本文探讨了人工智能两个子学科的整合，它们被用于开发表现出智能行为的人工代理：大语言模型（LLM）与认知架构（CA）。我们提出三种整合方法，每种均以理论模型为基础，并得到初步经验证据支持。模块化方法引入了四个具有不同整合程度的模型，采用思维链提示，并从增强型LLM、Common Model of Cognition（认知通用模型）以及认知模拟理论中汲取灵感。能动性方法受Society of Mind（心智社会）理论和LIDA认知架构启发，提出形成在微观与宏观认知层级交互的代理集合，这些交互由LLM或符号组件驱动。神经符号方法从CLARION认知架构中获得灵感，提出一种模型：其中自下而上的学习从LLM层提取符号表征，自上而下的引导则利用符号表征指导LLM层中的提示工程。这些方法旨在发挥LLM与CA各自优势，同时缓解其弱点，从而推动更鲁棒AI系统的开发。我们还讨论了与每种方法相关的权衡与挑战。
英文摘要：
This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs). We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. The modular approach, which introduces four models with varying degrees of integration, makes use of chain-of-thought prompting, and draws inspiration from augmented LLMs, the Common Model of Cognition, and the simulation theory of cognition. The agency approach, motivated by the Society of Mind theory and the LIDA cognitive architecture, proposes the formation of agent collections that interact at micro and macro cognitive levels, driven by either LLMs or symbolic components. The neuro-symbolic approach, which takes inspiration from the CLARION cognitive architecture, proposes a model where bottom-up learning extracts symbolic representations from an LLM layer and top-down guidance utilizes symbolic representations to direct prompt engineering in the LLM layer. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems. We discuss the tradeoffs and challenges associated with each approach.
MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Academy of Chinese Medical Sciences（广东省中医药科学院中医证候重点实验室，广州中医药大学第二附属医院，广东省中医院，广东省中医药科学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Department of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电子与计算机工程系）
Rethinking Explainable Disease Prediction: Synergizing Accuracy and Reliability via Reflective Cognitive Architecture
Institute for Artificial Intelligence, Peking University；School of Software \& Microelectronics, Peking University；School of Computer Science, Peking University；School of Life Sciences, Peking University；Department of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences；Peking Union Medical College Beijing, China
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG
Department of Artificial Intelligence in Healthcare, International Academia of Biomedical Innovation Technology（人工智能医疗系，国际生物医学创新技术学院）；National Taiwan University（台湾大学）；Neuro Industry Research, Neuro Industry, Inc.（神经产业研究，神经产业公司）；Department of Biomedical Engineering, University of Southern California（生物医学工程系，南加州大学）；Taiwan Artificial Intelligence Association（台湾人工智能协会）
TEAR: Table Extraction with Attribute Recommendation from Texts via Large Language Models
Hong Kong University of Science and Technology（香港科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；South China Normal University（华南师范大学）
Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks
ETH Zürich（苏黎世联邦理工学院）；University of Sussex（萨塞克斯大学）；Imperial College London（帝国理工学院）；University of Oxford（牛津大学）；University of Cambridge（剑桥大学）；The Alan Turing Institute（艾伦·图灵研究所）；Queen Mary University of London（伦敦玛丽女王大学）
Cooperative Design Optimization through Natural Language Interaction
OMRON SINIC X Corporation（OMRON SINIC X公司）；The University of Tsukuba（茨口大学）；The University of Tokyo（东京大学）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
Self-Emotion-Mediated Exploration in Artificial Intelligence Mirrors: Findings from Cognitive Psychology
University of Coimbra（科英布拉大学）；Institute for Biomedical Imaging and Translational Research（生物医学影像与转化研究 institutes）；Institute of Systems and Robotics（系统与机器人研究所）
Towards an intelligent assessment system for evaluating the development of algorithmic thinking skills: An exploratory study in Swiss compulsory schools
Università della Svizzera Italiana（瑞士意大利语区大学）；Faculty of Informatics（信息学院）
