Three ISIL-linked Australian women have been arrested at airports in Melbourne and Sydney based on allegations of slavery and terrorism. They were amongst four women and nine children arriving from Syria after spending years in al-Roj Camp near the border between Syria and Iraq. Iran's president says he has spoken to the country's Supreme Leader US court releases alleged Jeffrey Epstein'suicide note'

Dating Is a Rich Person's Game Now People actually can't afford to date anymore. Ask just about anyone what's wrong with modern dating and they will likely tell you the same thing: The apps suck. They're built on a pay-to-win model. Fewer people are finding quality partners. Some studies have even suggested that increased time on them leads to higher depression and anxiety while also contributing to loneliness among men .

ChatGPT's new privacy policy states how the company uses cookies for tracking, to turn free users into paying subscribers. OpenAI is ready to target free users of its services with advertisements around the web, based on what it knows about them. On Thursday, OpenAI sent an email to users laying out major changes to the AI company's privacy policy in the US. "We'll now use cookies to promote OpenAI products and services on other websites," reads the email sent on April 30. "This does not impact your conversations in ChatGPT. Your conversations with ChatGPT are private and are not shared with marketing partners."

The exploit, dubbed CopyFail and tracked as CVE-2026-31431, allows hackers to take over PCs and data center servers. The Linux vulnerabilities have been patched--but many machines remain at risk. Publicly released exploit code for an effectively unpatched vulnerability that gives root access to virtually all releases of Linux is setting off alarm bells as defenders scramble to ward off severe compromises inside data centers and on personal devices. The vulnerability and exploit code that exploits it were released Wednesday evening by researchers from security firm Theori, five weeks after privately disclosing it to the Linux kernel security team. The critical flaw, tracked as CVE-2026-31431 and the name CopyFail, is a local privilege escalation, a vulnerability class that allows unprivileged users to elevate themselves to administrators.

OpenAI is rolling out Advanced Account Security for people concerned that their ChatGPT or Codex accounts could be potential targets of phishing attacks. For anyone who fears their ChatGPT and Codex accounts might be targeted by attackers, OpenAI announced on Thursday that it is adding an optional new level of account protection that adds an extra layer of security. Dubbed Advanced Account Security, the feature enforces strict access controls that would make account takeover attacks very difficult. Such measures are not a new idea in the realm of account security. Google, for example, has offered its Advanced Protection account security tier for nearly a decade . But as mainstream AI services rapidly proliferate around the world, there is a pressing need for an array of basic protections to be put in place.

One group of hackers used AI for everything from vibe coding their malware to creating fake company websites--and stole as much as $12 million in three months. The advent of AI hacking tools has raised fears of a near future in which anyone can use automated tools to dig up exploitable vulnerabilities in any piece of software, like a kind of digital intrusion superpower. Here in the present, however, AI seems to be playing a more mundane, if still concerning, role in hackers' toolkit: It's helping mediocre hackers level up and carry out broad, effective malware campaigns. That includes one group of relatively unskilled North Korean cybercriminals who've been discovered using AI to carry out virtually every part of an operation that hacked thousands of victims to steal their cryptocurrency. On Wednesday, cybersecurity firm Expel revealed what it describes as a North Korean state-sponsored cybercrime operation that installed credential-stealing malware on more than 2,000 computers, specifically targeting the machines of developers working on small cryptocurrency launches, NFT creation, and Web3 projects.

Mozilla Used Anthropic's Mythos to Find and Fix 271 Bugs in Firefox The Firefox team doesn't think emerging AI capabilities will upend cybersecurity long term, but they warn that software developers are likely in for a rocky transition. Amid a raging debate over the impact that new AI models will have on cybersecurity, Mozilla said on Tuesday that its Firefox 150 browser release this week includes protections for 271 vulnerabilities identified using early access to Anthropic's Mythos Preview . The Firefox team says that it has taken resources and discipline to adjust to the firehose of bugs that new AI tools can uncover, but that this big lift is necessary for the security of Mozilla's users, given that the capabilities will inevitably be in attackers' hands soon. Both Anthropic and OpenAI have announced new AI models in recent weeks that the companies say have advanced cybersecurity capabilities that could represent a turning point in how defenders--and, crucially, attackers--find vulnerabilities and misconfigurations in software systems. With this in mind, the companies have so far only done limited private releases of their new models, and both have also convened industry working groups meant to assess the advances and strategize.

The sequential nature of autoregressive next-token prediction imposes a fundamental speed limit on large language models. While continuous flow models offer a path to parallel generation, they traditionally demand expensive iterative integration. Flow Maps bypass this bottleneck by compressing generative trajectories into single-step mappings, theoretically enabling the generation of full text sequences from noise in a single forward pass. However, standard formulations rely on Euclidean regression losses that are geometrically ill-suited for discrete data. In this work, we resolve this conflict with Discrete Flow Maps, a framework that reconciles trajectory compression with the geometry of the probability simplex. We recast standard flow map training for the discrete domain, aligning the training dynamics with the discrete nature of language. Empirically, this strict geometric alignment allows our method to surpass previous state-of-the-art results in discrete flow modeling.

Quasi-experimental evaluations are central for generating real-world causal evidence and complementing insights from randomized trials. The regression discontinuity design (RDD) is a quasi-experimental design that can be used to estimate the causal effect of treatments that are assigned based on a running variable crossing a threshold. Such threshold-based rules are ubiquitous in healthcare, where predictive and prognostic biomarkers frequently guide treatment decisions. However, standard RD estimators rely on complete outcome data, an assumption often violated in time-to-event analyses where censoring arises from loss to follow-up. To address this issue, we propose a nonparametric approach that leverages doubly robust censoring corrections and can be paired with existing RD estimators. Our approach can handle multiple survival endpoints, long follow-up times, and covariate-dependent variation in survival and censoring. We discuss the relevance of our approach across multiple areas of applications and demonstrate its usefulness through simulations and the prostate component of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial where our new approach offers several advantages, including higher efficiency and robustness to misspecification. We have also developed an open-source software package, $\texttt{rdsurvival}$, for the $\texttt{R}$ language.

We introduce Generalized Discrete Diffusion from Snapshots (GDDS), a unified framework for discrete diffusion modeling that supports arbitrary noising processes over large discrete state spaces. Our formulation encompasses all existing discrete diffusion approaches, while allowing significantly greater flexibility in the choice of corruption dynamics. The forward noising process relies on uniformization and enables fast arbitrary corruption. For the reverse process, we derive a simple evidence lower bound (ELBO) based on snapshot latents, instead of the entire noising path, that allows efficient training of standard generative modeling architectures with clear probabilistic interpretation. Our experiments on large-vocabulary discrete generation tasks suggest that the proposed framework outperforms existing discrete diffusion methods in terms of training efficiency and generation quality, and beats autoregressive models for the first time at this scale. We provide the code along with a blog post on the project page : \href{https://oussamazekri.fr/gdds}{https://oussamazekri.fr/gdds}.