Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Find love for the summer -- or forever. Anna Iovine is the associate editor of features at Mashable. Previously, as the sex and relationships reporter, she covered topics ranging from dating apps to pelvic pain. Before Mashable, Anna was a social editor at VICE and freelanced for publications such as Slate and the Columbia Journalism Review. Follow her on Bluesky . Bethany Allard is a Los Angeles-based shopping reporter at Mashable covering beauty tech, dating, sex and relationships, and headphones. That basically means she puts her hair through a lot, scrolls through a lot of dating apps, and rotates through a lot of different headphones. In addition to testing out and rounding up the best products, she also covers deals for Mashable, paying an especially obsessive amount of attention to Apple deals and prices. That knowledge comes in handy when she's covering shopping holidays like Prime Day and Black Friday, which she's now done for three years at Mashable. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School.

Many domain experts do not have the time or expertise to write formal Bayesian models. This paper takes an informal problem description as input, and combines a large language model and a probabilistic programming language to define a joint distribution over formal models, latent variables, and data. A posterior over latent variables follows by conditioning on observed data and integrating over formal models. This presents a challenging inference problem. We suggest an inference recipe that amounts to generating many formal models from the large language model, performing approximate inference on each, and then doing a weighted average. This is justified and analyzed as a combination of self-normalized importance sampling, MCMC, and importance-weighted variational inference. Experimentally, this produces sensible predictions from only data and an informal problem description, without the need to specify a formal model.

Large language models often improve reasoning by sampling multiple outputs and aggregating their final answers, but precise and efficient control of error levels remains a challenging task. In particular, deciding when to stop sampling remains difficult when the stopping rule is data-dependent and the set of possible response labels is not known in advance. We study anytime-valid certification of a prespecified target answer as the unique mode of the model's response distribution, a guarantee distinct from answer correctness. We propose the Certification by Intersection-union Testing with Eprocesses (CITE) algorithm, which provably controls false certification at any prescribed level under arbitrary data-driven stopping, without requiring prior knowledge of the answer category set. We also prove a category-set-size-free stopping-time rate, establish matching minimax lower bounds up to constants in the main regime, and extend the construction to confidence-weighted voting. Simulations and LLM self-consistency experiments show empirical error control and improved certification in diffuse-tail settings.

How men were left to starve in a South African gold mine. This image was created by Mohamed Hussein using the artificial intelligence (AI) tool Midjourney. Ayanda Ndabeni watched the faint glow from his headlamp fight the vast darkness 1,500 metres (4,920 feet) below ground. His miner's lamp had lasted for more than a week after he was lowered down into the shaft of the gold mine. But now the batteries were dying. He gently flipped the plastic switch of his lamp, turning it off, and the trapped men around him became shadows. In the stifling heat and humidity, their anxiety pressed in from all sides. Ayanda had descended into Shaft 10 of the Buffelsfontein mine in late September 2024, lowered by a team of nearly 20 men operating ropes and a pulley above ground. That day, he'd spotted police vehicles near the mine's entrance. The 36-year-old assumed it was just routine patrols around the mine system, which is 2km (1.2 miles) deep. But then the rope pulley, via which food, water, batteries and other items arrived, stopped moving. The shouting that usually indicated the rope operators were sending down a man or supplies also fell silent. When huge rocks came crashing down the shaft, they knew it was a warning. The men whispered of their growing fears that something was very wrong on the surface. Patrick Ntsokolo was also in Shaft 10. He was a few hundred metres higher up than Ayanda and had arrived in late July. Patrick was new to the mines. Tasked by the leaders of the artisanal miners with collecting the food, water and alcohol lowered down by the rope pulley, he hauled supplies along the slippery tunnels to small shops.

Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Alexander brothers' alleged HIGH SCHOOL gang rape video: Classmates speak out on sick'taking turns' footage... as creepy unseen photos are exposed Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Live Nation executives mocked'stupid' concert-goers in emails where they bragged about how to best rip them off: '$60 for closer grass' NFL superstar Xavier Worthy spills all on Travis Kelce, the Chiefs' struggles... and having Taylor Swift as his No 1 fan 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 Nancy Mace throws herself into Iran warzone as she goes rogue on Middle East rescue mission: 'I AM that person' Hidden toxins in kids' treats EXPOSED: Health guru Jillian Michaels' sit-down with Casey DeSantis reveals dangers lurking in popular foods Interactive map reveals your nearest nuclear shelter and states that are MOST exposed... amid fears of US attack: Make an emergency plan now The fear of a nuclear apocalypse has reached levels not seen in decades as the US and Israel launch a deadly new conflict with Iran, raising alarms across capitals and prompting emergency diplomatic efforts to prevent a wider war. For Americans, the pressing question may soon shift from geopolitics to personal preparedness, including where the nearest fallout shelter is located and how to protect themselves if tensions escalate further. There is currently no public list of active shelters available for everyday Americans, since most are defunct or privately owned. But survival expert and Air Force veteran Sean Gold has built his own fallout shelter map, revealing that the vast majority of these radiation bunkers are scattered throughout America's largest cities. The map can be found on his survival guide website, TruePrepper .

Communication compression isacrucial technique formodern distributedlearning systems to alleviate their communication bottlenecks over slower networks.

Ornate medieval ring discovered in Norway's oldest town Scientists are still investigating if the ring's center stone is a sapphire or colored glass. Breakthroughs, discoveries, and DIY tips sent every weekday. Last summer, Linda Åsheim found a ring so beautiful it looks like it could have been made yesterday. But Åsheim is an archaeologist, and she found the rare artifact while excavating in a Norwegian town believed to be the oldest in the country. The gorgeous golden ring is decorated with a gemstone and filigree décor--and is over 800 years old.

Clinical communication is central to patient outcomes, yet large-scale human annotation of patient-provider conversation remains labor-intensive, inconsistent, and difficult to scale. Existing approaches based on large language models typically rely on single-task models that lack adaptability, interpretability, and reliability, especially when applied across various communication frameworks and clinical domains. In this study, we developed a Multi-framework Structured Agentic AI system for Clinical Communication (MOSAIC), built on a LangGraph-based architecture that orchestrates four core agents, including a Plan Agent for codebook selection and workflow planning, an Update Agent for maintaining up-to-date retrieval databases, a set of Annotation Agents that applies codebook-guided retrieval-augmented generation (RAG) with dynamic few-shot prompting, and a Verification Agent that provides consistency checks and feedback. To evaluate performance, we compared MOSAIC outputs against gold-standard annotations created by trained human coders. We developed and evaluated MOSAIC using 26 gold standard annotated transcripts for training and 50 transcripts for testing, spanning rheumatology and OB/GYN domains. On the test set, MOSAIC achieved an overall F1 score of 0.928. Performance was highest in the Rheumatology subset (F1 = 0.962) and strongest for Patient Behavior (e.g., patients asking questions, expressing preferences, or showing assertiveness). Ablations revealed that MOSAIC outperforms baseline benchmarking.

Large language models (LLMs) such as ChatGPT o1, ChatGPT o3, and DeepSeek R1 have shown great potential in solving difficult problems. However, current LLM evaluation benchmarks are limited to one-step interactions. Some of the existing sequence decision-making environments, such as TextStarCraftII and LLM-PySC2, are too complicated and require hours of interaction to complete a game. In this paper, we introduce LLM-Cave, a benchmark and light environment for LLM reasoning and decision-making systems. This environment is a classic instance in the era of Symbolism. Artificial intelligence enables the agent to explore the environment and avoid potential losses by reasoning about nearby dangers using partial observable state information. In the experiment, we evaluated the sequential reasoning ability, decision-making performance and computational efficiency of mainstream large language models (LLMs) such as GPT-4o-mini, o1-mini, and DeepSeek-R1. Experiments show that while Deepseek-R1 achieved the highest success rate on complex reasoning tasks, smaller models like 4o-mini significantly narrowed the performance gap on challenges by employing Chain of Speculation and Planner-Critic strategies, at the expense of reduced computational efficiency. This indicates that structured, multi-step reasoning combined with an LLM-based feedback mechanism can substantially enhance an LLM's decision-making capabilities, providing a promising direction for improving reasoning in weaker models and suggesting a new reasoning-centered benchmark for LLM assessment. Our code is open-sourced in https://github.com/puleya1277/CaveEnv.

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