A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI· 生成式与行动式AI认知能力差距的分类
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 advanc
探讨生成式与行动式AI认知能力差距，并提出分类。
- 核心方法
- 基于五大维度（持续状态建模、目标导向自主性、自我监控与控制、环境互动、学习与适应）对现有文献进行分类和综述，识别认知能力差距。
- 适合谁读
- 研究者
- 要解决的问题
- 生成式与行动式AI在长时间内可靠运行的能力受限，因为许多基础认知功能仍不完善。
- 关键实验
- 未提供
- 主要贡献
- 提出了一个系统性的分类框架，帮助研究者更好地理解AI的认知能力限制。
- 意义与局限
- 该分类框架为AI系统的进一步研究和开发提供了方向，但目前主要停留在理论层面，缺乏具体应用场景的验证。