Cyber threat intelligence (CTI) is central to modern cybersecurity, providing critical insights for detecting and mitigating evolving threats. With the natural language understanding and reasoning capabilities of large language models (LLMs), there is increasing interest in applying them to CTI, which calls for benchmarks that can rigorously evaluate their performance. Several early efforts have studied LLMs on some CTI tasks but remain limited: (i) they adopt only closed-book settings, relying on parametric knowledge without leveraging CTI knowledge bases; (ii) they cover only a narrow set of tasks, lacking a systematic view of the CTI landscape; and (iii) they restrict evaluation to single-source analysis, unlike realistic scenarios that require reasoning across multiple sources. To fill these gaps, we present CTIArena, the first benchmark for evaluating LLM performance on heterogeneous, multi-source CTI under knowledge-augmented settings. CTIArena spans three categories, structured, unstructured, and hybrid, further divided into nine tasks that capture the breadth of CTI analysis in modern security operations. We evaluate ten widely used LLMs and find that most struggle in closed-book setups but show noticeable gains when augmented with security-specific knowledge through our designed retrieval-augmented techniques. These findings highlight the limitations of general-purpose LLMs and the need for domain-tailored techniques to fully unlock their potential for CTI.

Recent advances in large language models (LLMs) have enabled human-like social simulations at unprecedented scale and fidelity, offering new opportunities for computational social science. A key challenge, however, is the construction of persona sets that authentically represent the diversity and distribution of real-world populations. Most existing LLM-based social simulation studies focus primarily on designing agentic frameworks and simulation environments, often overlooking the complexities of persona generation and the potential biases introduced by unrepresentative persona sets. In this paper, we propose a systematic framework for synthesizing high-quality, population-aligned persona sets for LLM-driven social simulation. Our approach begins by leveraging LLMs to generate narrative personas from long-term social media data, followed by rigorous quality assessment to filter out low-fidelity profiles. We then apply importance sampling to achieve global alignment with reference psychometric distributions, such as the Big Five personality traits. To address the needs of specific simulation contexts, we further introduce a task-specific module that adapts the globally aligned persona set to targeted subpopulations. Extensive experiments demonstrate that our method significantly reduces population-level bias and enables accurate, flexible social simulation for a wide range of research and policy applications.

John Scalzi is a voluble man. He is the author of several New York Times best sellers and has been nominated for nearly every major award that the science-fiction industry has to offer--some of which he's won multiple times. Over the course of his career, he has written millions of words, filling dozens of books and 27 years' worth of posts on his personal blog. All of this is to say that if one wants to cite Scalzi, there is no shortage of material. But this month, the author noticed something odd: He was being quoted as saying things he'd never said.

The Trump administration's Federal Trade Commission has removed four years worth of business guidance blogs as of Tuesday morning, including important consumer protection information related to artificial intelligence and the agency's landmark privacy lawsuits under former chair Lina Khan against companies like Amazon and Microsoft. More than 300 blogs were removed. On the FTC's website, the page hosting all of the agency's business-related blogs and guidance no longer includes any information published during former president Joe Biden's administration, current and former FTC employees, who spoke under anonymity for fear of retaliation, tell WIRED. These blogs contained advice from the FTC on how big tech companies could avoid violating consumer protection laws. One now deleted blog, titled "Hey, Alexa! What are you doing with my data?" explains how, according to two FTC complaints, Amazon and its Ring security camera products allegedly leveraged sensitive consumer data to train the ecommerce giant's algorithms.

Whether you're managing your own business, running a blog, or working in e-commerce, creating content that's accurate and actually enjoyable to read is such a struggle. AI tools can be helpful, but they're often hit-or-miss when it comes to factual accuracy or meeting SEO standards. Katteb is a new SEO-friendly content generator that adds some reliability to that classic AI efficiency and does it without a huge new monthly bill. Instead of a recurring subscription, you can get Katteb for life for 79.99 (reg. With Katteb, you can create everything from long-form articles to Amazon product reviews in just a few clicks.

OpenAI published receipts, in the form of a long timeline of emails, texts and legal filings, illustrating that Elon Musk's injunction to prevent OpenAI from converting into a for-profit company runs counter to what he wanted in 2017. Essentially, OpenAI is providing even more evidence to the fact that its former co-founder wanted the AI startup to become a for-profit company and make him CEO. You should read the whole blog to get all of the details (and get a sense for how billionaires email) but the gist is that in 2017, Musk and OpenAI came to an understanding that the then non-profit needed to become a for-profit to "advance its mission" and seemingly capitalize on the public interest earned from its AI beating professional Dota 2 players in one-on-one matches. According to OpenAI, Musk proposed a new board structure where he "would unequivocally have initial control of the company," which OpenAI was opposed to. That led to the disagreements between Musk and OpenAI leadership, and him ultimately leaving the nonprofit's board in 2018.

I never thought I'd say this but … I cheated on you, ChatGPT, and Gemini. It was just the right thing to do. But hear me out: I was paying two separate subscription fees to get help writing and generating images for my blog when I heard about this all-in-one AI tool, 1minAI. Everyone was talking about how it does everything ChatGPT and Gemini do, but more. I didn't believe it because I'd never heard of it.

Waymo has raised another huge chunk of change from investors. The company announced on its blog that it secured an "oversubscribed investment round" of 5.6 billion in funding, the largest of which came from Google's parent company Alphabet. The company is working with Uber to expand to Austin and Atlanta by the early part of next year. Waymo says it plans to use this latest infusion of capital for the expansions. This latest round brings Waymo's total capital fundraising to 11.1 billion, with the 5.5 billion it picked up in two earlier rounds in 2020 and 2021.

The night after their son Mats died aged just 25, Trude and Robert Steen sat on the sofa in their living room in Oslo with their daughter Mia. "Everything was a blur," remembers Trude of that day 10 years ago. "Then Robert said, 'Maybe we should reach out to Mats' friends in World of Warcraft.'" Mats was born with Duchenne muscular dystrophy, a progressive condition that causes the muscles to weaken gradually. He was diagnosed aged four and started using a wheelchair at 10.

I made a controversial decision recently: I cheated on ChatGPT and Gemini. It was scary, but it was the right thing to do. I was paying two separate subscription fees to get help writing and generating images for my blog when I heard about this all-in-one AI tool, 1minAI. They said it could do everything ChatGPT and Gemini do, and more. I didn't believe it because I'd never heard of it.