Round-Robin Behavior Cloning (R2BC): Traditional Behavior Cloning (left) requires coordinated and centralized demonstrations, where an expert demonstrates actions near-optimally for all agents. In multi-agent domains, a lone human operator may not be able to provide high-quality demonstrations due to underactuated control and increased cognitive burden. Our method (right), R2BC, removes this restriction by letting the human control one agent at a time while the other agents act via their learned policies. This round-robin process collects realistic demonstrations and iteratively trains cooperative multi-agent behavior. Abstract-- Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high-quality demonstrations are easy to obtain. While IL has been widely applied to single-robot settings, relatively few studies have addressed the extension of these methods to multi-agent systems, especially in settings where a single human must provide demonstrations to a team of collaborating robots. In this paper, we introduce and study Round-Robin Behavior Cloning (R2BC), a method that enables a single human operator to effectively train multi-robot systems through sequential, single-agent demonstrations. Our approach allows the human to teleoperate one agent at a time and incrementally teach multi-agent behavior to the entire system, without requiring demonstrations in the joint multi-agent action space.

The integration of Unmanned Aerial Vehicles (UAVs) into Open Radio Access Networks (O-RAN) enhances communication in disaster management and Search and Rescue (SAR) operations by ensuring connectivity when infrastructure fails. However, SAR scenarios demand stringent security and low-latency communication, as delays or breaches can compromise mission success. While UAVs serve as mobile relays, they introduce challenges in energy consumption and resource management, necessitating intelligent allocation strategies. Existing UAV-assisted O-RAN approaches often overlook the joint optimization of security, latency, and energy efficiency in dynamic environments. This paper proposes a novel Reinforcement Learning (RL)-based framework for dynamic resource allocation in UAV relays, explicitly addressing these trade-offs. Our approach formulates an optimization problem that integrates security-aware resource allocation, latency minimization, and energy efficiency, which is solved using RL. Unlike heuristic or static methods, our framework adapts in real-time to network dynamics, ensuring robust communication. Simulations demonstrate superior performance compared to heuristic baselines, achieving enhanced security and energy efficiency while maintaining ultra-low latency in SAR scenarios.

Knowledge editing techniques promise to implant new factual knowledge into large language models (LLMs). But do LLMs really believe these facts? We develop a framework to measure belief depth and use it to evaluate the success of knowledge editing techniques. We operationalize belief depth as the extent to which implanted knowledge 1) generalizes to related contexts (e.g. Fermi estimates several logical steps removed), 2) is robust to self-scrutiny and direct challenge, and 3) is represented similarly to genuine knowledge (as measured by linear probes). Our evaluations show that simple prompting and mechanistic editing techniques fail to implant knowledge deeply. In contrast, Synthetic Document Finetuning (SDF) - where models are trained on LLM-generated documents consistent with a fact - often succeeds at implanting beliefs that behave similarly to genuine knowledge. However, SDF's success is not universal, as implanted beliefs that contradict basic world knowledge are brittle and representationally distinct from genuine knowledge. Overall, our work introduces measurable criteria for belief depth and enables the rigorous evaluation necessary for deploying knowledge editing in real-world applications.

Abstract--Integrated Satellite-Aerial-Terrestrial Networks (ISATNs) are envisioned as key enablers of 6G, providing global connectivity for applications such as autonomous transportation, Industrial IoT, and disaster response. ISATN-specific control knobs, including carbon-aware handovers, UA V duty-cycling, and renewable-aware edge placement, are exploited to reduce emissions. I. Introduction The rapid evolution of next-generation communication systems is driving the integration of Satellite, Aerial, and Terrestrial Networks (ISATNs) into a unified infrastructure capable of delivering seamless global connectivity. This convergence is critical for enabling emerging applications such as autonomous transportation, Industrial Internet of Things (IIoT), remote healthcare, and disaster response, where reliable, low-latency, and high-capacity communication is essential [ 1 ]. However, the energy consumption associated with operating dense terrestrial base stations, satellite constellations, and aerial platforms introduces significant carbon emissions, posing new challenges for designing energy-efficient and environmentally sustainable integrated networks. As communication networks scale toward 6G and beyond, addressing carbon emissions and energy optimization has become a priority. The increasing reliance on renewable energy sources and fluctuating carbon intensity in power grids demand intelligent orchestration mechanisms capable of balancing Quality of Service (QoS) with environmental impact. S. Javaid is with the College of Electronics and Information Engineering, Tongji University, Shanghai 201804, and the State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai 201210, China N. Saeed is with the Department of Electrical and Communication Engineering, College of Engineering, UAE University, Al-Ain 15551, UAE (e-mail: mr.nasir.saeed@ieee.org).

Visual navigation is a fundamental problem in embodied AI, yet practical deployments demand long-horizon planning capabilities to address multi-objective tasks. A major bottleneck is data scarcity: policies learned from limited data often overfit and fail to generalize OOD. Existing neural network-based agents typically increase architectural complexity that paradoxically become counterproductive in the small-sample regime. This paper introduce NeuRO, a integrated learning-to-optimize framework that tightly couples perception networks with downstream task-level robust optimization. Specifically, NeuRO addresses core difficulties in this integration: (i) it transforms noisy visual predictions under data scarcity into convex uncertainty sets using Partially Input Convex Neural Networks (PICNNs) with conformal calibration, which directly parameterize the optimization constraints; and (ii) it reformulates planning under partial observability as a robust optimization problem, enabling uncertainty-aware policies that transfer across environments. Extensive experiments on both unordered and sequential multi-object navigation tasks demonstrate that NeuRO establishes SoTA performance, particularly in generalization to unseen environments. Our work thus presents a significant advancement for developing robust, generalizable autonomous agents.

Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning, necessitating advanced computational analyses to ensure reliability and resilience. These analyses depend on disparate workflows comprising complex models, function calls, and data pipelines that require substantial expert knowledge and remain difficult to automate. Workforce and budget constraints further limit utilities' ability to apply such analyses at scale. To address this gap, we build an agentic system PowerChain, which is capable of autonomously performing complex grid analyses. Existing agentic AI systems are typically developed in a bottom-up manner with customized context for predefined analysis tasks; therefore, they do not generalize to tasks that the agent has never seen. In comparison, to generalize to unseen DG analysis tasks, PowerChain dynamically generates structured context by leveraging supervisory signals from self-contained power systems tools (e.g., GridLAB-D) and an optimized set of expert-annotated and verified reasoning trajectories. For complex DG tasks defined in natural language, empirical results on real utility data demonstrate that PowerChain achieves up to a 144/% improvement in performance over baselines.

Accurate annual average daily traffic (AADT) data are vital for transport planning and infrastructure management. However, automatic traffic detectors across national road networks often provide incomplete coverage, leading to underrepresentation of minor roads. While recent machine learning advances have improved AADT estimation at unmeasured locations, most models produce only point predictions and overlook estimation uncertainty. This study addresses that gap by introducing an interval prediction approach that explicitly quantifies predictive uncertainty. We integrate a Quantile Random Forest model with Principal Component Analysis to generate AADT prediction intervals, providing plausible traffic ranges bounded by estimated minima and maxima. Using data from over 2,000 minor roads in England and Wales, and evaluated with specialized interval metrics, the proposed method achieves an interval coverage probability of 88.22%, a normalized average width of 0.23, and a Winkler Score of 7,468.47. By combining machine learning with spatial and high-dimensional analysis, this framework enhances both the accuracy and interpretability of AADT estimation, supporting more robust and informed transport planning.

Encouraged early on by Nobel laureate Enrico Fermi, the "Queen of Carbon" laid the foundation for countless advances in nanotechnology--and mentored countless young scientists along the way. At MIT, Mildred Dresselhaus became a beloved professor who pushed her students to be their very best and provided support in ways big and small. Institute Professor Mildred "Millie" Dresselhaus forever altered our understanding of matter--the physical stuff of the universe that has mass and takes up space. Over 57 years at MIT, Dresselhaus also played a significant role in inspiring people to use this new knowledge to tackle some of the world's greatest challenges, from producing clean energy to curing cancer. Although she became an emerita professor in 2007, Dresselhaus, who taught electrical engineering and physics, remained actively involved in research and all other aspects of MIT life until her death in 2017. She would have been 95 this November.

The Ukrainian city of Chernihiv is in total blackout following what the authorities describe as a massive assault by Russian missiles and drones, with hundreds of thousands of people affected. Across the wider Chernihiv region, four people are reported to have been killed as residential neighbourhoods were struck in the town of Novhorod-Siverskyi. Ten others were injured, including a 10-year-old girl. The country's most northerly region is the latest to be hit in an intensifying series of attacks on civilian infrastructure as Russia targets energy supplies, the rail network, homes and businesses in its full-scale invasion of Ukraine. I personally heard the drones flying overhead, 55-year-old Oleksandr Babich said.

How successful is Ukraine's'gas war' against Russia? How will Putin travel to Hungary with an ICC arrest warrant? How much of Europe's oil still comes from Russia? How Russia's new tactics pose new winter threat to Ukraine The Russian drone strike was surgically precise and destroyed a giant transformer at a key power station in the Ukrainian capital. "There's nothing left to repair," Mykola Svyrydenko, who lives close to Thermal Station 5, a sprawling, Soviet-era structure with two giant steam pipes that provides electricity and heat to hundreds of thousands of Kyiv's residents, told Al Jazeera.