College football's chaos era is here: Texas rallies, Oregon falls and Lane Kiffin eyes return to Ole Miss America's women's basketball epic reaches its climax with national unity on the line in FIBA World Cup final Oklahoma State stuns Oregon, deals early season blow to Ducks' national title hopes Former ESPN host hypocritically calls network a'political lobbying firm' after Ted Cruz appearance Team USA survives Spain scare with fourth-quarter surge to reach FIBA Women's World Cup final It's only week two, but Kansas QB Isaiah Marshall might have completed the pass of the year WWE's Roxanne Perez earns No 1 contender spot for AAA Reina de Reinas Championship at Triplemania 34 Ted Cruz got booed so loudly during'College GameDay' interview you couldn't hear a thing he said Trespasser stopped by security at Kamala Harris' Malibu property Kyrsten Sinema: Trump has been'tremendous' on AI data centers Charles Payne: America voted for the'reindustrialization' of the nation Charles Payne: America voted for the'reindustrialization' of the nation Israeli ambassador to US: It's always been Israel, and always will be Israel Charles Payne praises'phenomenal' blue-collar boom under Trump Ret Col John Folsom outlines Dunham House's mission for combat-wounded veterans Ret Col John Folsom outlines Dunham House's mission for combat-wounded veterans The NFL'S kicking game has become too predictable and needs a serious shake-up The New England Patriots lost 13-10 to the Seattle Seahawks, and Drake Maye is facing extreme criticism after throwing 3 INTs in the 4th quarter. Nick Wright, Chris Broussard, Kevin Wildes, and Danny Parkins ask if the Seahawks can break the long-... It's time to fix the kick in the NFL. With the Chief's Harrison Butker's 69-yard field goal in the pre-season, it was demonstrated yet again that today's kickers are better than ever. Year after year they kick it higher, further, and straighter. Usually, the placekicker is the leading scorer on the team, but nobody cares because the kicking game has become boring and predictable.

Back to School Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Say More Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Switch Off Mashable Voices Safety Net Versus All Series A surprising ruling on AI CSAM doesn't mean what you think. Rebecca Ruiz is a Senior Reporter at Mashable. She frequently covers mental health, digital culture, and technology. Her areas of expertise include suicide prevention, screen use and mental health, parenting, youth well-being, and meditation and mindfulness. Rebecca's experience prior to Mashable includes working as a staff writer, reporter, and editor at NBC News Digital and as a staff writer at Forbes.

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The site, called Stud or Dud, helps daters dig up dirt on potential paramours. It's fueled by the same public data as PeopleFinders.com--and

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'Possession' review: A chilling, powerful horror series with a lot to say Colonial violence haunts this powerful Gugu Mbatha-Raw-led series set across Britain and Jamaica. Shannon Connellan is Mashable's Senior Editor, General Assignments, based in London. She has been Mashable's UK Editor (and still manages the illustrious UK team) and Australia Editor, but emotionally, she lives searching for . A Tomatometer-approved critic, Shannon writes about entertainment, tech, social good, science, culture, and Australian horror, and loves to nerd out with movie stars, filmmakers, and TV creators . All products featured here are independently selected by our editors and writers.

'What if it's the mess in therapy that is its most prized possession?' 'What if it's the mess in therapy that is its most prized possession?' My patients use ChatGPT for therapy. "Chat told me I should break up with him." I instructed my face to remain therapist-neutral, but I must have smirked. The truth is, I was annoyed. We had been discussing the viability of this relationship for weeks, and in an instant AI had brought the answer.

Invasion team sports such as soccer produce a high-dimensional, strongly coupled state space as many players continuously interact on a shared field, challenging quantitative tactical analysis. Traditional rule-based analyses are intuitive, while modern predictive machine learning models often perform pattern-matching without explicit agent representations. The problem we address is how to build player-level agent models from data, whose learned values and policies are both tactically interpretable and robust across heterogeneous data sources. Here, we propose Expandable Decision-Making States (EDMS), a semantically enriched state representation that augments raw positions and velocities with relational variables (e.g., scoring of space, pass, and score), combined with an action-masking scheme that gives on-ball and off-ball agents distinct decision sets. Compared to prior work, EDMS maps learned value functions and action policies to human-interpretable tactical concepts (e.g., marking pressure, passing lanes, ball accessibility) instead of raw coordinate features, and aligns agent choices with the rules of play. In the experiments, EDMS with action masking consistently reduced both action-prediction loss and temporal-difference (TD) error compared to the baseline. Qualitative case studies and Q-value visualizations further indicate that EDMS highlights high-risk, high-reward tactical patterns (e.g., fast counterattacks and defensive breakthroughs). We also integrated our approach into an open-source library and demonstrated compatibility with multiple commercial and open datasets, enabling cross-provider evaluation and reproducible experiments.

In just seven minutes, the thieves took off with crown jewels containing with thousands of diamonds along with other precious gems. Police stand outside the Louvre after a brazen theft. Could the French TV series have been prophetic? The show envisioned a heist at the Louvre, an event that became reality on the morning of October 19, when a group of professional thieves managed to break into the world-famous Paris museum . In just seven minutes, they stole a host of priceless French crown jewels.

The art of ordering and culling your possessions before you die should extend to your documents, photos, and digital accounts. Digital generated image of semi transparent multiple data server discs on white background. After Adam Liljenberg's grandmother died, his grandfather was ready to downsize and move into an assisted living facility. As Swedes, they were familiar with Swedish death cleaning, the idea that as you near the end of life, you declutter and organize your belongings so as not to burden those who survive you. When Liljenberg arrived to help his grandfather sort through his possessions, he didn't expect to be rescuing digital photos off a phone full of malware.

Recent years have witnessed a growing interest in personalizing the responses of large language models (LLMs). While existing evaluations primarily focus on whether a response aligns with a user's preferences, we argue that factuality is an equally important yet often overlooked dimension. In the context of personalization, we define a model as robust if its responses are both factually accurate and align with the user preferences. To assess this, we introduce PERG, a scalable framework for evaluating robustness in LLMs, along with a new dataset, PERGData. We evaluate fourteen models from five different model families using different prompting methods. Our findings show that current LLMs struggle with robust personalization: even the strongest models (GPT-4.1, LLaMA3-70B) fail to maintain correctness in 5% of previously successful cases without personalization, while smaller models (e.g., 7B-scale) can fail more than 20% of the time. Further analysis reveals that robustness is significantly affected by the nature of the query and the type of user preference. To mitigate these failures, we propose Pref-Aligner, a two-stage approach that improves robustness by an average of 25% across models. Our work highlights critical gaps in current evaluation practices and introduces tools and metrics to support more reliable, user-aligned LLM deployments.