Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Getting Technical with Mashable Mashable Selects Gift Ideas For Everyone On Your List Say More Versus Trending Now In My Bag Mashable @Home Top creators, ranked Creator Playbook All Series Spotify's QR code checks could complicate your shared subscription -- and make listening more expensive. Olivia Tauber is the deputy editor of digital culture, covering creators, media, movies, beauty, and more. Based in New York, her work has appeared in The New York Times, Vanity Fair, The Cut, Teen Vogue, Complex, and Interview Magazine. She holds a Master's degree in Journalism from NYU and a Bachelor's from the University of Michigan. She also runs Fan Mail, a weekly pop-culture newsletter.

To keep scammers at bay, dating apps are embracing biometric scanning tools and “verified human” badges. These moves are sparking concerns about surveillance and privacy.

When you purchase through links in our articles, we may earn a small commission. The actual teen protections are more complicated. I'm not a parent, but I'm looking at the Meta settlement wondering if our kids are any safer. If you haven't heard already, Meta agreed last Wednesday to settle a lawsuit over Facebook and Instagram's affect on children. Brought by 47 states, Washington D.C., and several U.S. territories, the suit alleged that Meta purposely made its platforms addictive to kids, ultimately causing negative mental health outcomes like anxiety, depression, and even suicide.

Florent Delgrange won the Best Blue Sky Paper Award at AAMAS 2026 for his work Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments . We caught up with him to find out more about his vision for agent learning. What is the topic of your Blue Sky Ideas paper and why is it an interesting area for study? My Blue Sky Ideas paper asks a simple but difficult question: how can an autonomous agent keep learning as its world changes without quietly losing the guarantees that made its behavior trustworthy? Reinforcement learning and formal methods address complementary parts of this problem.

The communications minister, Anika Wells, had said the report showed'many effective options' for online age verification in implementing Australia's social media ban. The communications minister, Anika Wells, had said the report showed'many effective options' for online age verification in implementing Australia's social media ban. Report supporting Australia's teen social media ban appears to contain AI hallucinations, Senate hears The authors of a report testing the technology underpinning Australia's social media ban have conceded ChatGPT was used in editing, but denied a number of citation errors in the report were due to AI hallucinations. The $3.48m age assurance technology trial, run by the UK-based Age Check Certification Scheme (ACCS) last year, tested various types of technology that could be used by social media platforms as part of Australia's under-16 social media ban. The communications minister, Anika Wells, heralded the report as showing "many effective options" for checking people's ages, and it paved the way for the ban to come into effect in December last year.

Anthropic's Claude AI chatbot may require some users to upload government-issued ID, passport, or driver's license starting July 8th for account verification. PCWorld reports this identity verification aims to prevent fraud and ensure terms of service compliance, handled by third-party service Persona. Similar ID verification policies by Discord, Reddit, and ChatGPT previously faced significant user backlash over privacy concerns. Anthropic has updated its privacy policy for its AI chatbot Claude. Starting July 8th, the company may--in certain situations--require users to verify their age or identity by uploading a copy of a government-issued ID card, passport, or driving license, reports TechCrunch .

Global society makes billions of images and uploads hundreds of thousands of hours of video on the internet every day. The problem is, some of this content is misleading or downright wrong. And when it's in visual form, it can be particularly convincing . Take the Met Gala that happened earlier this month in New York. While photographers snapped photos of Rhianna, Beyoncé and Nicole Kidman as they strutted their stuff, others saw "photos" of celebrities, such as Rosalía, Lady Gaga and Jacob Elordi, who were actually elsewhere (the images in the below Instagram carousel are AI generated).

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Large Language Models (LLMs) have demonstrated advanced capabilities in realworld agentic applications. Growing research efforts aim to develop LLM-based agents to address practical demands, introducing a new challenge: agentic scenarios often involve lengthy instructions with complex constraints, such as extended system prompts and detailed tool specifications. While adherence to such instructions is crucial for agentic applications, whether LLMs can reliably follow them remains underexplored. In this paper, we introduce AGENTIF, the first benchmark for systematically evaluating LLM instruction following ability in agentic scenarios. AGENTIF features three key characteristics: (1) Realistic, constructed from 50 real-world agentic applications.

Ensembles of generative large language models (LLMs) are a promising way to compensate for individual model limitations, integrating the strengths of different LLMs. Existing LLM ensemble methods, however, face limitations such as first-token delay and challenges in long-range semantic collaboration between models, Moreover, they typically assume equal voting weights for all models during ensemble, ignoring task-specific performance differences among models. In this work, we propose SpecEM, a training-free, plug-and-play LLM ensemble framework that dynamically adjusts each model's model contribution in real time based on task performance. Inspired by speculative decoding, SpecEM iteratively performs drafting and verification, allowing models to collaborate semantically at the segment level for integrated output. Furthermore, we introduce an online feedback mechanism with multiplicative weight updates, where each model's voting weight is adjusted on-the-fly according to how often it outperforms others during verification stage, ensuring that stronger models exert greater influence during ensembling. Experimental results on five LLM families (ranging from 7B to 72B parameters) and six benchmark datasets, spanning open-domain instruction following, reasoning, commonsense, demonstrate consistent performance improvements compared to state-of-the-art LLM ensemble methods.