Toward Self-Evolving Artificial General Intelligence: Adaptive Cognitive Architectures with Autonomous Knowledge Refinement

Abstract

ABSTRACT The pursuit of Artificial General Intelligence (AGI) requires cognitive architectures capable of continuous, autonomous self-evolution beyond fixed model parameters and post-hoc tuning. Current state-of-the-art foundation models, while demonstrating remarkable zero-shot and few-shot capabilities, remain bounded by rigid parametric knowledge distributions, static computational graphs, and susceptibility to hallucination and catastrophic forgetting during continuous fine-tuning. This paper presents a comprehensive review and structural framework for Toward Self-Evolving Artificial General Intelligence: Adaptive Cognitive Architectures with Autonomous Knowledge Refinement. We explore how integrating dynamic cognitive topologies, meta learning algorithms, epistemic graph verification, and non-destructive memory retrieval enables an artificial system to continuously refine its internal representations, autonomously curate its knowledge base, and dynamically reorganize its computational graph without human intervention. We analyze key structural components including metacognitive feedback loops, symbolic-subsymbolic integration, dynamic sparse routing, and memory consolidation mechanisms. Furthermore, we examine empirical benchmarks evaluating self-adaptation rate, hallucination mitigation, cross-domain reasoning, and algorithmic stability across iterative refinement cycles. Our synthesis highlights critical technical bottlenecks—such as drift control, unbounded complexity explosion, and epistemic drift—and outlines future research trajectories necessary to transition from static, large-scale predictive transformers to self-governing, resilient, and continuously evolving AGI systems.

KEYWORDS: Artificial General Intelligence (AGI), Self-Evolving Architectures, Adaptive Cognitive Systems, Autonomous Knowledge Refinement, Meta-Learning, Epistemic Verification, Continual Learning.