Due to health and energy concerns, the California Legislature is considering bills to prohibit data centers from being exempted from the state's stringent environmental law and impose new tariffs on new major energy users that strain power supplies.

Things to Do in L.A. With Iran, California military families fear another'forever war' Shalena Critchlow, at the Oceanside Pier, holds a photo of her son Cpl. Saiveon Critchlow, who recently completed his service with the U.S. Marines. This is read by an automated voice. Please report any issues or inconsistencies here .

Shocking new video shows NYC's anti-white renters' tsar sharing her desire to make ALL Americans live in social housing Amy Schumer's friends reveal true meaning of thin bikini pictures and why they're'monitoring her'... as depth of ex Chris Fischer's heartbreak is laid bare The urgent questions for Timothy Busfield's wife Melissa Gilbert that no one dares ask: MAUREEN CALLAHAN analyzes child sex abuse claims spanning 30 years... and uncovers a potential bombshell Chilling final message of doctor's wife gunned down next to her twins, 6, in Arkansas mansion... as her son reveals red flags everyone missed Moment teenager launches bottle attack on'paedophile' is shown to murder trial after 49-year-old'was lured to meeting with girl, 16, and beaten to death with rocks' 'Brazilian Popeye' bodybuilder famed for injecting alcohol and oil into his arms dead at 55 Disney adult sparks outrage with her'trashy' bar crawl through kid-friendly theme park Swimsuit model Aoi Fujino, 27, dies just days after retiring with emotional post: 'Please remember me' I was swimming in shallow waters on my dream holiday when I was attacked by a shark. I lost my hand, leg and two-thirds of my blood. I should be dead... but this is how I was saved by three angels Palm Beach elites break out in civil war over $200m'greed project'... as Don Jr's fiancée furiously intervenes Ellen Greenberg case set to be REOPENED by federal prosecutors after infamous 2011 'suicide' of Philadelphia schoolteacher found with 20 stab wounds RICHARD EDEN: Meghan and Harry'plot' and why Prince William and Kate have REALLY hired a crisis expert. 'The end of the world as we know it': Poland warns of'disaster' if NATO nations turn on each other over Trump's bid to claim Greenland as Danish troops arrive in the region At least 40 earthquakes have shaken Southern California since Wednesday morning, with the largest reaching a magnitude of 4.4. The US Geological Survey recorded the first quake near Holtville at 1:40am PT on Wednesday, with the most recent detected on Thursday morning.

AI is rapidly transforming journalism, but the extent of its use in published newspaper articles remains unclear. We address this gap by auditing a large-scale dataset of 186K articles from online editions of 1.5K American newspapers published in the summer of 2025. Using Pangram, a state-of-the-art AI detector, we discover that approximately 9% of newly-published articles are either partially or fully AI-generated. This AI use is unevenly distributed, appearing more frequently in smaller, local outlets, in specific topics such as weather and technology, and within certain ownership groups. We also analyze 45K opinion pieces from Washington Post, New York Times, and Wall Street Journal, finding that they are 6.4 times more likely to contain AI-generated content than news articles from the same publications, with many AI-flagged op-eds authored by prominent public figures. Despite this prevalence, we find that AI use is rarely disclosed: a manual audit of 100 AI-flagged articles found only five disclosures of AI use. Overall, our audit highlights the immediate need for greater transparency and updated editorial standards regarding the use of AI in journalism to maintain public trust.

Transitioning to sustainable and resilient energy systems requires navigating complex and interdependent trade-offs across environmental, social, and resource dimensions. Neglecting these trade-offs can lead to unintended consequences across sectors. However, existing assessments often evaluate emerging energy pathways and their impacts in silos, overlooking critical interactions such as regional resource competition and cumulative impacts. We present an integrated modeling framework that couples agent-based modeling and Life Cycle Assessment (LCA) to simulate how energy transition pathways interact with regional resource competition, ecological constraints, and community-level burdens. We apply the model to a case study in Southern California. The results demonstrate how integrated and multiscale decision making can shape energy pathway deployment and reveal spatially explicit trade-offs under scenario-driven constraints. This modeling framework can further support more adaptive and resilient energy transition planning on spatial and institutional scales.

Things to Do in L.A. Tap to enable a layout that focuses on the article. The California Department of Corrections and Rehabilitation operates a reentry facility across the street from MacArthur Park. Two inmates living at the facility were shot, one fatally, on Sept. 2. Voice comes from the use of AI. Please report any issues or inconsistencies here . One man was killed and another wounded outside a facility for state prisoners serving out the remainder of their sentences in the community.

--In today's information-rich era, learners have access to abundant educational resources, but the lack of practice materials tailored to these resources presents a significant challenge. This project addresses that gap by developing a multimodal question generation system that can automatically generate diverse question types from various content formats. This project lays the foundation for automated, scalable, and intelligent question generation, carefully balancing resource efficiency, robust functionality and a smooth user experience. Creating assessment questions is a time-consuming and labor-intensive task for educators. Traditional methods require manual extraction of information from materials, which can lead to inconsistencies and errors. Additionally, students often struggle to find varied practice questions that cover all aspects of the material they are studying. With the increasing use of multimedia in educational content, there is a growing need for systems that can process various data types, including text, diagrams, and audio recordings.

Lightweight Large Language Models (LwLLMs) are reduced-parameter, optimized models designed to run efficiently on consumer-grade hardware, offering significant advantages in resource efficiency, cost-effectiveness, and data privacy. However, these models often struggle with limited inference and reasoning capabilities, which restrict their performance on complex tasks and limit their practical applicability. Moreover, existing prompt optimization methods typically rely on extensive manual effort or the meta-cognitive abilities of state-of-the-art LLMs, making them less effective for LwLLMs. To address these challenges, we introduce DeBoP, a new Direct Behavior Optimization Paradigm, original from the Chain-of-Thought (CoT) prompting technique. Unlike CoT Prompting, DeBoP is an automatic optimization method, which focuses on the optimization directly on the behavior of LwLLMs. In particular, DeBoP transforms the optimization of complex prompts into the optimization of discrete, quantifiable execution sequences using a gradient-free Monte Carlo Tree Search. We evaluate DeBoP on seven challenging tasks where state-of-the-art LLMs excel but LwLLMs generally underperform. Experimental results demonstrate that DeBoP significantly outperforms recent prompt optimization methods on most tasks. In particular, DeBoP-optimized LwLLMs surpass GPT-3.5 on most tasks while reducing computational time by approximately 60% compared to other automatic prompt optimization methods.

This work tackles the problem of overoptimization in reinforcement learning from human feedback (RLHF), a prevalent technique for aligning models with human preferences. RLHF relies on reward or preference models trained on \emph{fixed preference datasets}, and these models are unreliable when evaluated outside the support of this preference data, leading to the common reward or preference hacking phenomenon. We propose novel, pessimistic objectives for RLHF which are provably robust to overoptimization through the use of pessimism in the face of uncertainty, and design practical algorithms, P3O and PRPO, to optimize these objectives. Our approach is derived for the general preference optimization setting, but can be used with reward models as well. We evaluate P3O and PRPO on the tasks of fine-tuning language models for document summarization and creating helpful assistants, demonstrating remarkable resilience to overoptimization.

Muon Space (Muon) is building a constellation of small satellites, many of which will carry global navigation satellite system-reflectometry (GNSS-R) receivers. In preparation for the launch of this constellation, we have developed a generalized deep learning retrieval pipeline, which now produces operational GNSS-R near-surface soil moisture retrievals using data from NASA's Cyclone GNSS (CYGNSS) mission. In this article, we describe the input datasets, preprocessing methods, model architecture, development methods, and detail the soil moisture products generated from these retrievals. The performance of this product is quantified against in situ measurements and compared to both the target dataset (retrievals from the Soil Moisture Active-Passive (SMAP) satellite) and the v1.0 soil moisture product from the CYGNSS mission. The Muon Space product achieves improvements in spatial resolution over SMAP with comparable performance in many regions. An ubRMSE of 0.032 cm$^3$ cm$^{-3}$ for in situ soil moisture observations from SMAP core validation sites is shown, though performance is lower than SMAP's when comparing in forests and/or mountainous terrain. The Muon Space product outperforms the v1.0 CYGNSS soil moisture product in almost all aspects. This initial release serves as the foundation of our operational soil moisture product, which soon will additionally include data from Muon Space satellites.