U.S. Open follow modal Personalized Content 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?

Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Trending Now Mashable Selects Say More Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook In My Bag AI at School Safety Net All Series Platforms such as YouTube and Twitch typically prohibit violence, but there are some exceptions. Amanda Yeo is an Assistant Editor at Mashable, covering entertainment, culture, tech, science, and social good. Based in Australia, she writes about everything from video games and K-pop to movies and gadgets. The U.S. government is planning to livestream the Fort Hood shooter's execution by firing squad. Secretary of War Pete Hegseth revealed the plan on Thursday, which would revive public executions in the U.S. for the first time in 90 years.

Christa Pike's prognosis still unclear after failed execution, lawyer says US murderer Christa Pike remains critically ill in hospital and her prognosis is still unclear following her failed execution, her lawyer has said on Sunday. Speaking to CNN on Sunday morning, Pike's lawyer Stephen Ferrell said doctors were still trying to clear two doses of pentobarbital from her body. Pike, 50, was sentenced to death following her conviction for murdering Colleen Slemmer in 1995. She survived two execution attempts on Wednesday, and was transferred to a hospital in Nashville, Tennessee. Ferrell said: She's still in the hospital, on a ventilator, critically ill, we really don't know the prognosis yet, and may not know for several days.

U.S. Open follow modal Personalized Content 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? Smart Alerts: Get notified about major news as it happens.

Britain is in talks with EU partners to release additional strategic fuel reserves, including diesel, if needed, the BBC understands. The talks come after President Donald Trump threatened to ban diesel exports from the US in a bid to reduce record prices at the pumps for Americans. Diesel prices in the UK also hit record highs this week and are hovering just under 200p per litre, according to motoring organisation the RAC. The US is a vital supplier of diesel to the world, exporting between 1.2 and 1.5 million barrels per day, with experts warning a ban on shipments abroad could put further pressure on prices in other countries. We're thinking about it very seriously, Trump said of an export ban at the weekend.

The ultimate goal of code agents is to solve complex tasks autonomously. Although large language models (LLMs) have made substantial progress in code generation, real-world tasks typically demand full-fledged code repositories rather than simple scripts. Building such repositories from scratch remains a major challenge. Fortunately, GitHub hosts a vast, evolving collection of open-source repositories, which developers frequently reuse as modular components for complex tasks. Yet, existing frameworks like OpenHands and SWE-Agent still struggle to effectively leverage these valuable resources.

We resolve a 30-year-old open problem concerning the power of unlabeled data in online learning by tightly quantifying the gap between transductive and standard online learning. In the standard setting, the optimal mistake bound is characterized by the Littlestone dimension dof the concept class H(Littlestone, 1987). We prove that in the transductive setting, the mistake bound is at least Ω d . This constitutes an exponential improvement over previous lower bounds of Ω(loglog(d)), Ω p log(d), and Ω(log(d)), due respectively to Ben-David, Kushilevitz, and Mansour (1995, 1997), and Hanneke, Moran, and Shafer (2023). We also show that this lower bound is tight: for every d, there exists a class of Littlestone dimension d with transductive mistake bound O d . Our upper bound also improves upon the best known upper bound of (2/3) d from Ben-David et al. (1997). These results establish a quadratic gap between transductive and standard online learning, thereby highlighting the benefit of advance access to the unlabeled instance sequence. This contrasts with the PAC setting, where transductive and standard learning exhibit similar sample complexities.

Data duplication within large-scale corpora often impedes large language models' (LLMs) performance and privacy. In privacy-concerned federated learning scenarios, conventional deduplication methods typically rely on trusted third parties to perform uniform deletion, risking loss of informative samples while introducing privacy vulnerabilities. To address these gaps, we propose Federated ReWeighting (FedRW), the first privacy-preserving framework, to the best of our knowledge, that performs soft deduplication via sample reweighting instead of deletion in federated LLM training, without assuming a trusted third party. At its core, FedRW proposes a secure, frequency-aware reweighting protocol through secure multi-party computation, coupled with a parallel orchestration strategy to ensure efficiency and scalability. During training, FedRW utilizes an adaptive reweighting mechanism with global sample frequencies to adjust individual loss contributions, effectively improving generalization and robustness. Empirical results demonstrate that FedRW outperforms the state-of-the-art method by achieving up to 28.78 speedup in preprocessing and approximately 11.42% improvement in perplexity, while offering enhanced security guarantees. FedRW thus establishes a new paradigm for managing duplication in federated LLM training.

Unmanned Aerial Vehicles (UAVs) are evolving into language-interactive platforms, enabling more intuitive forms of human-drone interaction. While prior works have primarily focused on high-level planning and long-horizon navigation, we shift attention to language-guided fine-grained trajectory control, where UAVs execute short-range, reactive flight behaviors in response to language instructions. We formalize this problem as the Flying-on-a-Word (Flow) task and introduce UAV imitation learning as an effective approach. In this framework, UAVs learn fine-grained control policies by mimicking eUAxpert pilotVtrajectoriesFlopaired withwatomic Fly around the tree ahead Land on the left side of carlanguage instructions. To support this paradigm, we present UAV-Flow, the firstreal-world benchmark for language-conditioned, fine-grained UAV control.

Large reasoning models (LRMs) excel at complex reasoning tasks but typically generate lengthy sequential chains-of-thought, resulting in long inference times before arriving at the final answer. To address this challenge, we introduce SPRINT, a novel post-training and inference-time framework designed to enable LRMs to dynamically identify and exploit opportunities for parallelization during their reasoning process. SPRINT incorporates an innovative data curation pipeline that reorganizes natural language reasoning trajectories into structured rounds of longhorizon planning and parallel execution. By fine-tuning LRMs on a small amount of such curated data, the models learn to dynamically identify independent subtasks within extended reasoning processes and effectively execute them in parallel. Through extensive evaluations, we demonstrate that models fine-tuned with the SPRINT framework match the performance of reasoning models on complex domains such as mathematics while generating up to 39% fewer sequential tokens on problems requiring more than 8,000 output tokens. Finally, we observe consistent results transferred to two out-of-distribution tasks, namely GPQA and Countdown, with up to 45% and 65% reduction in average sequential tokens respectively for longer reasoning trajectories, while matching the performance of the fine-tuned reasoning model.