On macOS Tahoe 26 or later, go to Apple menu > System Settings > General > Software Update . Click the More Info button next to Automatic Updates and make sure Install system data files and security updates is turned on. If your Mac runs Sonoma or Sequoia, look for Safari 26.5.2 in Software Update as well. That Safari update may be the protection your Mac needs if you are not on Tahoe.

Trump declares deal with Iran is DEAD after'scum' opened fire on tankers: Oil prices surge Trump's pick for Florida governor accused of ambushing mom in gourmet grocery store: Watch him vow to'crush' her as aide screams Trump's inner circle have shown me the real UFO disclosure: The president has an imminent speech'written and ready'... what he'll explain about non-human life will make it all make sense Group chats of Travis Kelce's Chiefs teammates explode with's**t talk' about Taylor Swift wedding... as groom's snubbed friends vent their fury and players take sides: 'WTF?!' Actress who starred with Robert Redford and has famous daughter spotted out in LA... can you guess who she is? English King Alfred who massacred thousands of Vikings is'found buried' under Hampshire car park days before England play Norway in World Cup USA star at the heart of World Cup controversy offers groveling apology for team's failure: 'Not good enough' Bombshell first details about Taylor Swift's wedding dress: Stunning off-the-shoulder design revealed... incredible '25ft train'... and shock SECOND outfit Ford wrongly accused a worker of stealing a $1.95 cookie and fired him - then BEGGED him to return to work MTG brands Mitch McConnell's wife a'communist spy' as she flees to China during terrifying hospitalization Middle-aged man with America's WORST table manners sparks fury by chewing with his mouth open and hurling food on floor at bagel bakery Everyone's missed something so utterly humiliating about Taylor and Travis's wedding... I can't help but scream it: JANA HOCKING Sick twist in horrific case of youth pastor who pushed wife off cliff: Friends and family reveal chilling new details his final phone call hours before suicide... and insult from beyond the grave The humanoid robot designed'for a lifetime': China reveals creepy bots that look and feel like real humans - and they can even reproduce 90% of our movements China has revealed a new generation of creepy humanoid robots that are designed for a'lifetime' of companionship. At an event in the Chinese tech hub of Shenzhen, UBTech Robotics launched the world's first mass-produced ultra-realistic humanoid robots. These Uworld U1 androids are covered with'biomimetic skin' that looks and feels just like that of a real human.

The Chinese supercomputer LineShine was ranked as the fastest in the world, despite not using any GPUs. China now has the world's fastest supercomputer, overtaking the United States. The system, known as LineShine and installed at the National Supercomputing Center in Shenzhen, displaced the US system El Capitan from the top spot in the TOP500 ranking in terms of computing power. The breakthrough comes amid an intense competition between Beijing and Washington for technological supremacy, marked by high tariffs and restrictions on a wide range of hardware components and software. Since 1993, the TOP500 ranking has identified the world's most powerful supercomputers every six months through a series of standardized benchmarks that evaluate each system's performance, taking into account both its theoretical speed and its real-world performance, as well as its energy efficiency.

China beats U.S. with world's fastest supercomputer, but race not geared for AI work Workers at Elon Musk's xAI facility, which houses a large supercomputer known as Colossus, used for Artificial Intelligence (AI) data processing, in Memphis, Tennessee, on Sept. 11, 2025 | REUTERS SAN FRANCISCO - China has overtaken the U.S. to win the top spot on a list of the world's fastest supercomputers, but the results may say more about Beijing's desire to show self-sufficiency in computing systems than its standing in the global AI race, experts said. The LineShine system at the National Supercomputing Center in Shenzhen, China, uses domestically designed chips and won the top spot on the TOP500, a biannual global ranking of supercomputers, with the country's first listing in three years. The ranking comes as the U.S. and China are increasingly competing in advanced computing, with U.S. President Donald Trump on Monday signing an executive order that aims to put the U.S. ahead of China in the emerging field of quantum computing. In the June 2026 edition of TOP500, LineShine beat out the previous titleholder, El Capitan, a supercomputer housed at Lawrence Livermore National Laboratory that the U.S. government uses to develop and maintain its nuclear weapons stockpile. But technology and policy experts said the results do not mean that China has the world's fastest computer for AI work because of changes in the computing industry in recent years and the methods used to compile the list.

Large Vision-Language Models (LVLMs) have exhibited remarkable progress. However, deficiencies remain compared to human intelligence, such as hallucination and shallow pattern matching. In this work, we aim to evaluate a fundamental yet underexplored intelligence: association, a cornerstone of human cognition for creative thinking and knowledge integration. Current benchmarks, often limited to closed-ended tasks, fail to capture the complexity of open-ended association reasoning vital for real-world applications. To address this, we present MMOPERA, a systematic benchmark with 11,497 instances across two open-ended tasks: Remote-Item Association (RIA) and In-Context Association (ICA), aligning association intelligence evaluation with human psychometric principles. It challenges LVLMs to resemble the spirit of divergent thinking and convergent associative reasoning through free-form responses and explicit reasoning paths. We deploy tailored LLM-as-a-Judge strategies to evaluate open-ended outputs, applying process-reward-informed judgment to dissect reasoning with precision. Extensive empirical studies on state-of-the-art LVLMs, including sensitivity analysis of task instances, validity analysis of LLM-as-a-Judge strategies, and diversity analysis across abilities, domains, languages, cultures, etc., provide a comprehensive and nuanced understanding of the limitations of current LVLMs in associative reasoning, paving the way for more human-like and general-purpose AI.

Poison-only Clean-label Backdoor Attacks (PCBAs) aim to covertly inject attackerdesired behavior into DNNs by merely poisoning the dataset without changing the labels. To effectively implant a backdoor, multiple triggers are proposed for various attack requirements of Attack Success Rate (ASR) and stealthiness. Additionally, sample selection enhances clean-label backdoor attacks' ASR by meticulously selecting "hard" samples instead of random samples to poison. Current methods, however, 1) usually handle the sample selection and triggers in isolation, leading to limited performance on both ASR and stealthiness when converted to PCBAs. Therefore, we seek to explore the bi-directional collaborative relations between the sample selection and triggers to address the above dilemma.

Plug-and-play (PnP) methods with deep denoisers have shown impressive results in imaging problems. They typically require strong convexity or smoothness of the fidelity term and a (residual) non-expansive denoiser for convergence. These assumptions, however, are violated in Poisson inverse problems, and non-expansiveness can hinder denoising performance. To address these challenges, we propose a cocoercive conservative (CoCo) denoiser, which may be (residual) expansive, leading to improved denoising performance. By leveraging the generalized Helmholtz decomposition, we introduce a novel training strategy that combines Hamiltonian regularization to promote conservativeness and spectral regularization to encourage cocoerciveness. We prove that CoCo denoiser is a proximal operator of a weakly convex function, enabling a restoration model with an implicit weakly convex prior. The global convergence of PnP methods to a stationary point of this restoration model is established. Extensive experimental results demonstrate that our approach outperforms closely related methods in both visual quality and quantitative metrics. A test code is provided for reproducibility2.

However, existing online methods face challenge in prohibitive storage requirements primarily due to point-wise modeling that fails to exploit the motion properties. To address this limitation, we propose a novel Compact Gaussian Streaming (ComGS) framework, leveraging the locality and consistency of motion in dynamic scene, that models object-consistent Gaussian point motion through keypoint-driven motion representation. By transmitting only the keypoint attributes, this framework provides a more storage-efficient solution. Specifically, we first identify a sparse set of motion-sensitive keypoints localized within motion regions using a viewspace gradient difference strategy. Equipped with these keypoints, we propose an adaptive motion-driven mechanism that predicts a spatial influence field for propagating keypoint motion to neighboring Gaussian points with similar motion. Moreover, ComGS adopts an error-aware correction strategy for key frame reconstruction that selectively refines erroneous regions and mitigates error accumulation without unnecessary overhead. Overall, ComGS achieves a remarkable storage reduction of over 159 compared to 3DGStream and 14 compared to the SOTA method QUEEN, while maintaining competitive visual fidelity and rendering speed.

Unlike reasoning, which often entails a deep sequence of deductive steps, complex real-world planning is characterized by the need to synthesize a broad spectrum of parallel and potentially conflicting information and constraints. For example, in travel planning scenarios, it requires the integration of diverse real-world information and user preferences.

LLM-based agents have been widely applied as personal assistants, capable of memorizing information from user messages and responding to personal queries. However, there still lacks an objective and automatic evaluation on their memory capability, largely due to the challenges in constructing reliable questions and answers (QAs) according to user messages. In this paper, we propose MemSim, a Bayesian simulator designed to automatically construct reliable QAs from generated user messages, simultaneously keeping their diversity and scalability. Specifically, we introduce the Bayesian Relation Network (BRNet) and a causal generation mechanism to mitigate the impact of LLM hallucinations on factual information, facilitating the automatic creation of an evaluation dataset. Based on MemSim, we generate a dataset in the daily-life scenario, named MemDaily, and conduct extensive experiments to assess the effectiveness of our approach. We also provide a benchmark for evaluating different memory mechanisms in LLM-based agents with the MemDaily dataset.