problems with tight constraints.</jats:p>