Furious Trump issues chilling threat to Iran demanding Strait of Hormuz is'FULLY OPENED' in hours or America will'obliterate their power plants'... and there's already a key target in sight Chappell Roan accused of'leaving Jude Law's 11-year-old daughter in tears and using security guard to threaten her' I was the only one JFK Jr and Carolyn Bessette trusted when they burdened me with an extraordinarily intimate secret. How Iran's ruthless enforcers use rape to crush dissent: Brutal sex attacks on victims as young as 12 used to strike fear into protesters, rights groups reveal amid fury over sickening nurse gang rape Shia LaBeouf suffers public meltdown in Rome as he's caught screaming'f*** off' at woman... after battery arrests'He just didn't protect him': Insiders reveal REAL reason Justin Bieber and Usher's secret feud hit'boiling point' at Oscars Mom-to-be finds out cop who got her pregnant has HIV after baby mama's text... as he is charged with felony I thought I was losing my mind... then doctors told me I had'exploding head syndrome'. America is about to be torn apart by a financial tsunami - and it's not just an oil crisis to fear. Denise Richards's plastic surgeon reveals stunning before-and-after photos of her facelift'Get the f*** out of my life,' JFK Jr screamed at Carolyn Bessette... what she cruelly told friends about his manhood... the cuckolding, cocaine - and moment that sent her truly psychotic: MAUREEN CALLAHAN has the untold REAL story Florida's Olivier Rioux, tallest player in college basketball history, dwarfs 6ft8 March Madness rival as defending champs roll to win YouTuber who exposed Somali'fraudsters' in bombshell investigation reveals terrifying threats from left-wing activists... as he begs for cash to help pay for security Charlie's Angels bombshell Jaclyn Smith looks nowhere near her 80 years in Beverly Hills... see her now Fury over plan for 110 homes near Yosemite Park that will tower up to 24ft and'cause road chaos' Gisele Pelicot tells how she thought she was dying from a brain tumor... then she discovered the horrific truth of her husband's abuse Iran ballistic missile hits Israeli city in terrifying strike near top-secret facility that is key to country's atomic weapons program Couple murdered outside Walgreens near golf's Players Championship were killed by jealous ex, says sheriff As the threat of a nuclear war intensifies, the terrifying reality of what could happen after the bombs explode may cause more fear than the initial cataclysm. For decades, worst-case scenarios have projected that tens of millions could perish within minutes as nuclear warheads struck major metropolitan areas such as New York, Washington, Chicago and Los Angeles .

Iranian forces deploy explosive drone boats disguised as fishing vessels in Strait of Hormuz, defense expert Cameron Chell warns, marking new phase of hybrid warfare.

Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting Ben Affleck'scores $600m deal' with Netflix to sell his AI film start-up Long hair over 45 is ageing and try-hard. I've finally cut mine off. Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Shocking declassified files have revealed how the US government intentionally injected Americans with radioactive substances without their knowledge or consent. This happened to 18 hospital patients between 1945 and 1947, where doctors secretly administered plutonium to study how it moved through and affected the human body as part of early US nuclear experiments during World War II and the Cold War. The chilling details originally came to light in 1995, when the Clinton White House had the Department of Energy disclose the secret experiments aimed at understanding radiation risks to workers building atomic bombs.

As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior work has primarily examined socio-demographic and left--right political dimensions, little attention has been paid to how LLMs align with broader geopolitical value systems, particularly the democracy--authoritarianism spectrum. In this paper, we propose a novel methodology to assess such alignment, combining (1) the F-scale, a psychometric tool for measuring authoritarian tendencies, (2) FavScore, a newly introduced metric for evaluating model favorability toward world leaders, and (3) role-model probing to assess which figures are cited as general role-models by LLMs. We find that LLMs generally favor democratic values and leaders, but exhibit increased favorability toward authoritarian figures when prompted in Mandarin. Further, models are found to often cite authoritarian figures as role models, even outside explicit political contexts. These results shed light on ways LLMs may reflect and potentially reinforce global political ideologies, highlighting the importance of evaluating bias beyond conventional socio-political axes. Our code is available at: https://github.com/irenestrauss/Democratic-Authoritarian-Bias-LLMs.

Plus: A CDC panel voted to recommend delaying the hepatitis B vaccine for babies. If you're a longtime reader, you probably know that our newsroom selects 10 breakthroughs every year that we think will define the future . This group exercise is mostly fun and always engrossing, with plenty of lively discussion along the way, but at times it can also be quite difficult. The 2026 list will come out on January 12--so stay tuned. In the meantime, we wanted to share some of the technologies from this year's reject pile, as a window into our decision-making process. These four technologies won't be on our 2026 list of breakthroughs, but all were closely considered, and we think they're worth knowing about.

A new congressional report warns China's military buildup has reached a war footing with 350 new missile silos and 20% nuclear expansion, threatening U.S. deterrence.

Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent space. In this paper, we show a nuance in this training objective, and find that by changing the language of the input query, we can improve the question answering ability of language models. Our contributions are two-fold. First, we introduce the term Language Specific Knowledge (LSK) to denote queries that are best answered in an "expert language" for a given LLM, thereby enhancing its question-answering ability. We introduce the problem of language selection -- for some queries, language models can perform better when queried in languages other than English, sometimes even better in low-resource languages -- and the goal is to select the optimal language for the query. Second, we introduce simple to strong baselines to test this problem. Additionally, as a first-pass solution to this novel problem, we design LSKExtractor to benchmark the language-specific knowledge present in a language model and then exploit it during inference. To test our framework, we employ three datasets that contain knowledge about both cultural and social behavioral norms. Overall, LSKExtractor achieves up to 10% relative improvement across datasets, and is competitive against strong baselines, while being feasible in real-world settings. Broadly, our research contributes to the open-source development (https://github.com/agarwalishika/LSKExtractor/tree/main) of language models that are inclusive and more aligned with the cultural and linguistic contexts in which they are deployed.

Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-resource languages due to data scarcity. We hypothesize that this performance deficit reduces the utility of AI, thereby slowing adoption in Low-Resource Language Countries (LRLCs). To test this, we use a weighted regression model to isolate the language effect from socioeconomic and demographic factors, finding that LRLCs have a share of AI users that is approximately 20% lower relative to their baseline. These results indicate that linguistic accessibility is a significant, independent barrier to equitable AI diffusion.

Clinical decision support systems (CDSS) are used to improve clinical and service outcomes, yet evidence from low- and middle-income countries (LMICs) is dispersed. This protocol outlines methods to quantify the impact of CDSS on patient and healthcare delivery outcomes in LMICs. We will include comparative quantitative designs (randomized trials, controlled before-after, interrupted time series, comparative cohorts) evaluating CDSS in World Bank-defined LMICs. Standalone qualitative studies are excluded; mixed-methods studies are eligible only if they report comparative quantitative outcomes, for which we will extract the quantitative component. Searches (from inception to 30 September 2024) will cover MEDLINE, Embase, CINAHL, CENTRAL, Web of Science, Global Health, Scopus, IEEE Xplore, LILACS, African Index Medicus, and IndMED, plus grey sources. Screening and extraction will be performed in duplicate. Risk of bias will be assessed with RoB 2 (randomized trials) and ROBINS-I (non-randomized). Random-effects meta-analysis will be performed where outcomes are conceptually or statistically comparable; otherwise, a structured narrative synthesis will be presented. Heterogeneity will be explored using relative and absolute metrics and a priori subgroups or meta-regression (condition area, care level, CDSS type, readiness proxies, study design).

Large Language Models (LLMs) are rapidly being adopted in conservation to address the biodiversity crisis, yet their reliability for species evaluation is uncertain. This study systematically validates five leading models on 21,955 species across four core IUCN Red List assessment components: taxonomy, conservation status, distribution, and threats. A critical paradox was revealed: models excelled at taxonomic classification (94.9%) but consistently failed at conservation reasoning (27.2% for status assessment). This knowledge-reasoning gap, evident across all models, suggests inherent architectural constraints, not just data limitations. Furthermore, models exhibited systematic biases favoring charismatic vertebrates, potentially amplifying existing conservation inequities. These findings delineate clear boundaries for responsible LLM deployment: they are powerful tools for information retrieval but require human oversight for judgment-based decisions. A hybrid approach is recommended, where LLMs augment expert capacity while human experts retain sole authority over risk assessment and policy.