Microsoft has good news for anyone with corner office ambitions. In the future we're all going to be bosses – of AI employees. The tech company is predicting the rise of a new kind of business, called a "frontier firm", where ultimately a human worker directs autonomous artificial intelligence agents to carry out tasks. Everyone, according to Microsoft, will become an agent boss. "As agents increasingly join the workforce, we'll see the rise of the agent boss: someone who builds, delegates to and manages agents to amplify their impact and take control of their career in the age of AI," wrote Jared Spataro, a Microsoft executive, in a blogpost this week.

The rapid electrification of transportation, driven by stringent decarbonization targets and supportive policies, poses significant challenges for distribution system operators (DSOs). When numerous electric vehicles (EVs) charge concurrently, local transformers risk overloading - a problem that current tariff-based strategies do not adequately address. This paper introduces an aggregator-based coordination mechanism that shifts EV charging from congested to underutilized periods using a rule-based scheduling algorithm. Unlike conventional methods that depend on complex real-time pricing signals or optimization-heavy solutions, the aggregator approach uses a simple yet effective "laxity" measure to prioritize charging flexibility. To assess technical and economic viability, a multi-agent simulation was developed to replicate residential user behavior and DSO constraints under the use of a 400 kVA low-voltage transformer. The results indicate that overloads are completely eliminated with minimal inconvenience to users, whose increased charging costs are offset by the aggregator at an annual total of under DKK 6000 - significantly lower than the cost of infrastructure reinforcement. This study contributes by (i) quantifying the compensation needed to prevent large-scale overloads, (ii) presenting a replicable, computationally feasible, rule-based aggregator model for DSOs, and (iii) comparing aggregator solutions to costly transformer upgrades, underscoring the aggregator's role as a viable tool for future distribution systems.

Abstract--T o jointly tackle the challenges of data and node heterogeneity in decentralized learning, we propose a dist ributed strong lottery ticket hypothesis (DSL TH), based on which a communication-efficient personalized learning algorithm is developed. In the proposed method, each local model is represente d as the Hadamard product of global real-valued parameters and a personalized binary mask for pruning. The local model is lea rned by updating and fusing the personalized binary masks while the real-valued parameters are fixed among different agents . T o further reduce the complexity of hardware implementatio n, we incorporate a group sparse regularization term in the los s function, enabling the learned local model to achieve struc - tured sparsity. Then, a binary mask aggregation algorithm i s designed by introducing an intermediate aggregation tenso r and adding a personalized fine-tuning step in each iteration, wh ich constrains model updates towards the local data distributi on. The proposed method effectively leverages the relativity a mong agents while meeting personalized requirements in heterog eneous node conditions. We also provide a theoretical proof for the DSL TH, establishing it as the foundation of the proposed met hod. Numerical simulations confirm the validity of the DSL TH and demonstrate the effectiveness of the proposed algorithm. Index T erms--Distributed learning, personalized learning, data and node heterogeneity, communication efficiency. As one of the most promising applications in 6G era, Artificial Intelligence of Things (AIoT) combines the artifi cial intelligence technologies with the Internet of Things (IoT) infrastructure, resembling the transformation from "conn ected things" to "connected intelligence" . This work was supported in part by National Natural Science F oundation of China under Grants 62394292 and U20A20158, Ministry of In dustry and Information Technology under Grant TC220H07E, Zhejiang Pr ovincial Key R&D Program under Grant 2023C01021, the Fundamental Resear ch Funds for the Central Universities No. 226-2024-00069, and the EU-SN S 6G CENTRIC Project. Z. Tian (email: dankotian@zju.edu.cn) was with the College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China and now is with the Center for Wireless Communications, University of Oulu, Oulu 90014, Finland. Z. Zhang (Corresponding Author, email: ning ming@zju.edu.cn) is with the College of Information Science and Electronic Engineer ing, Zhejiang University, Hangzhou 310027, China, and with the State Key L aboratory of Industrial Control Technology, Hangzhou 310027, China, and also with Zhejiang Provincial Key Laboratory of Multimodal Communic ation Networks and Intelligent Information Processing, Hangzhou 310027, China.

Rapid progress in intelligent unmanned systems has presented new opportunities for mobile crowd sensing (MCS). Today, heterogeneous air-ground collaborative multi-agent framework, which comprise unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), have presented superior flexibility and efficiency compared to traditional homogeneous frameworks in complex sensing tasks. Within this context, task allocation among different agents always play an important role in improving overall MCS quality. In order to better allocate tasks among heterogeneous collaborative agents, in this paper, we investigated two representative complex multi-agent task allocation scenarios with dual optimization objectives: (1) For AG-FAMT (Air-Ground Few Agents More Tasks) scenario, the objectives are to maximize the task completion while minimizing the total travel distance; (2) For AG-MAFT (Air-Ground More Agents Few Tasks) scenario, where the agents are allocated based on their locations, has the optimization objectives of minimizing the total travel distance while reducing travel time cost. To achieve this, we proposed a Multi-Task Minimum Cost Maximum Flow (MT-MCMF) optimization algorithm tailored for AG-FAMT, along with a multi-objective optimization algorithm called W-ILP designed for AG-MAFT, with a particular focus on optimizing the charging path planning of UAVs. Our experiments based on a large-scale real-world dataset demonstrated that the proposed two algorithms both outperform baseline approaches under varying experimental settings, including task quantity, task difficulty, and task distribution, providing a novel way to improve the overall quality of mobile crowdsensing tasks.

In social learning, a network of agents assigns probability scores (beliefs) to some hypotheses of interest, which rule the generation of local streaming data observed by each agent. Belief formation takes place by means of an iterative two-step procedure where: i) the agents update locally their beliefs by using some likelihood model; and ii) the updated beliefs are combined with the beliefs of the neighboring agents, using a pooling rule. This procedure can fail to perform well in the presence of dynamic drifts, leading the agents to incorrect decision making. Here, we focus on the fully online setting where both the true hypothesis and the likelihood models can change over time. This goal is achieved by exploiting two adaptation stages: i) a stochastic gradient descent update to learn and track the drifts in the decision model; ii) and an adaptive belief update to track the true hypothesis changing over time. These stages are controlled by two adaptation parameters that govern the evolution of the error probability for each agent. We show that all agents learn consistently for sufficiently small adaptation parameters, in the sense that they ultimately place all their belief mass on the true hypothesis. Index T erms Social learning, belief formation, decision making, distributed optimization, online leaerning, opinion diffusion over graphs. Marco Carpentiero and Vincenzo Matta are with the Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, via Giovanni Paolo II, I-84084, Fisciano (SA), Italy, and Vincenzo Matta is also with the National Inter-University Consortium for Telecommunications (CNIT), Italy (e-mails: { mcarpentiero, vmatta }@unisa.it). Matta was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on "Telecommunications of the Future" (PE00000001 - program "REST ART"). This work was produced while Virginia Bordignon was a post-doc with the Ecole Polytechnique F ed erale de Lausanne EPFL, School of Engineering, CH-1015 Lausanne, Switzerland (e-mail: virginia.bordignon@alumni.epfl.ch).

--Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream machine learning tasks. Existing frameworks, though designed to mitigate manual costs, often treat feature transformations as isolated operations, ignoring dynamic dependencies between transformation steps. T o address the limitations, we propose TCTO, a collaborative multi-agent reinforcement learning framework that automates feature engineering through graph-driven path optimization. The framework's core innovation lies in an evolving interaction graph that models features as nodes and transformations as edges. Through graph pruning and backtracking, it dynamically eliminates low-impact edges, reduces redundant operation, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. T o demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets. LASSICAL machine learning (ML) heavily relies on the structure of the model and the quality of the involving features [1]-[4]. This dependency makes designing effective features a crucial step before the learning process. Traditionally, designing effective features required extensive manual intervention, where scientists applied mathematical transformations to raw data to create meaningful ones [5], [6]. This process, illustrated in Figure 1, is known as feature transformation [7]-[9]. Xiaohan Huang, Zhiyuan Ning and Qingqing Long are with the Computer Network Information Center, Chinese Academy of Sciences, and the University of the Chinese Academy of Sciences. Yi Du, Y uanchun Zhou, and Meng Xiao are with the Computer Network Information Center, Chinese Academy of Sciences. Dongjie Wang is with the Department of Electrical Engineering and Computer Science at the University of Kansas. Ziyue Qiao is with the School of Computing and Information Technology, Great Bay University, Dongguan, China.

Large language models (LLMs) are a transformational capability at the frontier of artificial intelligence and machine learning that can support decision-makers in addressing pressing societal challenges such as extreme natural hazard events. As generalized models, LLMs often struggle to provide context-specific information, particularly in areas requiring specialized knowledge. In this work we propose a retrieval-augmented generation (RAG)-based multi-agent LLM system to support analysis and decision-making in the context of natural hazards and extreme weather events. As a proof of concept, we present WildfireGPT, a specialized system focused on wildfire hazards. The architecture employs a user-centered, multi-agent design to deliver tailored risk insights across diverse stakeholder groups. By integrating natural hazard and extreme weather projection data, observational datasets, and scientific literature through an RAG framework, the system ensures both the accuracy and contextual relevance of the information it provides. Evaluation across ten expert-led case studies demonstrates that WildfireGPT significantly outperforms existing LLM-based solutions for decision support.

Stochastic Optimal Control (SOC) problems arise in systems influenced by uncertainty, such as autonomous robots or financial models. Traditional methods like dynamic programming are often intractable for high-dimensional, nonlinear systems due to the curse of dimensionality. This dissertation explores the path integral control framework as a scalable, sampling-based alternative. By reformulating SOC problems as expectations over stochastic trajectories, it enables efficient policy synthesis via Monte Carlo sampling and supports real-time implementation through GPU parallelization. We apply this framework to six classes of SOC problems: Chance-Constrained SOC, Stochastic Differential Games, Deceptive Control, Task Hierarchical Control, Risk Mitigation of Stealthy Attacks, and Discrete-Time LQR. A sample complexity analysis for the discrete-time case is also provided. These contributions establish a foundation for simulator-driven autonomy in complex, uncertain environments.

In this paper, we address the problem of a two-player linear quadratic differential game with incomplete information, a scenario commonly encountered in multi-agent control, human-robot interaction (HRI), and approximation methods for solving general-sum differential games. While solutions to such linear differential games are typically obtained through coupled Riccati equations, the complexity increases when agents have incomplete information, particularly when neither is aware of the other's cost function. To tackle this challenge, we propose a model-based Peer-A ware Cost Estimation (P ACE) framework for learning the cost parameters of the other agent. In P ACE, each agent treats its peer as a learning agent rather than a stationary optimal agent, models their learning dynamics, and leverages this dynamic to infer the cost function parameters of the other agent. This approach enables agents to infer each other's objective function in real time based solely on their previous state observations and dynamically adapt their control policies. Furthermore, we provide a theoretical guarantee for the convergence of parameter estimation and the stability of system states in P ACE. Additionally, in our numerical studies, we demonstrate how modeling the learning dynamics of the other agent benefits P ACE, compared to approaches that approximate the other agent as having complete information, particularly in terms of stability and convergence speed.

Eufy is teeing up a pair of networked security video recorder and camera kits that will--eventually--use AI agents to warn you of possible threats in real time. Both systems offer connections for PoE (Power over Ethernet) cameras, with the ProSecure NVR arriving in various packages that combine 4K bullet Pan/tilt/zoom and turret-style cameras with color night vision, cross-camera tracking (a Eufy feature that stiches together video events from different vantage points), and up to 8X digital zoom. The ProSecure NVR supports a total of 16 connected cameras. The ProSecure NVR arrives in various packages that combine bullet PTZ and turret-style cameras. In addition to PoE cams, the Zigbee-enabled HomeBase Pro can connect to everything from Eufy keypads and smoke detectors to video doorbells and water leak sensors.