Bridging the Gap: Toward Cognitive Autonomy in Artificial Intelligence

TL;DR

This paper critically examines the limitations of current AI systems in achieving true autonomy and proposes a neurocognitively inspired framework to develop more self-aware, adaptable, and goal-oriented artificial intelligence.

Contribution

It identifies seven core deficiencies in modern AI and outlines a neurocognitive-inspired architecture to advance toward cognitive autonomy.

Findings

Current AI lacks intrinsic self-monitoring and meta-cognitive awareness.

Scaling models alone cannot overcome fundamental cognitive limitations.

A paradigm shift towards cognitively grounded AI is necessary for true autonomy.

Abstract

Artificial intelligence has advanced rapidly across perception, language, reasoning, and multimodal domains. Yet despite these achievements, modern AI systems remain fundamentally limited in their ability to self-monitor, self-correct, and regulate their behavior autonomously in dynamic contexts. This paper identifies and analyzes seven core deficiencies that constrain contemporary AI models: the absence of intrinsic self-monitoring, lack of meta-cognitive awareness, fixed and non-adaptive learning mechanisms, inability to restructure goals, lack of representational maintenance, insufficient embodied feedback, and the absence of intrinsic agency. Alongside identifying these limitations, we also outline a forward-looking perspective on how AI may evolve beyond them through architectures that mirror neurocognitive principles. We argue that these structural limitations prevent current architectures, including deep learning and transformer-based systems, from achieving robust generalization, lifelong adaptability, and real-world autonomy. Drawing on a comparative analysis of artificial systems and biological cognition [7], and integrating insights from AI research, cognitive science, and neuroscience, we outline how these capabilities are absent in current models and why scaling alone cannot resolve them. We conclude by advocating for a paradigmatic shift toward cognitively grounded AI (cognitive autonomy) capable of self-directed adaptation, dynamic representation management, and intentional, goal-oriented behavior, paired with reformative oversight mechanisms [8] that ensure autonomous systems remain interpretable, governable, and aligned with human values.