DEVELOPMENTAL METACOGNITIVE ARCHITECTURE (DMA) - A Framework for Transparent, Mentor Guided Artificial Cognition v3

The Developmental Metacognitive Architecture (DMA) is presented as a unified scientific framework for constructing artificial agents whose cognition develops through coherence‑driven dynamics, trust‑modulated inference, and mentor‑guided structural updates. Contemporary AI systems optimize opaque internal objectives and lack mechanisms for detecting, narrating, and reconciling representational conflict, resulting in silent drift, brittle reasoning, and misaligned behavior. DMA addresses these limitations by embedding non‑bypassable subsystems for coherence evaluation, dissonance surfacing, epistemic calibration, metacognitive monitoring, and slow‑cycle structural integration. Central to the architecture is the Coherence Field, a real‑valued function over the agent’s belief–action–goal configuration space that governs developmental dynamics through gradient‑based flow. Trust priors modulate alignment within this field, ensuring that coherence reflects epistemic quality rather than mere internal consistency. Development is further shaped by the Mentor Loop, a control‑theoretic mechanism that alternates between scaffolding, challenging, and reflective modalities based on coherence history, enabling the agent to escape shallow equilibria and progress toward increasingly integrated conceptual structures. A dual‑system action‑selection pipeline blends fast associative responses with deliberative reasoning through coherence‑dependent arbitration, while the Non‑Dominant Metacognitive Monitor (NDMM) detects override errors, rumination, arbitration drift, and calibration faults, triggering graduated interventions or mentor escalation. The resulting dynamics are explicitly non‑Markovian: transitions depend on coherence trajectories, trust evolution, and action histories rather than instantaneous state alone. The manuscript formalizes these mechanisms through operators for coherence computation, trust‑weighted alignment, mentor correction, conflict disambiguation, and subsystem interaction, and provides convergence guarantees for both unmentored and mentored developmental flows. DMA is situated relative to Markov decision processes, predictive processing, hierarchical Bayesian inference, metacognitive ML systems, and AGM belief revision, showing that each emerges as a special case under restrictive assumptions. The framework establishes a developmental, transparency‑anchored paradigm for artificial cognition—one in which agents grow through guided experience, maintain interpretable internal structure, and preserve alignment across the full arc of their cognitive trajectory.