A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI

2026-08-03

AI Summary

Current generative and agent-based AI systems exhibit significant limitations in cognitive capabilities such as sustained reasoning, adaptive behavior, persistent memory, and self-regulation, hindering their ability to operate reliably over extended periods. This work proposes a novel taxonomy of cognitive capability gaps, structured around five key dimensions: persistent state modeling, goal-directed autonomy, self-monitoring and control, environmental interaction, and learning adaptation, offering a systematic review of the current research landscape. Building upon this framework, we introduce the Adaptive Cognitive Intelligence Architecture (ACIA) and a cognition-centric evaluation methodology that integrates principles from cognitive science and artificial intelligence. Together, they provide a theoretical foundation and a systematic roadmap for developing next-generation AI systems capable of long-term reasoning, adaptive decision-making, and continuous learning.

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).

Problem

• Cognitive AI
• capability gaps
• generative AI
• agentic AI
• Artificial General Intelligence

Innovation

• Cognitive AI
• capability gaps
• taxonomy
• Adaptive Cognitive Intelligence Architecture
• cognition-centric evaluation