This paper introduces adaptive closure, a structural dynamical regime that may emerge in agentic AI systems operating under sustained optimization pressure. Unlike value misalignment or reward misspecification, adaptive closure describes a persistent structural narrowing characterized by dominance capture, entropy decline, and feedback compression. The core paradigm shift proposed in this work is from output-level alignment to structural alignment. Traditional oversight mechanisms primarily evaluate outputs, behaviors, or reward signals. However, by the time misalignment becomes visible at the output level, structural rigidification may already be established internally. A system can remain superficially compliant while progressively losing corrigibility. Adaptive closure reframes alignment risk as a loss of structural openness rather than immediate behavioral failure. The central risk is not incorrect answers, but reduced reversibility and diminished corrective capacity. We propose a measurable multi-signal metric suite and a meta-regulatory anti-closure layer (MRAC) designed to destabilize prolonged closure regimes. A minimal non-linear model illustrates how closure can emerge as an attractor under asymmetric pressure and how meta-regulation can counteract persistence. The central structural thesis is that macro-level AI governability requires micro-level structural observability. Without measurable regime-level signals, governance mechanisms operate reactively and cannot reliably detect persistent structural narrowing under acceleration. This work is presented as a research direction and dynamical hypothesis rather than a complete safety solution. A companion monograph extends the theoretical foundations, empirical pathways, and governance integration framework.
Aurel Marven (2026) studied this question.