Self-Reflective Meta-Learning Structures in AI Cognitive Feedback Systems

Abstract

Self-reflective meta-learning introduces an internal cognitive feedback layer that enables AI systems
to evaluate and refine their own representational states during live inference. Unlike traditional
retraining-based adaptation, this framework continuously monitors embedding consistency, structural
stability, and temporal representational drift to detect reliability degradation before output accuracy
declines. The proposed methodology integrates intermediate representation capture, proportional
adaptation policies, and stability-governed update gating to maintain reasoning coherence while
avoiding disruptive full-model retraining. Experimental evaluation across stable, gradually shifting,
and abruptly shifting data environments shows that self-reflective adaptation significantly delays
performance decay and preserves functional reliability under continuous deployment. The results
demonstrate that cognitive introspection offers a practical and efficient foundation for sustained model
robustness in dynamic operational contexts.