The UK is forecast to be the second-fastest growing of the world's most advanced economies this year and next, according to new projections from the International Monetary Fund (IMF). The rates of growth remain modest at 1.3% for both years, but that outperforms the other G7 economies apart from the US, in a torrid year of trade and geopolitical tensions. However, UK inflation is set to rise to the highest in the G7 in 2025 and 2026, the IMF predicts, driven by larger energy and utility bills. UK inflation is forecast to average 3.4% this year and 2.5% in 2026 but the IMF says this will be temporary, and fall to 2% by the end of next year. The G7 are seven advanced economies - the US, UK, France, Germany, Italy, Canada and Japan - but the group doesn't include fast-growing economies such as China and India.

Hamas executes'collaborators' in Gaza as it clings to power amid fears Trump's peace deal is already at risk Internet star who demanded free seats for fat fliers vanished without trace... now the Daily Mail has learned the heartbreaking reason why Donald Trump tells crowds there are world leaders he'doesn't like at ALL' as he teases who they are How Diane Keaton's closest friend helped her to achieve her'lifelong ambition' just months before she died - and the poignant legacy it leaves Kate and Wills' fresh start at their'forever home': Why they have fast-tracked their move to house they will never leave - even when he becomes King'It's Meghan Markle 3.0': Why the duchess has set tongues wagging that she's plotting another Sussex relaunch'as she holds cosy meeting with new editor of US Vogue' Trump's ominous warning to Macron at Egypt summit: 'You will see what is about to happen' Neil Diamond, 84, sang Sweet Caroline and worked with Cher as well as Barbra Streisand... see him now Insiders reveal how reluctant Katy Perry finally gave in to'persistent' Justin Trudeau... as sexy yacht photos get spicy response from his ex-wife Awkward moment Donald Trump asks Giorgia Meloni'You won't be offended if I say you're beautiful, right? Horrors endured by Israel's last 20 hostages: Chained, tortured, and starved. Lindsey Halligan removes senior DOJ official after taking over Virginia US attorney's office Gorgeous Bay Area enclave filled with hippies becomes America's ANGRIEST town over plans for huge affordable housing project MLB fans hail'greatest play in baseball HISTORY' after Dodgers thought they hit grand slam in Brewers game Father launches campaign to become sheriff as he faces murder trial for killing teenage daughter's abuser Spot the difference: Apple has rebranded its TV service as part of a'vibrant new identity' - so, can you see what has changed? But Apple TV+ is no more - as Apple has quietly rebranded its streaming service. 'Apple TV+ is now simply Apple TV, with a vibrant new identity,' the tech giant explained in the bottom of a press release on the streaming debut of its film, 'F1 The Movie'.

Hamas executes'collaborators' in Gaza as it clings to power amid fears Trump's peace deal is already at risk Internet star who demanded free seats for fat fliers vanished without trace... now the Daily Mail has learned the heartbreaking reason why Donald Trump tells crowds there are world leaders he'doesn't like at ALL' as he teases who they are How Diane Keaton's closest friend helped her to achieve her'lifelong ambition' just months before she died - and the poignant legacy it leaves Kate and Wills' fresh start at their'forever home': Why they have fast-tracked their move to house they will never leave - even when he becomes King'It's Meghan Markle 3.0': Why the duchess has set tongues wagging that she's plotting another Sussex relaunch'as she holds cosy meeting with new editor of US Vogue' Trump's ominous warning to Macron at Egypt summit: 'You will see what is about to happen' Neil Diamond, 84, sang Sweet Caroline and worked with Cher as well as Barbra Streisand... see him now Insiders reveal how reluctant Katy Perry finally gave in to'persistent' Justin Trudeau... as sexy yacht photos get spicy response from his ex-wife Awkward moment Donald Trump asks Giorgia Meloni'You won't be offended if I say you're beautiful, right? Horrors endured by Israel's last 20 hostages: Chained, tortured, and starved. Lindsey Halligan removes senior DOJ official after taking over Virginia US attorney's office Gorgeous Bay Area enclave filled with hippies becomes America's ANGRIEST town over plans for huge affordable housing project MLB fans hail'greatest play in baseball HISTORY' after Dodgers thought they hit grand slam in Brewers game Father launches campaign to become sheriff as he faces murder trial for killing teenage daughter's abuser Infections that are resistant to antibiotics continue to threaten global health, experts have warned--as hospitals report an alarming rise in the number of deaths driven by drug resistant strains. According to the World Health Organisation's (WHO) latest surveillance report, one in six bacterial infections were resistant to antibiotic treatments in 2023. Alarmingly, more than 40 per cent of antibiotics lost efficacy to treat common urinary tract, blood, gut and sexually-transmitted infections between 2018 and 2023, figures show.

Can Ukraine restore its pre-war borders? Why are Tomahawk missiles for Ukraine a'red line' for Russia? Is Russia testing NATO with aerial incursions in Europe? Ukrainian President Volodymyr Zelenskyy has said he will travel to Washington, DC, to meet his US counterpart, Donald Trump, on Friday. The main topics to be discussed will be air defence and long-range capabilities, Zelenskyy said in a message on his Telegram channel.

The private phone numbers of several high-profile figures including Australia's Prime Minister and Donald Trump Jr have been published on a US website. Both of their personal contact details remain publicly listed on the site, which uses AI to scrape the internet for information and the BBC has chosen not to name. Prime Minister Anthony Albanese's office is aware of the situation - which was first reported by independent Australian media outlet Ette Media - and local authorities are investigating. A spokesman for Australia's opposition leader Sussan Ley, whose private number was also published, said the matter was obviously concerning and they had requested the information be removed. The site claims to have contact details for hundreds of millions of professionals and is used by recruiters and sales representatives.

As Tina walks onto the stage in front of hundreds of people she is beaming. She's collecting her British Sign Language (BSL) certificate which is the culmination of a journey that began with tragedy. Learning BSL has helped me say words that I cannot speak, she says. In 2018, while returning from a holiday, Tina fell down a flight of stairs and was in a coma for six weeks. The accident caused a traumatic brain injury that dramatically changed her life, leaving her struggling to speak.

Recent advances in reasoning domains with neural networks have primarily been enabled by a training recipe that optimizes Large Language Models, previously trained to predict the next-token in a sequence, with reinforcement learning algorithms. We introduce a framework to study the success of this paradigm, and we theoretically expose the optimization mechanisms by which reinforcement learning improves over next-token prediction in this setting. We study learning from mixture distributions of short and long ``chain-of-thought'' sequences encoding a single task. In particular, when the task consists of predicting the parity of $d$ bits and long sequences are rare, we show how reinforcement learning after next-token prediction enables autoregressive transformers to generalize, whereas mere next-token prediction requires extreme statistical or computational resources to do so. We further explain how reinforcement learning leverages increased test-time computation, manifested in longer responses, to facilitate this learning process. In a simplified setting, we theoretically prove that autoregressive linear models following this training recipe can efficiently learn to predict the parity of $d$ bits as long as the proportion of long demonstrations in the data mix is not exponentially small in the input dimension $d$. Finally, we demonstrate these same phenomena in other settings, including the post-training of Llama-series models on mixture variations of common mathematical reasoning benchmarks.

Anemia, a condition marked by insufficient levels of red blood cells or hemoglobin, remains a widespread health issue affecting millions of individuals globally. Accurate and timely diagnosis is essential for effective management and treatment of anemia. In recent years, there has been a growing interest in the use of artificial intelligence techniques, i.e., machine learning (ML) and deep learning (DL) for the detection, classification, and diagnosis of anemia. This paper provides a systematic review of the recent advancements in this field, with a focus on various models applied to anemia detection. The review also compares these models based on several performance metrics, including accuracy, sensitivity, specificity, and precision. By analyzing these metrics, the paper evaluates the strengths and limitation of discussed models in detecting and classifying anemia, emphasizing the importance of addressing these factors to improve diagnostic accuracy.

Fake news detection becomes particularly challenging in real-time scenarios, where emerging events often lack sufficient supporting evidence. Existing approaches often rely heavily on external evidence and therefore struggle to generalize under evidence scarcity. To address this issue, we propose Evaluation-Aware Selection of Experts (EASE), a novel framework for real-time fake news detection that dynamically adapts its decision-making process according to the assessed sufficiency of available evidence. EASE introduces a sequential evaluation mechanism comprising three independent perspectives: (1) Evidence-based evaluation, which assesses evidence and incorporates it into decision-making only when the evidence is sufficiently supportive; (2) Reasoning-based evaluation, which leverages the world knowledge of large language models (LLMs) and applies them only when their reliability is adequately established; and (3) Sentiment-based fallback, which integrates sentiment cues when neither evidence nor reasoning is reliable. To enhance the accuracy of evaluation processes, EASE employs instruction tuning with pseudo labels to guide each evaluator in justifying its perspective-specific knowledge through interpretable reasoning. Furthermore, the expert modules integrate the evaluators' justified assessments with the news content to enable evaluation-aware decision-making, thereby enhancing overall detection accuracy. Moreover, we introduce RealTimeNews-25, a new benchmark comprising recent news for evaluating model generalization on emerging news with limited evidence. Extensive experiments demonstrate that EASE not only achieves state-of-the-art performance across multiple benchmarks, but also significantly improves generalization to real-time news. The code and dataset are available: https://github.com/wgyhhhh/EASE.

With the rise of reasoning language models and test-time scaling methods as a paradigm for improving model performance, substantial computation is often required to generate multiple candidate sequences from the same prompt. This enables exploration of different reasoning paths toward the correct solution, however, allocates the same compute budget for each prompt. Grounded on the assumption that different prompts carry different degrees of complexity, and thus different computation needs, we propose EAGer, a training-free generation method that leverages model uncertainty through token-wise entropy distribution to reduce redundant computation and concurrently improve overall performance. EAGer allows branching to multiple reasoning paths only in the presence of high-entropy tokens, and then reallocates the saved compute budget to the instances where exploration of alternative paths is most needed. We find that across multiple open-source models on complex reasoning benchmarks such as AIME 2025, EAGer can reallocate the budget without accessing target labels, achieving the best efficiency-performance trade-off in terms of reasoning length and Pass@k. When target labels are accessible, EAGer generates up to 65% fewer tokens (hence saving compute) and achieves up to 37% improvement in Pass@k compared to the Full Parallel Sampling.