Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and interdependence theory, alongside interdisciplinary methodologies. Unlike prior reviews, we examine team cognition, ethical AI, multi-modal interactions, and real-world evaluation frameworks. Key challenges include explainability, role allocation, and scalable benchmarking. We propose future research in cross-domain adaptation, trust-aware AI, and standardized testbeds. By bridging computational and social sciences, this work lays a foundation for resilient, ethical, and scalable HMT systems.

Data, as an observable form of knowledge, has become one of the most important factors of production for the development of Artificial Intelligence (AI). Meanwhile, increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although some collaborative learning paradigms such as Federated Learning (FL) can enable privacy-preserving training over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-centric, which greatly limits the way how participants cooperate with each other. In light of this, we present a knowledge-centric paradigm termed Knowledge Augmentation in Federation (KAF), with focus on how to enhance local knowledge through collaborative effort. We provide the suggested system architecture, formulate the prototypical optimization objective, and review emerging studies that employ methodologies suitable for KAF. On our roadmap, with a three-way categorization we describe the methods for knowledge expansion, knowledge filtering, and label and feature space correction in the federation. Further, we highlight several challenges and open questions that deserve more attention from the community. With our investigation, we intend to offer new insights for what collaborative learning can bring back to decentralized data.

There is a growing consensus that information is the "lifeblood of good governance" (Kolt et al., 2024) and that information sharing should be one of the "natural initial target[s]" of AI governance (Bommasani et al., 2024). Up-to-date and reliable information about AI systems' capabilities and how capabilities will develop in the future can help developers, governments, and researchers advance safety evaluations (Frontier Model Forum, 2024), develop best practices (UK DSIT, 2023), and respond effectively to the new risks posed by frontier AI (Kolt et al., 2024). Information sharing also supports regulatory visibility (Anderljung et al., 2023) and can thus enable better-informed AI governance (O'Brien et al., 2024). Further, access to knowledge about AI systems' potential risks allows AI systems claims to be scrutinized more effectively (Brundage et al., 2020). By contrast, information asymmetries could lead regulators to miscalibrated over-regulation--or under-regulation--of AI (Ball & Kokotajlo, 2024) and could contribute to the "pacing problem," a situation in which government oversight consistently lags behind technology development (Marchant et al., 2011). In short, there is a strong case for information sharing being one "key to making AI go well" (Ball & Kokotajlo, 2024). The Frontier AI Safety Commitments ("FAISC") are an important step towards more comprehensive information sharing by AI developers.

This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co-design approaches, emphasizing usability, manageability, affordability, adaptability, efficiency, and scalability. By integrating cutting-edge technologies such as generative and agentic AI, cross-layer automation and optimization, unified control plane, and composable and adaptive system architecture, the proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness. Incorporating quantum computing as it matures will enable quantum-accelerated simulations for materials science, climate modeling, and other high-impact domains. Collaborative efforts between academia and industry are central to this vision, driving advancements in foundation models for material design and climate solutions, scalable multimodal data processing, and enhanced physics-based AI emulators for applications like weather forecasting and carbon sequestration. Research priorities include advancing AI agentic systems, LLM as an Abstraction (LLMaaA), AI model optimization and unified abstractions across heterogeneous infrastructure, end-to-end edge-cloud transformation, efficient programming model, middleware and platform, secure infrastructure, application-adaptive cloud systems, and new quantum-classical collaborative workflows. These ideas and solutions encompass both theoretical and practical research questions, requiring coordinated input and support from the research community. This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms, fostering breakthroughs in AI-driven applications and scientific discovery across academia, industry, and society.

Generative artificial intelligence has shown promise in prompting virtual worlds into existence, yet little attention has been given to understanding how this process unfolds as social interaction. We present Social Conjurer, a framework for AI-augmented dynamic 3D scene co-creation, where multiple users collaboratively build and modify virtual worlds in real-time. Through an expanded set of interactions, including social and tool-based engagements as well as spatial reasoning, our framework facilitates the creation of rich, diverse virtual environments. Findings from a preliminary user study (N=12) provide insight into the user experience of this approach, how social contexts shape the prompting of spatial environments, and perspective on social applications of prompt-based 3D co-creation. In addition to highlighting the potential of AI-supported multi-user world creation and offering new pathways for AI-augmented creative processes in VR, this article presents a set of implications for designing human-centered interfaces that incorporate AI models into 3D content generation.

In the rapidly evolving landscape of artificial intelligence (AI), the collaboration between human intelligence and AI systems, known as Human-AI (HAI) Teaming, has emerged as a cornerstone for advancing problem-solving and decision-making processes. The advent of Large Pre-trained Models (LPtM) has significantly transformed this landscape, offering unprecedented capabilities by leveraging vast amounts of data to understand and predict complex patterns. This paper surveys the pivotal integration of LPtMs with HAI, emphasizing how these models enhance collaborative intelligence beyond traditional approaches. It examines the potential of LPtMs in augmenting human capabilities, discussing this collaboration for AI model improvements, effective teaming, ethical considerations, and their broad applied implications in various sectors. Through this exploration, the study sheds light on the transformative impact of LPtM-enhanced HAI Teaming, providing insights for future research, policy development, and strategic implementations aimed at harnessing the full potential of this collaboration for research and societal benefit.

In this paper, we present a comprehensive survey of the metaverse, envisioned as a transformative dimension of next-generation Internet technologies. This study not only outlines the structural components of our survey but also makes a substantial scientific contribution by elucidating the foundational concepts underlying the emergence of the metaverse. We analyze its architecture by defining key characteristics and requirements, thereby illuminating the nascent reality set to revolutionize digital interactions. Our analysis emphasizes the importance of collaborative efforts in developing metaverse standards, thereby fostering a unified understanding among industry stakeholders, organizations, and regulatory bodies. We extend our scrutiny to critical technologies integral to the metaverse, including interactive experiences, communication technologies, ubiquitous computing, digital twins, artificial intelligence, and cybersecurity measures. For each technological domain, we rigorously assess current contributions, principal techniques, and representative use cases, providing a nuanced perspective on their potential impacts. Furthermore, we delve into the metaverse's diverse applications across education, healthcare, business, social interactions, industrial sectors, defense, and mission-critical operations, highlighting its extensive utility. Each application is thoroughly analyzed, demonstrating its value and addressing associated challenges. The survey concludes with an overview of persistent challenges and future directions, offering insights into essential considerations and strategies necessary to harness the full potential of the metaverse. Through this detailed investigation, our goal is to articulate the scientific contributions of this survey paper, transcending a mere structural overview to highlight the transformative implications of the metaverse.

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficacy. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research.

With the rapid advancement of technology, Augmented Reality (AR) technology, known for its ability to deeply integrate virtual information with the real world, is gradually transforming traditional work modes and teaching methods. Particularly in the realms of remote work and online education, AR technology demonstrates a broad spectrum of application prospects. This paper delves into the application potential and actual effects of AR technology in remote work and education. Through a systematic literature review, this study outlines the key features, advantages, and challenges of AR technology. Based on theoretical analysis, it discusses the scientific basis and technical support that AR technology provides for enhancing remote work efficiency and promoting innovation in educational teaching models. Additionally, by designing an empirical research plan and analyzing experimental data, this article reveals the specific performance and influencing factors of AR technology in practical applications. Finally, based on the results of the experiments, this research summarizes the application value of AR technology in remote work and education, looks forward to its future development trends, and proposes forward-looking research directions and strategic suggestions, offering empirical foundation and theoretical guidance for further promoting the in-depth application of AR technology in related fields.

This study addresses the challenge of forming effective groups in collaborative problem-solving environments. Recognizing the complexity of human interactions and the necessity for efficient collaboration, we propose a novel approach leveraging graph theory and reinforcement learning. Our methodology involves constructing a graph from a dataset where nodes represent participants, and edges signify the interactions between them. We conceptualize each participant as an agent within a reinforcement learning framework, aiming to learn an optimal graph structure that reflects effective group dynamics. Clustering techniques are employed to delineate clear group structures based on the learned graph. Our approach provides theoretical solutions based on evaluation metrics and graph measurements, offering insights into potential improvements in group effectiveness and reductions in conflict incidences. This research contributes to the fields of collaborative work and educational psychology by presenting a data-driven, analytical approach to group formation. It has practical implications for organizational team building, classroom settings, and any collaborative scenario where group dynamics are crucial. The study opens new avenues for exploring the application of graph theory and reinforcement learning in social and behavioral sciences, highlighting the potential for empirical validation in future work.