Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Trending Now Mashable Selects Creator Playbook Back to School Good Connection: Uplifting stories for a digital age Switch Off Mashable Voices Safety Net Versus All Series Scientists knew Mercury was shrinking. They didn't know the true extent. Elisha Sauers writes about space for Mashable, taking deep dives into NASA's moon and Mars missions, chatting up astronauts and history-making discoverers, and jetting above the clouds . Through 17 years of reporting, she's covered a variety of topics, including health, business, and government, with a penchant for public records requests. She previously worked for in Norfolk, Virginia, and in Annapolis, Maryland.

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Fix It Future Blink Self Made Small Talk AI at Heart Watch of the Week Amplify The Best of 2024 AI at Play Watch History Office Ladies Podcast Don't Freak Out All Series Elisha Sauers writes about space for Mashable, taking deep dives into NASA's moon and Mars missions, chatting up astronauts and history-making discoverers, and jetting above the clouds . Through 17 years of reporting, she's covered a variety of topics, including health, business, and government, with a penchant for public records requests. She previously worked for in Norfolk, Virginia, and in Annapolis, Maryland. Her work has earned numerous state awards, including the Virginia Press Association's top honor, Best in Show, and national recognition for narrative storytelling. For each year she has covered space, Sauers has won National Headliner Awards, including first place for her Sex in Space series.

We present a systematic theoretical framework that interprets masked diffusion models (MDMs) as solutions to energy minimization problems in discrete optimal transport. Specifically, we prove that three distinct energy formulationskinetic, conditional kinetic, and geodesic energyare mathematically equivalent under the structure of MDMs, and that MDMs minimize all three when the mask schedule satisfies a closed-form optimality condition. This unification not only clarifies the theoretical foundations of MDMs, but also motivates practical improvements in sampling. By parameterizing interpolation schedules via Beta distributions, we reduce the schedule design space to a tractable 2D search, enabling efficient post-training tuning without model modification. Experiments on synthetic and real-world benchmarks demonstrate that our energy-inspired schedules outperform hand-crafted baselines, particularly in low-step sampling settings.

Caitlyn Jenner biographer and Robin Riker's ex William Hasley found dead on hiking trail at 78 Disgraceful texts'hot' teacher sent boy, 17, who she had illegal sex with where she moaned about her HUSBAND Everyone always said I cleared my throat a lot. But then I developed shoulder pain and doctors discovered the sinister cause... the world's deadliest cancer. Don't leave it too late like I did Urgent recall for 1.1m vehicles over fears they could spontaneously CATCH FIRE even when parked Leaked transcript of UNAIRED 60 Minutes interview exposes REAL reason'callous' CBS star Scott Pelley'deserved to be fired' Karmelo Anthony's parents seen leaving the courtroom in tears just before son's defense team pulls shock move'Great' mom, 32, tried to gas herself and her three young kids to death after inviting them to'popcorn sleepover' in car, prosecutors allege The porn-fuelled fantasy middle-class husbands are desperate to try with their wives... and it almost always ends in divorce: JANA HOCKING The historic steel mill that helped build America was written off for dead. Medical student, 24, died by suicide in his white coat a day after he was suspended for alleged'inappropriate' behavior towards female patient, lawsuit alleges, as his heartbreaking goodbye note to parents is revealed Furious dad films his partner in bed with his 19-year-old son: You've seen the viral video - now all three tell the Daily Mail what REALLY happened in the scandal gripping Australia Woke Vegas school compared boy to racist cross burner over pro-ICE stickers and expelled him... but did not punish pro-migrant students for class walkout, lawsuit alleges Gaming influencer Alex Cimo dies'very suddenly' aged 32 just a month after'refusing to accept his fate' Mother's final words before she was shot dead'by new husband' in front of her two young children All the backstage gossip from Miami Swim Week: Insider exposes'catty' VIP's diva demands... STEALING... and'morbidly embarrassing' celeb moment everyone is whispering about They said their toothpaste was the best for my daughter... then I read the sickening claims I am the type of mom who reads every label before buying a product for my four-year-old daughter. So when I learned about a lawsuit against a toothpaste marketed as safe, natural and free from artificial dyes and sweeteners, I immediately checked the tube sitting in my bathroom.

Amidst the recent strides in evaluating Large Language Models for Code (Code LLMs), existing benchmarks have mainly focused on the functional correctness of generated code, neglecting the importance of their computational efficiency. To fill the gap, we present Mercury, the first code efficiency benchmark for Code LLMs. It comprises 1,889 Python tasks, each accompanied by adequate solutions that serve as real-world efficiency baselines, enabling a comprehensive analysis of the runtime distribution. Based on the distribution, we introduce a new metric Beyond, which computes a runtime-percentile-weighted Pass score to reflect functional correctness and code efficiency simultaneously. On Mercury, leading Code LLMs can achieve 65% on Pass, while less than 50% on Beyond. Given that an ideal Beyond score would be aligned with the Pass score, it indicates that while Code LLMs exhibit impressive capabilities in generating functionally correct code, there remains a notable gap in their efficiency. Finally, our empirical experiments reveal that Direct Preference Optimization (DPO) serves as a robust baseline for enhancing code efficiency compared with Supervised Fine Tuning (SFT), which paves a promising avenue for future exploration of efficient code generation. Our code and data are available on GitHub: https://github.com/Elfsong/Mercury.

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Amidst therecent strides inevaluating LargeLanguage Models forCode (Code LLMs), existing benchmarks havemainly focused onthefunctional correctness of generated code, neglecting the importance of their computational efficiency.

We study reasoning tasks through a framework that integrates auto-regressive (AR) and non-autoregressive (NAR) language models. AR models, which generate text sequentially, excel at producing coherent outputs but often suffer from slow inference, particularly in reasoning-intensive domains such as mathematics and code, where lengthy chains of thought are required. In contrast, NAR models, such as discrete diffusion models, allow parallel generation and offer substantial speedups, though typically at the cost of reduced output quality. To address these limitations, we introduce a new paradigm in which an NAR model efficiently produces intermediate reasoning traces, which subsequently guide an AR model to deliver precise final answers. Experiments demonstrate that our approach yields significant 26% improvements over strong baselines while substantially reducing inference cost.