这篇综述把生成式和智能体AI的认知短板按五个维度梳理清楚了，还给了个ACIA架构思路，适合想系统了解认知AI研究现状的人。
该论文提出一种针对生成式与智能体AI认知能力差距的分类法综述，围绕持久状态建模、目标导向自主性、自我监控与控制、环境交互、学习与适应五个维度组织现有研究。论文回顾了各维度的最新进展，指出常见局限，并提出自适应认知智能架构（ACIA）作为统一框架。还讨论了以认知为中心的评价方向，为迈向长期可靠推理和持续学习的认知AI提供路线图。
A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI
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).