This booklet, Unlocking the Wisdom of Multi-LLM Collaborative Intelligence, serves as an accessible introduction to the full volume The Path to Artificial General Intelligence. Through fourteen aphorisms, it distills the core principles of Multi-LLM Agent Collaborative Intelligence (MACI), a framework designed to coordinate multiple LLMs toward reasoning, planning, and decision-making that surpasses the capabilities of any single model. The booklet includes titles, abstracts, and introductions from each main chapter, along with the full content of the first two. The newly released third edition features significant enhancements to Chapters 6 through 9 and a revised preface responding to Yann LeCun's critique of AGI feasibility. While LeCun argues that LLMs lack grounding, memory, and planning, we propose that MACI's collaborative architecture, featuring multimodal agents in executive, legislative, and judicial roles, directly addresses these limitations. Chapters on SocraSynth, EVINCE, consciousness modeling, and behavior regulation demonstrate that reasoning systems grounded in structured interaction and checks and balances can produce more reliable, interpretable, and adaptive intelligence. By integrating complementary model strengths, including world modeling and multimodal perception, MACI enables a system-level intelligence that exceeds the sum of its parts. Like human institutions, progress in AI may depend less on isolated performance and more on coordinated judgment. Collaborative LLMs, not just larger ones, may chart the path toward artificial general intelligence.
Performance of ChatGPT on USMLE: Unlocking the Potential of Large Language Models for AI-Assisted Medical Education
University of Massachusetts Boston（马萨诸塞大学波士顿分校）；Texas Tech University（德克萨斯理工大学）；Coventry University（考文垂大学）；MacNeal Hospital（麦克尼尔医院）；Washington State University（华盛顿州立大学）
本研究评估了 ChatGPT 回答复杂医学临床问题的可靠性，发现其在 USMLE 等测试中表现出较好的演绎推理能力，伦理问题得分达 60%，逻辑问题达 58.8%，证明其可作为 AI 辅助医学教育的潜力工具，但准确性仍待提升。
Unlocking Speech Instruction Data Potential with Query Rewriting
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangxi Zhuang Autonomous Region Big Data Research Institute（广西壮族自治区大数据研究院）；The Hong Kong University of Science and Technology（香港科技大学）；School of Artificial Intelligence, Shanghai Jiaotong University（上海交通大学人工智能学院）
Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intellige…
George Mason University（乔治梅森大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）
The field of artificial intelligence (AI) is witnessing a recent upsurge in research, tools development, and deployment of applications. Multiple software companies are shifting their focus to developing intelligent systems; and many others are deploying AI paradigms to their existing processes. In parallel, the academic research community is injecting AI paradigms to provide solutions to traditional engineering problems. Similarly, AI has evidently been proved useful to software engineering (SE). When one observes the SE phases (requirements, design, development, testing, release, and mainten…
Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence
Hasso Plattner Institute, University of Potsdam（哈索普兰特纳研究所，波茨坦大学）；TU Darmstadt, Secure Mobile Networking Lab（德累斯顿技术大学，安全移动网络实验室）；IMDEA Networks Institute, Madrid, Spain（IMDEA网络研究所，马德里，西班牙）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
This article discusses some trends and concepts in developing new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional and ethical intelligence. We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Using the concept of 'multiple intelligences' rather than a single type of intelligence, we categorize and provide working definitions of various AGI depending on their cognitive skills or ca…
Amirkabir University of Technology (Tehran Polytechnic)（阿米卡比尔理工大学（德黑兰理工大学））
From early days, a key and controversial question inside the artificial intelligence community was whether Artificial General Intelligence (AGI) is achievable. AGI is the ability of machines and computer programs to achieve human-level intelligence and do all tasks that a human being can. While there exist a number of systems in the literature claiming they realize AGI, several other researchers argue that it is impossible to achieve it. In this paper, we take a different view to the problem. First, we discuss that in order to realize AGI, along with building intelligent machines and programs,…
Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitations
ICB UMR 6303 CNRS（ICB UMR 6303 法国国家科学研究中心）；Université Marie et Louis Pasteur（玛丽和路易·巴斯德大学）；UTBM（贝尔福-蒙贝利亚尔技术大学）；Université Bourgogne Europe（勃艮第欧洲大学）；Institut universitaire de France (IUF)（法国大学研究院）
The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament
Ross School of Business University of Michigan（密歇根大学罗斯商学院）；NYU Stern School of Business New York University（纽约大学 Stern 商学院）；Kelley School of Business Indiana University（印第安纳大学凯利商学院）
Are Large Language Models the New Interface for Data Pipelines?
University of Trieste（的里雅斯特大学）；University of Milan（米兰大学）；University of Lübeck（吕贝克大学）；Birzeit University（比尔宰特大学）；IRIT（信息学、机器人与信息技术研究所）；University Toulouse 3（图卢兹第三大学）；Université Paris Cité（巴黎城市大学）；University of Twente（特温特大学）
University of Chicago（芝加哥大学）；Santa Fe Institute（圣塔菲研究所）
Neither artificial intelligence designed to play Turing's imitation game, nor augmented intelligence built to maximize the human manipulation of information are tuned to accelerate innovation and improve humanity's collective advance against its greatest challenges. We reconceptualize and pilot beneficial AI to radically augment human understanding by complementing rather than competing with human cognitive capacity. Our approach to complementary intelligence builds on insights underlying the wisdom of crowds, which hinges on the independence and diversity of crowd members' information and app…
Many educational technologies use artificial intelligence (AI) that presents generated or produced language to the learner. We contend that all language, including all AI communication, encodes information about the identity of the human or humans who contributed to crafting the language. With AI communication, however, the user may index identity information that does not match the source. This can lead to representational harms if language associated with one cultural group is presented as "standard" or "neutral", if the language advantages one group over another, or if the language reinforc…
Wireless Traffic Prediction with Large Language Model
Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration, Shandong University（山东大学智能通信与感知-计算整合重点实验室）；School of Information Science and Engineering, Shandong Normal University（山东师范大学信息科学与工程学院）；China Mobile Communications Group Shandong Co., Ltd（中国移动通信集团山东有限公司）；Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST)（卡布斯大学科学与工程学院）
Southern Cross University（南十字星大学）；LIFE；University of Amsterdam（阿姆斯特丹大学）；Oxford University（牛津大学）；Imperial College London（帝国理工学院）；University of London（伦敦大学）；University of Cambridge（剑桥大学）；Monash University（莫纳什大学）；Neuroelectrics；Universitat Pompeu Fabra（庞培法布拉大学）；Princeton University（普林斯顿大学）；Aily Labs
This paper is about the meaning of understanding in scientific and in artificial intelligent systems. We give a mathematical definition of the understanding, where, contrary to the common wisdom, we define the probability space on the input set, and we treat the transformation made by an intelligent actor not as a loss of information, but instead a reorganization of the information in the framework of a new coordinate system. We introduce, following the ideas of physical renormalization group, the notions of relevant and irrelevant parameters, and discuss, how the different AI tasks can be int…
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without inte…
Corporate Language Model (CLM): Transforming Tacit and Fragmented Enterprise Knowledge into a Sovereign, Auditable, and Executable Corporate Intelligence Layer
Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures perfusion of the retinal vasculature, to train an AI algorithm to generate vasculature maps from standard structural optical coherence tomography (OCT) images of the same retinae, both exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer perfusion of …
Artificial Intelligence and the Structure of Mathematics
Meta Superintelligence Labs, Fundamental AI Research（Meta超级智能实验室，基础人工智能研究）；Department of Physics and Joint Quantum Institute, University of Maryland, College Park（马里兰大学帕克分校物理系与联合量子研究所）；Center for Mathematical Sciences and Applications, Harvard University（哈佛大学数学科学与应用中心）
Berlin Institute of Health（柏林健康研究所）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）
Artificial Intelligence (AI) in healthcare holds great potential to expand access to high-quality medical care, whilst reducing overall systemic costs. Despite hitting the headlines regularly and many publications of proofs-of-concept, certified products are failing to breakthrough to the clinic. AI in healthcare is a multi-party process with deep knowledge required in multiple individual domains. The lack of understanding of the specific challenges in the domain is, therefore, the major contributor to the failure to deliver on the big promises. Thus, we present a decision perspective framewor…
Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence
Westlake Laboratory of Life Sciences and Biomedicine（西湖实验室生命科学与生物医药实验室）；Brigham Young University（杨百翰大学）；University of Antwerp（安特卫普大学）；Monash University（莫纳什大学）；European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI)（欧洲分子生物学实验室欧洲生物信息研究所）；Harvard Medical School（哈佛医学院）；Broad Institute（布罗德研究所）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；ETH Zürich（苏黎世联邦理工学院）；Bruker Ltd.（布鲁克有限公司）；Tencent（腾讯）；Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics（北京蛋白质组研究中心、国家蛋白质科学中心（北京）、北京生命组学研究所）；Thermo Fisher Scientific GmbH（赛默飞世尔科技公司）；Bristol Myers Squibb（百时美施贵宝公司）；Fudan University（复旦大学）；Indiana University（印第安纳大学）
SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
Massachusetts Institute of Technology（麻省理工学院）；Schwarzman College of Computing（计算学院）；Center for Computational Science and Engineering（计算科学与工程中心）；Laboratory for Atomistic and Molecular Mechanics (LAMM)（原子与分子力学实验室）
