Resolving the dichotomy between the human-like yet constrained reasoning processes of Cognitive Architectures and the broad but often noisy inference behavior of Large Language Models (LLMs) remains a challenging but exciting pursuit, for enabling reliable machine reasoning capabilities in production systems. Because Cognitive Architectures are famously developed for the purpose of modeling the internal mechanisms of human cognitive decision-making at a computational level, new investigations consider the goal of informing LLMs with the knowledge necessary for replicating such processes, e.g., guided perception, memory, goal-setting, and action. Previous approaches that use LLMs for grounded decision-making struggle with complex reasoning tasks that require slower, deliberate cognition over fast and intuitive inference -- reporting issues related to the lack of sufficient grounding, as in hallucination. To resolve these challenges, we introduce LLM-ACTR, a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making by integrating the ACT-R Cognitive Architecture with LLMs. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations, injects this information into trainable LLM adapter layers, and fine-tunes the LLMs for downstream prediction. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability of our approach, compared to LLM-only baselines that leverage chain-of-thought reasoning strategies.
A Generic Review of Integrating Artificial Intelligence in Cognitive Behavioral Therapy
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université（索邦大学）；ICM（巴黎脑研究所）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié-Salpêtrière（皮蒂-萨尔皮特里耶医院）
A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback
Faculty of Artificial Intelligence in Education（人工智能教育学院）；Central China Normal University（中国中部师范大学）；School of Electrical and Electronic Engineering（电气与电子工程学院）；Hubei University of Technology（湖北工业大学）
Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis
Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）；Birla Institute of Technology and Science（贝拉理工与科学学院）；NUS Centre for Trusted Internet and Community, National University of Singapore（新加坡国立大学可信互联网与社区中心）
ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations
Microsoft Research（微软研究院）；Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属上海市精神卫生中心老年精神科）
Deep Search with Hierarchical Meta-Cognitive Monitoring Inspired by Cognitive Neuroscience
Gaoling School of Artificial Intelligence\ University of China Beijing China；School of Information Technology；Search Applications Department, Tencent Beijing China；Beijing University of Posts；Gaoling School of Artificial Intelligence\ University of China；Search Applications Department, Tencent
Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models
1Department of Software；IT Engineering, École de technologie supérieure, Montreal, Canada 2School of Electrical \& Computer Engineering, National Technical University of Athens
Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation
Learning, Data and Robotics (LDR) ESIEA Lab（学习、数据与机器人（LDR）ESIEA实验室）；Department of Computer Engineering（计算机工程系）；Graduate School of Management and Innovation（管理与创新研究生院）；Faculty of Science（科学学院）；Lebanese University（黎巴嫩大学）
Integrating Generative AI-enhanced Cognitive Systems in Higher Education: From Stakeholder Perceptions to a Conceptual Framework considering the EU AI Act
Faculty of Information Technology and Electrical Engineering, University of Oulu（奥卢大学信息技术与电气工程学院）
A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots
Ritsumeikan University（立命馆大学）；The University of Tokyo（东京大学）；The Whole Brain Architecture Initiative（全脑架构倡议）；Osaka University（大阪大学）；Okinawa Institute of Science and Technology Graduate University（冲绳科学技术大学院大学）；Tamagawa University（玉川大学）；The University of Electro-Communications（电气通信大学）
Flexible and Inherently Comprehensible Knowledge Representation for Data-Efficient Learning and Trustworthy Human-Machine Teaming in Manufacturing Environments
Airbus Central R&T（空客中央研发部）
本文利用 Conceptual Space 框架和典型性量化模型表示智能体知识，解决制造领域数据稀缺下的可解释分类问题，提升人机协作的可信赖性。
Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games
South Westphalia University of Applied Sciences（南威斯特法伦应用科学大学）；Otto von Guericke University Magdeburg（奥托·冯·格里克马格德堡大学）；Hochschule Düsseldorf University of Applied Sciences（杜塞尔多夫应用科学大学）
Integrating a Causal Foundation Model into a Prescriptive Maintenance Framework for Optimising Production-Line OEE
Department of Engineering University of Luxembourg（工程系卢森堡大学）；Department of Computer Science University of Luxembourg（计算机科学系卢森堡大学）；Chair of Production and Maintenance Management TU Wien（生产与维护管理系维也纳技术大学）
