A Cognitive Universal AI Architecture for Lifelong Learning, Self Reflection, and Explainable Autonomous Intelligence

Abstract

ABSTRACT Modern artificial intelligence systems have achieved remarkable performance across specialized, closed-world domains. However, current architectures remain constrained by brittle knowledge representations, catastrophic forgetting during continuous adaptation, and an inherent opacity that hampers real-world autonomous deployment in mission-critical environments. To bridge this fundamental gap, this paper presents a novel Cognitive Universal AI Architecture (CUAA) that integrates continual lifelong learning, metacognitive self-reflection, and intrinsically explainable neuro-symbolic intelligence. The proposed framework combines an episodic-semantic dual memory replay engine with elastic weight regularization to mitigate catastrophic forgetting without infinite parameter growth. A meta-reflective system continuously evaluates internal prediction uncertainty, detecting out of-distribution dynamic shifts and triggering online self-correction. Simultaneously, an integrated causal explainability interface generates human-interpretable structural causal attribution trees in real time. We conduct comprehensive empirical and simulation-based evaluations across sequential multi-task decision settings. The results demonstrate that CUAA achieves an overall retainment rate of 89.2% across ten consecutive non stationary task shifts, outperforming state-of-the-art baselines by 47% while reducing explanatory generation latency by 38.5% with high decision fidelity. This study provides a unified blueprint for next-generation, self-aware, accountable autonomous intelligence.

KEYWORDS: Autonomous Intelligence, Continual Lifelong Learning, Explainable AI (XAI), Metacognition, Neuro-Symbolic Systems, Self Reflection.