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You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. What Trump and Melania appeared to whisper during'intense' VIP suite exchange... as Natalie Harp stood just behind them Canada threatens to cut off US electricity as Trump's trade war with Mark Carney spirals into fresh crisis: 'You better have batteries' Harry and Meghan want to move to the Cotswolds... but the party I attended last weekend - and these neighbourhood WhatsApp chats - show why they won't get the warm welcome they're hoping for: NADINE DORRIES The 10-cent supplement that turbocharges your Mounjaro AND reduces your cholesterol: Most patients won't have heard of it, but now experts reveal why anyone on jabs must take it - and how it can stop you putting weight back on Damning report confirms FEMA relief workers deliberately SKIPPED Trump voters' homes after deadly Hurricane Milton Distressing audio reveals moment doctor's husband who left daughter, 2, to die in hot car was found by nanny after killing himself just hours before he was due to be jailed Veteran actor who quit Hollywood for extreme career change steps out at 86... can you guess who? Shocking brawl breaks out at Barstool HQ as Dave Portnoy shares footage of'Uber Eats driver attacking staffer in dispute over order... before arrest is made' Supreme Court clears path for Trump's mail-in voting crackdown ahead of make-or-break midterms Hayden Panettiere's mom: 'My daughter didn't want to be saved!' Read bombshell hour-long interview on what she thinks REALLY happened at death-scene Airbnb... her jaw-dropping Brian Hickerson allegations... and fury at'shameful' Gayle King Tom Cruise's sex life crisis: Insiders reveal actor's secret'frustration' at 64... real reason Ana de Armas fling collapsed... lover's name he can't bear to hear... and Victoria Beckham's failed interventions Buc-ee's CEO reveals why beloved chain is DONE with blue states... and where he is plotting his next mega gas stations Lindsay Clancy lawyer asks judge to declare a MISTRIAL over prosecutor's very pointed question about'mortal sin' GOP civil war erupts over the creepy'Big Brother' weapon following MILLIONS of Americans as ex-CIA officer delivers chilling warning Jimmy Kimmel's wife recalls their illicit romance while she was working on his show... as she reveals how she knew he wanted to sleep with her Scientists uncover lost world beneath Egypt's desert that rewrites its ancient past Mica Miller's horrifying final months before she was found dead in swampy waters: Truth about disgraced pastor husband and sick texts he sent her family READ MORE: Does pouring coffee down the drain really harm the environment? Whether it's a trendy matcha latte or basic black filter coffee, hot drinks are a daily staple for millions of Brits. But a new analysis might make you think twice before splashing the cash at certain popular high street chains.

Children under 16 will be banned from buying high-caffeine energy drinks in England from April, the government has said. Drinks containing more than 150mg of caffeine per litre will be illegal to sell to children and younger teenagers in shops, restaurants, cafes, vending machines and online. Lower-caffeine soft drinks, such Diet Coke, are not affected and neither are tea and coffee. However, drinks including Red Bull, Monster, Relentless and Prime would all breach the limit. The new rules aim to reduce obesity levels in children and prevent issues such as disrupted sleep, increased anxiety and lack of concentration, as well as poor school results.

From protein supplements and electrolytes to greens powders and energy drinks, these are the discounted picks worth snagging. The wellness industry is a wild marketplace. You can't trust the marketing alone, and FDA regulation on protein powder deals is quite limited. It pays to be cautious. So for this year's Black Friday, we sifted through the markdowns, cross-checked claims, verified third-party tests, and sampled the supplements so you don't have to.

From protein supplements and electrolytes to greens powders and energy drinks, these are the discounted picks worth snagging. The wellness industry is a wild marketplace. You can't trust the marketing alone, and FDA regulation is quite limited. It pays to be cautious. So for this year's Black Friday, we sifted through the markdowns, cross-checked claims, verified third-party tests, and sampled the supplements so you don't have to.

Breakthroughs, discoveries, and DIY tips sent every weekday. Humans have a long history with alcohol--we've been making and consuming it for over ten thousand years, about as long as we've had agriculture. That's a long time for people to come up with all kinds of ideas about the drug and how it works. So, not surprisingly, some of them are wrong. Here are a few common myths about alcohol, debunked by scientific research.

We drank adaptogen drinks for weeks, and taste-tested with a trained sommelier. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. The best adaptogen drinks promise not just to wake you up in the morning, but offer focus and clarity and maybe even a warm wash of well-being. A different drink might tuck you gently in at night, or sub in for alcohol as a mindful party drink. I've spent months trying some of the most popular functional drinks on the market, bedding down with kava or tryptophan-laced xicha morada, and waking up with caffeine and L-theanine. Many of the new school of nootropic and functional drinks are like kissing cousins of mushroom coffee, except in refreshing soda form. Functional sodas might be chockablock with mushroom adaptogens such as reishi and cordyceps, alongside traditional home anxiety remedies such as ashwagandha or L-theanine. I both logged the effects of each soda, and held a large taste test with Portland, Oregon, sommelier Sami Gaston, owner of an excellent wine bar and shop called Bar Diane and Negociant, respectively--to determine how happy you'd be to drink them even if they didn't help you focus better on endless spreadsheets or the hunt for a job. Also check out WIRED's guide to mushroom gummies, or take your wellness in powdered form with the best greens powders and the best protein powders .

Recent advances in large language models (LLMs) highlight the importance of training data structure and quality in shaping reasoning behavior. However, most existing approaches focus on transforming data formats while neglecting the internal reasoning complexity of training samples, leaving the reasoning potential of data under-explored and underutilized. In this work, we posit that LLM logical reasoning performance is jointly constrained by the potential of the training data and the cognitive capacity of the model. To make this relationship measurable, we introduce Data Reasoning Intensity (DRI), a novel metric that quantifies the latent logical reasoning complexity of samples by decomposing and aggregating their logical structures. This allows us to analyze how well current LLMs utilize logical reasoning signals and identify performance gaps relative to data potential. Based on this insight, we introduce a re-cognizing optimization strategy that systematically enhances the logical reasoning intensity of training data. Rather than increasing data volume, our method re-optimizes existing samples to better align with the LLM's logical reasoning boundary. Extensive experiments show that our approach significantly improves performance and generalization over data-centric strategies. We further validate our method under a reinforcement learning framework. Our results indicate that prioritizing reasoning complexity in data rather than sheer scale or superficial form is essential to realizing LLMs' full cognitive potential.

Large language models (LLMs) have shown strong performance in many reasoning benchmarks. However, recent studies have pointed to memorization, rather than generalization, as one of the leading causes for such performance. LLMs, in fact, are susceptible to content variations, demonstrating a lack of robust planning or symbolic abstractions supporting their reasoning process. To improve reliability, many attempts have been made to combine LLMs with symbolic methods. Nevertheless, existing approaches fail to effectively leverage symbolic representations due to the challenges involved in developing reliable and scalable verification mechanisms. In this paper, we propose to overcome such limitations by synthesizing high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale via Monte Carlo estimation. A Process Reward Model (PRM) can be efficiently trained based on the synthesized data and then used to select more symbolic trajectories. The trajectories are then employed with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT) to improve logical reasoning and generalization. Our results on benchmarks (i.e., FOLIO and LogicAsker) show the effectiveness of the proposed method with gains on frontier and open-weight models. Moreover, additional experiments on claim verification data reveal that fine-tuning on the generated symbolic reasoning trajectories enhances out-of-domain generalizability, suggesting the potential impact of the proposed method in enhancing planning and logical reasoning.

Code generation, symbolic math reasoning, and other tasks require LLMs to produce outputs that are both syntactically and semantically correct. Constrained LLM generation is a promising direction to enforce adherence to formal grammar, but prior works have empirically observed that strict enforcement of formal constraints often diminishes the reasoning capabilities of LLMs. In this work, we first provide a theoretical explanation for why constraining LLM outputs to very restrictive grammars that only allow syntactically valid final answers reduces the reasoning capabilities of the model. Second, we demonstrate that by augmenting the output grammar with carefully designed additional rules, it is always possible to preserve the reasoning capabilities of the LLM while ensuring syntactic and semantic correctness in its outputs. Building on these theoretical insights, we propose a reasoning-augmented constrained decoding algorithm, CRANE, which effectively balances the correctness of constrained generation with the flexibility of unconstrained generation. Experiments on multiple open-source LLMs and benchmarks show that CRANE significantly outperforms both state-of-the-art constrained decoding strategies and standard unconstrained decoding, showing up to 10% points accuracy improvement over baselines on challenging symbolic reasoning benchmarks GSM-symbolic and FOLIO.