The US Built a Site to Ensure Fair Access to Public Lands. Recreation.gov was supposed to make access to public lands more equitable and streamlined. It's a few minutes before 8 am Mountain Time on March 16, the day that river permit cancellations are released on Recreation.gov, the federal website for public land reservations. Rec.gov, as it's commonly called, administers everything from river permits and timed entrance fees at the most popular national parks to campground reservations on remote sites belonging to the Bureau of Land Management, and a lot of people are recreating on public land these days. There were 11 million reservations on the site in 2024, up significantly from 3.5 million reservations reported in 2019. At the center of it all is an unlikely player in the outdoor recreation space: The site is operated by the government contractor Booz Allen Hamilton, a corporation known more for cybersecurity than rafting trips. Early each year, outdoor enthusiasts gear up for Recreation.gov's annual lotteries for some of the most iconic experiences in the country: a river trip down Idaho's Middle Fork of the Salmon River, which flows through the Frank Church River of No Return Wilderness. Backcountry permits to hike into the Wave, an otherworldly rock formation in Arizona's Paria Canyon-Vermilion Cliffs Wilderness. Overnight stays in the rugged, lake-studded Enchantments, in Washington's Okanogan-Wenatchee National Forest. Odds of getting a desirable Middle Fork permit are around 2 percent.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Former Vice President Kamala Harris addresses delegates with the Democratic National Committee at their winter meeting in downtown Los Angeles on Friday. This is read by an automated voice. Please report any issues or inconsistencies here . California's two most prominent Democrats, former Vice President Kamala Harris and Gov. Gavin Newsom, addressed national Democratic leaders in L.A.

Verifying the complex and multi-step reasoning of Large Language Models (LLMs) is a critical challenge, as holistic methods often overlook localized flaws. Step-by-step validation is a promising alternative, yet existing methods are often rigid. They struggle to adapt to diverse reasoning structures, from formal proofs to informal natural language narratives. To address this adaptability gap, we propose the Graph of Verification (GoV), a novel framework for adaptable and multi-granular verification. GoV's core innovation is its flexible "node block" architecture. This mechanism allows GoV to adaptively adjust its verification granularity--from atomic steps for formal tasks to entire paragraphs for natural language--to match the native structure of the reasoning process. This flexibility allows GoV to resolve the fundamental trade-off between verification precision and robustness. Experiments on both well-structured and loosely-structured benchmarks demonstrate GoV's versatility. The results show that GoV's adaptive approach significantly outperforms both holistic baselines and other state-of-the-art decomposition-based methods, establishing a new standard for training-free reasoning verification.

Fox News contributor Dan Hoffman joins'Fox & Friends' to discuss Ukraine's claims that Russia has violated the partial ceasefire deal and Putin's threat to Trump over his effort to acquire Greenland. Two people were killed and 35 others were injured after a Russian drone attack struck a military hospital and shopping center in Ukraine late Saturday night, Ukrainian officials say. Regional Gov. Oleh Syniehubov condemned the attack on Kharkiv in a statement on Sunday, saying a 67-year-old man and a 70-year-old woman were killed. The attack comes as Russia's aggression in Ukraine shows no signs of stopping despite efforts by President Donald Trump's administration to speed along peace talks. Ukraine says that many of the casualties were servicemen undergoing treatment at the military hospital.

Digital individual participant data (IPD) from clinical trials are increasingly distributed for potential scientific reuse. The identification of available IPD, however, requires interpretations of textual data-sharing statements (DSS) in large databases. Recent advancements in computational linguistics include pre-trained language models that promise to simplify the implementation of effective classifiers based on textual inputs. In a subset of 5,000 textual DSS from ClinicalTrials.gov, we evaluate how well classifiers based on domain-specific pre-trained language models reproduce original availability categories as well as manually annotated labels. Typical metrics indicate that classifiers that predicted manual annotations outperformed those that learned to output the original availability categories. This suggests that the textual DSS descriptions contain applicable information that the availability categories do not, and that such classifiers could thus aid the automatic identification of available IPD in large trial databases.

Gov. Gavin Newsom on Sunday vetoed SB 1047, an artificial intelligence safety bill that would have established requirements for developers of advanced AI models to create protocols aimed at preventing catastrophes. The bill, introduced by Sen. Scott Wiener (D-San Francisco), would have required developers to submit their safety plans to the state attorney general, who could hold them liable if AI models they directly control were to cause harm or imminent threats to public safety. Additionally, the legislation would have required tech firms to be able to turn off the AI models they directly control if things went awry. In his veto message, Newsom said the legislation could give the public a "false sense of security about controlling this fast-moving technology" because it targeted only large-scale and expensive AI models and not smaller, specialized systems. "While well-intentioned, SB 1047 does not take into account whether an AI system is deployed in high-risk environments, involves critical decision-making or the use of sensitive data," Newsom's veto message stated.

Gov. Gavin Newsom on Tuesday signed into law two bills that will give actors more protections over their digital likenesses, addressing concerns brought up during last year's Hollywood strike led by performers guild SAG-AFTRA. One of the bills, AB1836, prohibits and penalizes the making and distribution of a deceased person's digital replica without permission from their estate. The other legislation, AB2602, makes a contract entered after Jan. 1, 2025, unenforceable if a digital replica of an actor was used when the individual could have performed the work in person, if the contract did not include a reasonably specific description of how the digital replica would be used and if the actor was not represented by their lawyer or labor union when the deal was signed. "No one should live in fear of becoming someone else's unpaid digital puppet," said Duncan Crabtree-Ireland, SAG-AFTRA's national executive director and chief negotiator in a statement. Newsom has led the way in protecting people -- and families -- from A.I. replication without real consent."

Misinformation is all over the internet, including the -- at times -- chaos that is X (formerly Twitter). AI bots have a habit of adding to it. Now, with barely two months left until the presidential election, an update to Grok, X's premium chatbot, could curve some of it (after being called out for said election misinformation). Grok will now direct anyone with an election-related query to Vote.org, a non-partisan website operated through a partnership between the US government, the US Election Assistance Commission and the Cybersecurity and Infrastructure Security Agency. The catalyst for change came on July 21, only hours after President Biden announced his decision not to seek reelection, when Grok falsely posted that the ballot deadline had passed in nine states, implying officials couldn't change the democratic candidate. Minnesota Secretary of State Steve Simon had staff attempt to contact X about the error, to which they received the response, "Busy now, please check back later."

Large language models (LLMs) are currently being used to answer medical questions across a variety of clinical domains. Recent top-performing commercial LLMs, in particular, are also capable of citing sources to support their responses. In this paper, we ask: do the sources that LLMs generate actually support the claims that they make? To answer this, we propose three contributions. First, as expert medical annotations are an expensive and time-consuming bottleneck for scalable evaluation, we demonstrate that GPT-4 is highly accurate in validating source relevance, agreeing 88% of the time with a panel of medical doctors. Second, we develop an end-to-end, automated pipeline called \textit{SourceCheckup} and use it to evaluate five top-performing LLMs on a dataset of 1200 generated questions, totaling over 40K pairs of statements and sources. Interestingly, we find that between ~50% to 90% of LLM responses are not fully supported by the sources they provide. We also evaluate GPT-4 with retrieval augmented generation (RAG) and find that, even still, around 30\% of individual statements are unsupported, while nearly half of its responses are not fully supported. Third, we open-source our curated dataset of medical questions and expert annotations for future evaluations. Given the rapid pace of LLM development and the potential harms of incorrect or outdated medical information, it is crucial to also understand and quantify their capability to produce relevant, trustworthy medical references.

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained clinical trial BERT model. Our results demonstrate the feasibility of automatically classifying common exclusion criteria. Additionally, we demonstrate the value of a pre-trained language model specifically for clinical trials, which yields the highest average performance across all criteria.