Environment

Autonomous UAV systems increasingly rely on multimodal large language models (MLLMs) to operate in complex real-world environments. Such embodied scenarios require not only understanding the surrounding space but also maintaining a coherent representation of the agent itself. However, existing UAV-oriented approaches and benchmarks remain largely environment-centric, primarily focusing on spatial understanding tasks, with the agent's self-awareness remaining implicit. To address this gap, we int

Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next action. Most agent systems run on desktops or servers, which support tool use and task automation. Mobile devices are also important agent environments because they are widely accessible and contain users' data, sensors, and daily-use applications. Existing mobile agents mainly operate smartphones through graphical user

Thermal infrared (TIR) imaging is essential for UAV swarm operations in visually degraded environments. However, tracking tiny UAVs remains challenging due to limited appearance cues, frequent occlusions, and rapid maneuvers. Despite significant progress driven by benchmarks such as the Anti-UAV challenge, existing methods primarily prioritize accuracy while overlooking the computational constraints of real-time edge deployment. The standard Kalman Filter (KF) offers the efficiency required for

Meta said the planned Hyperion data center supercluster in Richland Parish, Louisiana, will be a 5GW facility and cost more than $50 billion.

wealthy companies putting billions of dollars into data centers can afford to build renewable energy sources to power them.

“SceneSmith” system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores.

Responding to Musk accusing him of being a scammer, Altman said, "homeboy you're the one sellling [sic] public market investors on short-term space datacenters."

This paper presents a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion framework for action and activity recognition in healthcare-oriented training environments. The proposed architecture combines parameter-efficient modality-specific adaptation with sequential fusion, enabling modalities to be integrated in stages without retraining previously learned components. Rather than assuming a fixed fusion structure, the framework first integrates more closely related modalities and then in

Richard Sutton, 2024 Turing Award winner and co-founder of modern reinforcement learning, has launched a new startup called Oak Lab in Toronto. He calls current deep learning methods "weak and inefficient" and wants to build AI agents that learn continuously from their environment. The article Turing Award winner Rich Sutton founds Oak Lab to build AI agents that learn on their own appeared first on The Decoder .

We introduce MM-ToolSandBox, a benchmark and evaluation framework for visually grounded tool-calling agents. The framework provides a stateful execution environment spanning 500+ tools across 16 application domains, supporting multi-image, multi-turn tasks where agents must ground progressively arriving visual inputs into executable tool calls while handling realistic conversational phenomena (goal revisions, error corrections, state mutations). An automated scenario generation pipeline produces

Meta will expand its largest data center to 5 gigawatts of compute capacity as investment in the project hits more than $50 billion, the company announced Monday. The Hyperion data center in Richland Parish, La., was announced in October and was originally projected to cost more than $27 billion as part of Meta's joint venture...

Soil plasticity and water content drive elevated soil cracking risk across ~60% of China’s land during summer peaks, with increasing risk under future warming, suggests an analysis of 4,000 laboratory ...

Climate change is a source of anxiety about the future. Understanding how people express themselves about climate change enables us to address such concerns. To study climate change expression on social media, we analyzed 200 TikTok videos tagged with #climatechange, identifying four categories of content: expression-feelings, views-appeals, news-information, and trend-hijacking. We found that creators use humor to package sharp critiques, avoiding direct confrontation. They replace complex disc

The EU, its members and the U.K. took action against Russian government officials and others while attributing the winter cyberattacks against Poland’s energy grid to the FSB. The post Europe strikes out against Russia’s Turla over espionage, ‘destructive attacks’ appeared first on CyberScoop .

The Anomalous Health Incidents cross-functional team has been renamed as officials focus on "non-kinetic threats." The post Pentagon disburses Havana Syndrome compensation, rebrands team focused on ‘Directed Energy Bio-Effects’ appeared first on DefenseScoop .

State-sponsored attackers are targeting critical infrastructure networks in defense, communications, energy, finance, government and health care. The post Officials once again warn defenders that Russian hackers are targeting network devices appeared first on CyberScoop .

Labor sources say the PM will discuss safety concerns in speech this week but will not provide an update on copyright reforms to protect artists Follow our Australia news live blog for latest updates Get our breaking news email , free app or daily news podcast Anthony Albanese will describe the progress of AI as an inflection point for society on par with the renewable energy transition, but is not expected to detail progress on copyright reforms to protect creative industries. The prime ministe

As robots become increasingly integrated into human environments, their ability to detect and respond to errors remains critical for maintaining user trust and interaction quality. While recent advances in machine learning have improved error detection capabilities, most approaches are limited to specific contexts, controlled settings, or pre-extracted features, limiting their generalizability and applicability to real-world conditions. To address this challenge, the third edition of the ERR@HRI

This paper presents the mAIEnergy dataset, an open-access, multimodal corpus developed to support Large Language Model (LLM) applications in the energy sector. The dataset integrates approximately 50,000 textual documents, 20,000 images, 25 million numerical time series records, and 2 million geospatial and relational data entries. It includes policy and regulatory texts, scientific articles and news articles, satellite and contextual imagery, electricity system measurements, weather observation

Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical. Yet most deep learning models yield only deterministic predictions, providing no indication of per-pixel reliability. These regression tasks are inherently challenging due to heterogeneous land surfaces, skewed target distributions, sensor noise, and

The growing ability of large language models and vision language models to jointly interpret and reason over images and text is reshaping medical agents, moving them from task specific predictors toward autonomous systems that perceive, reason, plan, remember, and act in clinical environments. This work departs from the capability first perspective of existing literature and instead begins from clinical deployment, asking what tasks, contamination resistant benchmarks, and interactive training e

Agentic research systems are emerging as a new paradigm for coordinating scientific workflows beyond isolated model inference, code generation, or statistical analysis. However, deployment in institutional biomedical environments requires governed mechanisms for research planning, data access, workflow orchestration, evidence tracking, reproducibility, and human oversight. We present NVAITC AI Scientist (NAIS), a governed end-to-end agentic research system designed to support domain-general scie

Enterprise data analysis is emerging as a distinct frontier for autonomous agents. Compared with general-purpose interaction and software engineering, it operates in an open, ambiguous, and continuously evolving environment. These characteristics call for a data-agent architecture that treats semantics, methodology, execution, and evolution as first-class system concerns. To this end, we introduce QwenPaw-Data, an agentic data system designed for enterprise intelligent data analysis. QwenPaw-Dat

A group of astronomers has detected a sugar molecule swirling inside a cloud of gas and dust near the centre of our galaxy. They are calling the molecule — a compound with four carbon atoms called ...

Image — The ‘Climate Changed Oak Tree’ in Kew Gardens, London, on 22 June 2026 at the start of London Climate Action Week. Photo by Brook Mitchell / AFP via Getty Images. As much of Europe emerged ...

Indoor spatial understanding remains a fundamental challenge for intelligent systems operating in physical environments. Traditional RFID localization techniques typically estimate positions of tags using signal strength measurements but fail to capture higher-order spatial relationships between objects and infrastructure. Recent work on RFID and wireless indoor localization has increasingly emphasized robust learning under noisy propagation, while recent graph-based localization methods demonst

French tech leaders and politicians don't want the country's strategic supply of electricity to end up lining the pockets of American AI giants.

Autonomous driving requires long-horizon closedloop decision making in dynamic traffic environments. Latent world models offer an effective framework for this problem by enabling imagination-based decision making in compact latent spaces. However, multi-source observations contain controlirrelevant redundancy, whereas reliable driving decisions rely on risk-relevant relations, future dynamics, and continuous action adjustments. This mismatch makes observation reconstruction and absolute action m

Political backlash to data centers is putting gubernatorial candidates in the hot seat as the presence of the massive AI infrastructure becomes a flash point in races up and down the ballot. Incumbent governors and hopeful challengers are forced to wrestle with Americans’ growing concerns around artificial intelligence, along with fears about energy prices and land...

AI engineering is shifting from passive text generation by large language models (LLMs) to agent-driven task execution, creating new reliability challenges for long-horizon tasks under resource constraints and environmental uncertainty. Conventional error-elimination optimization strategies fail to address cumulative error propagation. This paper proposes Distributed Agent System (DAS), a device-edge-cloud framework for fault-tolerant collaboration among heterogeneous agents. We redefine agent r

Editor’s note, July 12, 8 am ET: We’re bringing you some of our best-loved Your Mileage May Vary columns while Sigal Samuel is on parental leave. The one below was originally published in January 2025. This unconventional advice column offers you a unique framework for thinking through moral dilemmas. It’s based on value pluralism: the idea that each […]

The accelerating shift toward low-carbon power systems, together with the widespread adoption of behind-the-meter technologies such as rooftop solar and electric vehicles, is placing new operational and analytical demands on electricity grids. At the same time, smart-grid research increasingly relies on machine learning (ML), yet progress is constrained by limited access to high-resolution household energy data due to privacy concerns, regulatory barriers, and collection costs. This work present

Efficient waste segregation is critical for sustainable urban management and environmental governance. Existing automated systems are limited by single-modality visual processing, insufficient contextual understanding, and weak regulatory alignment. To address these issues, we propose a language-guided vision-AI framework that integrates vision-language models and multimodal large language models for joint visual-linguistic reasoning. This framework implements a visual question answering paradig

Anthony Albanese will deliver a landmark speech on AI this week as MPs are torn between attracting datacentre investment and protecting the rights of creatives Follow our Australia news live blog for latest updates When Anna Funder stood before a pack of journalists at Parliament House this month, she presented herself not just as a writer but also a “victim of crime”. The Stasiland author was using the analogy to illustrate how technology companies have flagrantly “hoovered up” her literary wor

From local communities to the global stage, a diverse group of young leaders from Thailand is helping shape conversations on public policy, climate action, inclusion, indigenous rights, disability ...

Safe and socially compliant navigation remains a fundamental challenge for autonomous robots operating in human-populated environments. Beyond collision avoidance, robots must anticipate human motion and respect personal space to ensure human comfort. Model Predictive Control (MPC) offers a robust alternative to classical and data-driven methods, although its effectiveness strongly depends on accurate human motion prediction and efficient computation. This paper introduces SFM-NMPC, a Social For

Microsoft, Amazon and Google say they still aim to achieve net zero output despite construction boom Microsoft, Amazon and Google’s collective carbon emissions have increased by nearly a fifth in the past year, driven largely by datacentre construction. In the financial year ending March 2026, the three tech companies emitted 119m mTCO₂e (metric tonnes of carbon dioxide equivalent), or about a third of those of France. Continue reading...

Equitable renewable-energy planning is a sequential decision problem, but the decision variables available to a public planner differ sharply between mature and emerging economies. In the former the government largely builds generation, while in the latter it steers private investment through incentives and quotas. We formulate socially-equitable renewable-energy budget allocation as a Markov Decision Process (MDP) and, using a single problem-agnostic solver interface, compare the same policies

In a crowded field of AI contenders, the agency’s office of the CIO prioritizes partnerships with vendors that employees are favoring, such as OpenAI and Anthropic. The post Energy IT shop not interested in Grok, Perplexity as its AI portfolio expands appeared first on FedScoop .

In my first weeks as Executive Director of EFF, I’ve been reminded every day how consequential this moment is in determining what kind of future we will have. We are on the edge. What each one of us steps up to do – with our expertise, energy, and resources – will determine whether our future is one of openness, security, and fundamental rights, or one controlled through fear, surveillance, and centralized power. I am proud to take the torch and help lead our EFF community forward at this pivota