A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI

ID: 4c04e586
原文链接: https://arxiv.org/abs/2608.02553
PDF: https://arxiv.org/pdf/2608.02553v1
作者: Taye Akinrele, Sindhuja Penchala, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi
日期: 2026-08-03
更新: 2026-08-03
分类: agents
来源类型: paper
标签: cognitive-ai, agentic-ai, long-term-reasoning, survey
质量评分: 4/5
抓取时间: 2026-08-05T04:19:03Z

中文导读

这篇综述将生成式和 Agentic AI 的认知能力缺口归纳为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，并提出 Adaptive Cognitive Intelligence Architecture 和面向认知的评估方向，适合作为长时间推理持续记忆和自适应 Agent 设计的分类框架

为什么值得关注

A taxonomy of capability gaps for long-horizon cognitive and agentic AI.

Grounded relevance: authors, date, arXiv categories, and abstract claims below; no extra experimental claims beyond the abstract/metadata.

arXiv comment: 15 pages, 4 figures

关键信息

论文标题：A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI
作者：Taye Akinrele, Sindhuja Penchala, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi
arXiv：https://arxiv.org/abs/2608.02553
发布时间：2026-08-03
arXiv 分类：cs.AI
关联标签：cognitive-ai, agentic-ai, long-term-reasoning, survey

English 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).

English Summary

Obsidian Notes

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中文导读与价值判断均锚定在条目已有摘要、论文摘要、作者、日期与分类信息上；未补充论文摘要之外的实验细节。
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