AI 中文总结
本文针对制约认知AI发展的认知能力差距开展分类学综述，梳理五大维度的进展与挑战，提出ACIA架构及认知导向评估框架，为构建可靠认知AI与AGI提供路线图。
AI 中文摘要
认知AI旨在超越语言生成与自主任务执行，向具备持续推理、自适应行为、持久记忆及自我调节能力的系统迈进。尽管生成式AI与智能体式AI在各类任务中展现出令人瞩目的能力，但诸多基础认知功能仍处于碎片化或欠发展状态，限制了其在长时间尺度上的可靠运行。本文针对持续制约认知AI发展的主要认知能力差距开展了一项基于分类学的综述研究。文献围绕五个维度展开梳理：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。针对每个维度，我们综述了近期进展，识别出反复出现的局限性，并探讨了未解决的研究挑战。基于这些洞见，我们提出了一个概念性的自适应认知智能架构（Adaptive Cognitive Intelligence Architecture，ACIA），并考察了以认知为核心的评估的新兴方向。所提出的分类学为组织现有研究、识别未解决挑战及指导未来认知能力系统的设计提供了统一框架。分类学、架构视角与评估框架共同构成了推进AI系统的路线图，这类系统将展现出更可靠的长期推理、自适应决策与持续学习能力。该综述强调了构建更具适应性、可靠性与认知能力的AI系统的关键研究机遇，为认知AI乃至最终实现通用人工智能（Artificial General Intelligence，AGI）的未来进展奠定了基础。
英文摘要
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).
Comments15 pages, 4 figures
