Structural Limits of Anthropomorphic Intelligence Models: Regime Inhibition, Regime Selection, and the Failure of Global Metrics

This technical note provides a structural, non-prescriptive analysis of recurring failure modes observed in artificial intelligence systems, cognitive evaluation practices, and academic knowledge production. Rather than advancing a cognitive theory or proposing new architectures, the work isolates a common error of category: the extension of local metrics, constructs, and evaluative regimes beyond their domain of validity. By contrasting evolutionary systems characterized by irreversible closure with iteratively updatable artificial systems, the analysis highlights the role of regime-level constraints in ensuring stability, comparability, and coherence. Particular attention is given to the structural functions of pre-emptive regime inhibition and hierarchical regime selection, treated here as necessary conditions for stability rather than as implementative mechanisms. The paper further examines how the misuse of globalized metrics—such as scalar or ordinal performance indicators—produces systematic distortions through cognitive compression and self-selection, leading to non-correctable closure failures. The contribution is strictly diagnostic: it aims to clarify structural limits and interpretative boundaries across domains, without introducing operational solutions, algorithms, or design prescriptions.