Environment

The Summer of Ludd just wrapped in NYC; we talk to a participant and documentarian. Plus, is the Trump admin secretly worried about an AI bubble? The biggest-ever data center gets shut down, and more.

Amid growing political pushback against data center development, more evidence is emerging of their impact on local economies.

As America celebrates the 250th anniversary of the signing of the Declaration of Independence, recent polls remind us of how divided this country remains. And yet, despite these harsh numbers, it’s not as divided as polls portray. Americans across the political spectrum are finding unexpected common ground. For example,  health  is becoming a powerful force reshaping traditional political alliances, with bipartisan movements to ban food additives and  pesticides , expand care for

Continual learning commonly relies on post-hoc mechanisms such as replay, elastic regularization, or distillation. This work argues that forgetting should instead be modeled directly as interference between tasks. In the frozen-feature regime, forgetting from learning a new task is exactly the interference energy induced on the old task. In deep networks, the same quantity is recovered through path-averaged curvature with minimal additional forward passes. When task supports are disjoint, forget

Data centers are so hot right now—and not just because of the waste heat. From the halls of Congress to the digital world of podcasts, it seems like all people want to talk about is artificial ...

Artificial intelligence training data company Mercor.io Corp. announced today that it has acquired Deeptune Inc., a startup that builds simulated software environments used to train AI agents. Financial terms were not disclosed. The deal closed nearly four months after Mercor Chief Executive Brendan Foody wrote a personal angel check into Deeptune’s $43 million Series A […] The post Mercor buys Deeptune to build training environments for AI agents appeared first on SiliconANGLE .

Defense officials have long-touted the benefits of directed energy systems such as their high-speed engagement, low cost-per-shot and deep magazines. The post Pentagon awards deals for laser weapons that could shoot down drone swarms appeared first on DefenseScoop .

As agentic AI systems are increasingly applied to cyber-physical environments, their evaluation requires assessment of both task performance and trustworthiness. In decentralized energy markets, autonomous agents may improve market utility, but may also exploit invalid physical data, create artificial liquidity, and produce unstable governance decisions. Therefore, we propose SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents. It formulates market governa

As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such changes rather than overfitting to any single environment. Inverse reinforcement learning (IRL) provides a principled way to infer such objectives from human feedback. However, existing analyses of optimal teaching approaches for IRL focus on single-environment, demonstration-only settings, leaving underexplored how hete

MIT researchers developed FloatForm, a swarm of small aquatic robots that snap together like ants forming a raft, assembling into reconfigurable structures on the water.

On June 10, 2026, the FPF Center for Artificial Intelligence convened a Frontiers Workshop in Washington, DC. Held as part of FPF’s National Science Foundation (NSF) and the Department of Energy (DoE)-funded Privacy-Enhancing Technologies (PETs) Research Coordination Network, the workshop brought together privacy and frontier AI practitioners to examine challenges at the intersection of data […]

What GAO Found In April 2025, GAO identified 30 priority recommendations for the Department of Energy (DOE). Since then, DOE has implemented 5 of those recommendations by, among other things, directing NNSA Production Modernization programs to follow best practices for schedule development. In July 2026, GAO identified an additional priority recommendation, and removed the priority status from two recommendations, bringing the total number to 24. GAO is highlighting the following three areas tha

Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet existing benchmarks rarely simulate complete psychiatric clinical encounters. We introduce $\textbf{MentalHospital}$, a virtual evaluation environment for LLM-based psychiatric clinical encounters. MentalHospital instantiates the Subjective Interviewing, Objective Examination, Diagnostic Assessment, and Treatment Planning (S.O.A.P.) workflow, using

Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and social conditions. We identify self-locking as a runtime failure mode in continuing persona-life loops: locally plausible events keep appearing while the generated life collapses toward familiar environments, weak relationships, suspended decisions, and stale life stages. We trace this failure to model-level convergence toward high-probability behavioral channels and system-level context

The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models. While existing UDA methods alleviate this problem by aligning source and target features, they give limited consideration to modulation-specific structures that remain informative across domain conditions. In this paper, we consider signal prior knowledge, grounded in communication protocols and p

Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computationally intensive screening. In complex reactions such as electrochemical carbon dioxide reduction, product selectivity is governed by dynamic interfacial, electrolyte, and potential factors as well as kinetic pathway competition. Conventional descriptor-based machine learning and computational potentials struggle to res

Pursuant to the Federal Advisory Committee Act and following consultation with the Committee Management Secretariat of the General Services Administration, notice is hereby given that the Electricity Advisory Committee (EAC) will be renewed for a two-year period. The Committee will provide advice, information, and recommendations to the Secretary of Energy on a continuing basis regarding policies and programs to modernize the nation's electric system.

A state-owned newspaper in China recently published a satellite image of a data center in Gainesville, Va., writing in English that the development of artificial intelligence posed a threat to Americans' physical and financial well-being. ... (https://incidentdatabase.ai/cite/1581#7504)

With opposition to data centers cutting across traditional political lines, and multiple high-profile projects already blocked or delayed, the data center fight has become volatile at the hyperlocal level, creating the conditions that forei ... (https://incidentdatabase.ai/cite/1581#7505)

Jellyfish, raw crabs, and endless ocean — an entrepreneur’s relaxing trip in South China turns into a nightmare ordeal.

How readily people override intuitive conclusions through reflection shapes how they navigate dense information environments with reliable and misleading sources; yet the effectiveness of a prominent measure, the Cognitive Reflection Test (CRT), is eroded by widespread exposure to classic items and leaves open how such tendencies manifest more generally in decision style and linguistic expression. The Behavioural Reflection Test (BRT) addresses these issues with a brief open-ended measure of rea

The AI infrastructure boom has trained the industry’s attention on silicon and power, but a more fundamental constraint within the capital stack is quietly throttling the speed of global data center deployment. As demand for AI compute continues to outpace infrastructure availability, a growing number of data center developers are stalling not because they lack megawatts […] The post Argentum targets the capital stack as the missing layer in AI infrastructure buildout appeared first on SiliconAN

The attacker exploited AI workflows, chained cloud weaknesses, and stolen credentials to extort a large Amazon customer.

In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed. We call this failure mode "behavioral state decay". We study memory as an active intervention mechanism rather than passive retrieval. A separate me

Semantic audio applications increasingly require controllable generation on commodity and embedded hardware rather than through framework-heavy datacenter stacks. We present aria, a dependency-free native runtime that runs the complete text-to-music pipeline of Stable Audio~3 (SA3) on ordinary GPUs, CPU-only machines, and a Raspberry~Pi~5, with no Python or deep-learning framework underneath. Our main contribution is a study of quantization: running the model at lower numerical precision to fit

The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assisting users in real-world environments. However, existing benchmarks struggle to evaluate such agents effectively, as they often rely on sandboxed environments and single-turn evaluation paradigms. Moreover, their scenario-based task taxonomies mix multiple model capabilities within the same task category, making it diff

The NPAEEA, supported by the Global Program on Sustainability (GPS), introduces a groundbreaking approach to integrating environmental and economic data. It prioritizes the development of six key ...

Iran’s leaders could use the peace dividend to invest in fixing its severe environmental problems.

The climate case against beef is now almost boringly well-established: It is, by far, the most carbon-intensive food in the world, amounting to about 6 percent of all global greenhouse gas emissions. But cows don’t just become burgers and steaks. They also become shoes, bags, couches, and car interiors — products often marketed with a […]

Precise autonomous navigation on unstructured planetary surfaces is a critical prerequisite for future exploration missions, particularly in GNSS-denied environments such as the Lunar South Pole or Martian deserts. Traditional Visual Odometry (VO) methods, which rely on tracking low-level geometric features (e.g., corners), often fail under the extreme illumination contrast of the Moon or the textural monotony of the Martian regolith. In this work, we present a zero-shot semantic landmark-based

Squeeze on Rust Belt electricity bills threatens Trump’s manufacturing plan.

Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools. DeepSearch-World contains 420K multi-hop QA tasks

Large language models are rapidly moving towards closing the development cycle, transitioning from simple assistive companions to autonomous contributors deeply embedded into collaborative development environments. Despite their accelerated adoption, existing evaluation techniques are limited due to their fragmented nature and distorted projection of true model capabilities, often obtained from hypothetical syntactic scenarios. This research aims to bridge this gap by providing a comprehensive e

Vision-language models (VLMs) struggle to generalize in interactive physical reasoning, particularly under unseen tasks and environments. Two key failure modes are prominent: hallucinated chain-of-thought (CoT) reasoning that contradicts physical reality, and misalignment between the model's reasoning and actions. We present VAORA (Visual Action Outcome Reasoning Alignment), a novel reward design that directly addresses both issues. VAORA introduces two complementary rewards: Visual Alignment Re

Images tell us what a scene looks like, but rarely what it would feel like to be there. While recent datasets pair visual scenes with electronic-nose measurements, aligning smell signals with images remains challenging because many olfactory cues arise from contextual environmental factors that are not directly visible in pixels. We introduce SCENT, a multimodal framework that uses language guidance as a semantic bridge between vision and olfaction. Our approach leverages Vision-Language Models

In May, an anonymous artist who goes by SHL0MS on X posted that he had used AI to generate an image inspired by Claude Monet and asked people to weigh in on how it missed the mark. More than 600 responses called out issues, saying the colors were off, the depth was all wrong, and that AI didn’t understand how light worked. SHL0MS then revealed that the image was of a real Monet, one of around 250 variations of water lilies the artist had painted in his lifetime. He had simply downloaded a high-r

Environmental DNA contained in a small sample of water, sand or even air can reveal the presence of people, wildlife and pathogens, helping researchers track where they’ve migrated.

July 2026 - Since their adoption in 2015, the Sustainable Development Goals (SDGs) have delivered results at scale – bringing access to water, electricity and health care to billions. Without a ...

Putnam's Social Capital Theory is a foundational framework for collective action and community prosperity. However, traditional empirical methods face practical limits on control and replication. Meanwhile, LLM-based social simulations are typically behavior-driven and lack theory-aligned environments for modeling Putnam's core propositions. To address these gaps, we introduce SocaSim, an LLM-based multi-agent simulation framework to study Putnam's Social Capital Theory from theoretical blueprin