A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI
2026-08-03 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors review the current challenges in making AI systems that can think and learn like humans over long periods. They organize these challenges into five main areas: keeping knowledge over time, working independently toward goals, monitoring themselves, interacting with their surroundings, and learning from experience. They summarize recent progress and highlight what still needs improvement. The authors also propose a new framework to help guide future AI designs that aim for better reasoning, adaptability, and continuous learning.
Cognitive AIPersistent state modelingGoal-directed autonomySelf-monitoringAdaptive learningArtificial General IntelligenceEnvironment interactionContinual learning
Authors
Taye Akinrele, Sindhuja Penchala, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi
Abstract
Cognitive AI seeks to move beyond language generation and autonomous task execution toward systems capable of sustained reasoning, adaptive behavior, persistent memory, and self-regulation. While generative and agentic AI have demonstrated impressive capabilities across a wide range of tasks, many fundamental cognitive functions remain fragmented or weakly developed, limiting reliable operation over extended time horizons. This paper presents a taxonomy-driven survey of the major cognitive capability gaps that continue to constrain the development of Cognitive AI. The literature is organized around five dimensions: persistent state modeling, goal-directed autonomy, self-monitoring and control, environment interaction, and learning and adaptation. For each dimension, we review recent advances, identify recurring limitations, and discuss open research challenges. Building on these insights, we outline a conceptual Adaptive Cognitive Intelligence Architecture (ACIA) and examine emerging directions in cognition-centric evaluation. The proposed taxonomy provides a unified framework for organizing existing research, identifying unresolved challenges, and guiding the design of future cognitively capable systems. Together, the taxonomy, architectural perspective, and evaluation framework offer a roadmap for advancing AI systems that exhibit more reliable long-term reasoning, adaptive decision-making, and continual learning. The survey highlights key research opportunities toward more adaptive, reliable, and cognitively capable AI systems, providing a foundation for future progress toward Cognitive AI and, ultimately, Artificial General Intelligence (AGI).