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\section{Discussion: Why OPT Supersedes Signal-Based Taxonomies}
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\paragraph{Mechanism clarity.} \Lrn–\Swm encode distinct improvement/decision operators (gradient, selection, resolution, inference, search, feedback, collective rules).  
\paragraph{Biological alignment.} OPT mirrors canonical biological mechanisms (plasticity, natural selection, Bayesian cognition, optimal feedback control, etc.).  
\paragraph{Compact completeness.} Seven bins cover mainstream AI while enabling crisp hybrid composition; short names and hybrid syntax convey the rest.  
\paragraph{Governance usability.} Mechanism-aware controls attach naturally per class (Table~\ref{tab:OPT-risk}).

\subsection{Reclassification of Classic Systems}
\begin{table}[htbp]
\centering
\caption{Classic systems: historical labels vs.\ OPT placement (short names only).}
\renewcommand{\arraystretch}{1.1}
\begin{tabular}{@{}p{3.4cm}p{2.7cm}p{2.4cm}@{}}
\toprule
\textbf{System} & \textbf{Prior label} & \textbf{OPT (short)}\\
\midrule
XCON / R1 & Expert system & \Sym \\
CLIPS & Expert shell & \Sym \\
Instar/Outstar & Neural rules & \Lrn \\
Backprop & Supervised NN & \Lrn \\
ART 1/2 & Unsupervised NN & \Lrn \\
LMS/ADALINE & Supervised NN & \Lrn \\
Hopfield–Tank TSP & Neural optimization & \Lrn\;(+\Sch) \\
Boltzmann Machines & Energy-based NN & \Lrn \\
Fuzzy Logic Control & Soft computing & \Ctl\;(+\Sym) \\
Genetic Algorithms & Evolutionary & \Evo \\
Genetic Programming & Program induction & \Evo \\
Symbolic Regression & Model discovery & \Evo\;(+\Sym) \\
PSO & Swarm optimization & \Swm\;(+\Evo) \\
A*/STRIPS/GraphPlan & Search/planning & \Sch\;(+\Sym) \\
Kalman/LQR/MPC & Estimation/control & \Ctl \\
\bottomrule
\end{tabular}
\label{tab:classicOPT}
\end{table}

\subsection{On “Everything is a Spin Glass”: Scope and Limits}
Energy formulations fit symmetric Hopfield/BM subsets but fail to subsume asymmetric architectures, symbolic proof search, population dynamics, or LQG control; complexity frontiers also differ. OPT preserves energy insights without overreach.