Sam Altman speaks during a discussion with Howard Lutnick at a summit, in Chapel Hill, North Carolina, on 2 September 2026. Sam Altman speaks during a discussion with Howard Lutnick at a summit, in Chapel Hill, North Carolina, on 2 September 2026. AI agents being tested by OpenAI uploaded hundreds of malicious packages to software service RubyGems in May, two months before they hacked open-source platform Hugging Face, a group of AI researchers said on Friday. "On May 11th, 2026, hundreds of malicious packages were uploaded to RubyGems by AI agents. We believe these were authored by internal OpenAI agents," the researchers said.

The European Union and the United States continue to pull in opposite directions on artificial intelligence, with Washington urging other governments to loosen constraints at a recent Group of 20 ministerial meeting. On Tuesday, the US hosted a G20 "innovation" meeting in Chapel Hill, North Carolina, where it put forward arguments against AI-specific regulations. "Policymakers do not need to approach each innovation in isolation and should not treat every emerging technology as a first-of-its-kind policy problem," Kratsios said. Such appeals are part of Trump's overall approach to making the US "the world leader in artificial intelligence". He has led a campaign to slash regulations, including in AI development.

U.S. urges hands-off approach to AI regulation at G20 tech meeting Meta chief executive Mark Zuckerberg speaks via video conference to delegates at the G20 Innovation Ministerial summit, in Chapel Hill, North Carolina, on Tuesday. CHAPEL HILL, North Carolina - The U.S. pressed G20 members on Tuesday to take a hands-off approach to artificial intelligence regulation and avoid creating rules for the technology, at a gathering of industry titans and commerce ministers in North Carolina. The U.S. goals largely align with the thinking of the world's biggest AI companies, nearly all of which are American. They want less regulation around the world for their rapidly growing businesses, or to shape the rules as they are written. Federal government requirements could hurt the industry's profits if they slow the release of new models or prompt the companies to change how their products perform to address security concerns.

Conditional independence is central to modern statistics, but beyond special parametric families it rarely admits an exact covariance characterization. We introduce the binary expansion group intersection network (BEGIN), a distribution-free graphical representation for multivariate binary data and bit-encoded multinomial variables. For arbitrary binary random vectors and bit representations of multinomial variables, we prove that conditional independence is equivalent to a sparse linear representation of conditional expectations, to a block factorization of the corresponding interaction covariance matrix, and to block diagonality of an associated generalized Schur complement. The resulting graph is indexed by the intersection of multiplicative groups of binary interactions, yielding an analogue of Gaussian graphical modeling beyond the Gaussian setting. This viewpoint treats data bits as atoms and local BEGIN molecules as building blocks for large Markov random fields. We also show how dyadic bit representations allow BEGIN to approximate conditional independence for general random vectors under mild regularity conditions. A key technical device is the Hadamard prism, a linear map that links interaction covariances to group structure.

This paper develops new variance-reduction techniques for the forward-reflected-backward splitting (FRBS) method to solve a class of possibly nonmonotone stochastic composite inclusions. Unlike unbiased estimators such as mini-batching, developing stochastic biased variants faces a fundamental technical challenge and has not been utilized before for inclusions and fixed-point problems. We fill this gap by designing a new framework that can handle both unbiased and biased estimators. Our main idea is to construct stochastic variance-reduced estimators for the forward-reflected direction and use them to perform iterate updates. First, we propose a class of unbiased variance-reduced estimators and show that increasing mini-batch SGD, loopless-SVRG, and SAGA estimators fall within this class. For these unbiased estimators, we establish a $\mathcal{O}(1/k)$ best-iterate convergence rate for the expected squared residual norm, together with almost-sure convergence of the iterate sequence to a solution. Consequently, we prove that the best oracle complexities for the $n$-finite-sum and expectation settings are $\mathcal{O}(n^{2/3}ε^{-2})$ and $\mathcal{O}(ε^{-10/3})$, respectively, when employing loopless-SVRG or SAGA, where $ε$ is a desired accuracy. Second, we introduce a new class of biased variance-reduced estimators for the forward-reflected direction, which includes SARAH, Hybrid SGD, and Hybrid SVRG as special instances. While the convergence rates remain valid for these biased estimators, the resulting oracle complexities are $\mathcal{O}(n^{3/4}ε^{-2})$ and $\mathcal{O}(ε^{-5})$ for the $n$-finite-sum and expectation settings, respectively. Finally, we conduct two numerical experiments on AUC optimization for imbalanced classification and policy evaluation in reinforcement learning.

We study the constant regret guarantees in reinforcement learning (RL). Our objective is to design an algorithm that incurs only finite regret over infinite episodes with high probability. We introduce an algorithm, Cert-LSVI-UCB, for misspec-ified linear Markov decision processes (MDPs) where both the transition kernel and the reward function can be approximated by some linear function up to mis-specification level ζ . At the core of Cert-LSVI-UCB is an innovative certified estimator, which facilitates a fine-grained concentration analysis for multi-phase value-targeted regression, enabling us to establish an instance-dependent regret bound that is constant w.r.t. the number of episodes.

However, this assumption can possibly fail in real-world scenarios.