Meta-Learning and Recursive Self-Improvement in Artificial General Intelligence: Opportunities, Risks, and Evaluation Metrics

Abstract

ABSTRACT The quest to achieve Artificial General Intelligence (AGI) has increasingly centered on paradigms capable of autonomous, continuous adaptation beyond frozen parametric distributions. While contemporary large-scale foundation models exhibit impressive few-shot capabilities, they remain fundamentally constrained by fixed weight architectures, susceptibility to catastrophic forgetting, and vulnerability to hallucinations during sequential task exposure. This comprehensive review investigates the convergence of meta-learning ('learning to learn') and Recursive Self-Improvement (RSI) as pivotal foundations for self-evolving AGI. We formalize an Advanced Adaptive Cognitive Architecture (AACA) that integrates dynamic sparse Mixture-of Experts (MoE) routing, non-destructive memory consolidation, and epistemic knowledge graph verification. Through structural modeling and empirical simulations across 10,000 self-refinement cycles, we analyze how meta learned optimization loops enable an artificial agent to autonomously modify its weights, reconfigure routing topologies, and suppress errors without human oversight. Furthermore, we address severe system-level risks, including epistemic drift, reward hacking, intelligence explosion instabilities, and loss of safety alignment. To enable rigorous validation, we propose a multi-dimensional benchmarking matrix evaluating adaptation speed, retention rate, hallucination suppression, and computational efficiency. Our synthesis provides a definitive theoretical and operational roadmap for transitioning from static predictive models to self-governing, resilient, and aligned AGI systems.

KEYWORDS: Artificial General Intelligence (AGI), Meta-Learning, Recursive Self-Improvement (RSI), Adaptive Cognitive Architectures, Epistemic Drift, Safety Alignment, Evaluation Metrics.