Ukraine is preparing to present a revised peace plan to the White House, as it seeks to avoid making territorial concessions to Russia. Kyiv is set propose alternatives to the US after President Volodymyr Zelensky again ruled out surrendering land, saying he had no right to do so under Ukrainian or international law. He made the comments as he met European and Nato leaders on Monday, part of a collective push to deter the US from backing a peace deal which includes major concessions for Ukraine, and which allies fear would leave it vulnerable to a future invasion. Meanwhile, the city of Sumy in north-western Ukraine was left without power overnight after a Russian drone attack. The region's governor said more than a dozen drones had hit power infrastructure, the latest in Russia's nightly attacks.

About 40% of 13-to 17-year-olds in England and Wales affected by youth violence are turning to AI chatbots for mental health support. About 40% of 13-to 17-year-olds in England and Wales affected by youth violence are turning to AI chatbots for mental health support. 'I feel it's a friend': quarter of teenagers turn to AI chatbots for mental health support It was after one friend was shot and another stabbed, both fatally, that Shan asked ChatGPT for help. She had tried conventional mental health services but "chat", as she came to know her AI "friend", felt safer, less intimidating and, crucially, more available when it came to handling the trauma from the deaths of her young friends. As she started consulting the AI model, the Tottenham teenager joined about 40% of 13-to 17-year-olds in England and Wales affected by youth violence who are turning to AI chatbots for mental health support, according to research among more than 11,000 young people.

What are the implications of Trump's Somali'garbage' comments? What happens if the US attacks Venezuela? Does'America First' make the US weaker? What we know about the DC pipe bomb suspect Brian Cole Jr. US President Donald Trump has cleared the way for tech giant Nvidia to sell its advanced H200 chip to China, in a significant easing of Washington's export controls targeting Chinese tech. Trump said on Monday that he had informed Chinese President Xi Jinping of the decision to allow the export of the chip under an arrangement that will see 25 percent of sales paid to the US government.

US President Donald Trump has announced that he will allow AI chip giant Nvidia to sell its advanced H200 chips to approved customers in China. We will protect National Security, create American Jobs, and keep America's lead in AI, Trump said on social media on Monday. The decision will apply to other US chip companies like AMD and comes after extensive lobbying by Nvidia boss Jensen Huang, who visited Washington last week to drum up support. Nvidia - both the world's leading chip firm and most valuable company - has found itself at the centre of a geopolitical tug-of-war between the US and China in recent months, and had been banned from selling its most advanced chips to Beijing. Trump reversed the chip-selling ban in July, but demanded that Nvidia pay 15% of its Chinese revenues to the US government. Beijing then reportedly ordered its tech companies to stop buying Nvidia chips manufactured for use in the Chinese market.

Sudan's air force has carried out bombings in which at least 1,700 civilians have died in attacks on residential neighbourhoods, markets, schools and camps for displaced people, according to an investigation into air raids in the country's civil war. The Sudan Witness Project says it has compiled the largest known dataset of military airstrikes in the conflict, which began in April 2023. Its analysis indicates that the air force has used unguided bombs in populated areas. The data focuses on attacks by warplanes, which only the Sudanese Armed Forces (SAF) is capable of operating. Its rival, the paramilitary Rapid Support Forces (RSF) does not have aircraft.

While reinforcement learning has advanced the alignment of text-to-image (T2I) models, state-of-the-art policy gradient methods are still hampered by training instability and high variance, hindering convergence speed and compromising image quality. Our analysis identifies a key cause of this instability: disproportionate credit assignment, in which the mathematical structure of the generative sampler produces volatile and non-proportional feedback across timesteps. To address this, we introduce Proportionate Credit Policy Optimization (PCPO), a framework that enforces proportional credit assignment through a stable objective reformulation and a principled reweighting of timesteps. This correction stabilizes the training process, leading to significantly accelerated convergence and superior image quality. The improvement in quality is a direct result of mitigating model collapse, a common failure mode in recursive training. PCPO substantially outperforms existing policy gradient baselines on all fronts, including the state-of-the-art DanceGRPO. Code is available at https://github.com/jaylee2000/pcpo/.

Fault-tolerant quantum computing will require error rates far below those achievable with physical qubits. Quantum error correction (QEC) bridges this gap, but depends on decoders being simultaneously fast, accurate, and scalable. This combination of requirements has not yet been met by a machine-learning decoder, nor by any decoder for promising resource-efficient codes such as the colour code. Here we introduce AlphaQubit 2, a neural-network decoder that achieves near-optimal logical error rates for both surface and colour codes at large scales under realistic noise. For the colour code, it is orders of magnitude faster than other high-accuracy decoders. For the surface code, we demonstrate real-time decoding faster than 1 microsecond per cycle up to distance 11 on current commercial accelerators with better accuracy than leading real-time decoders. These results support the practical application of a wider class of promising QEC codes, and establish a credible path towards high-accuracy, real-time neural decoding at the scales required for fault-tolerant quantum computation.

XX 1 Disturbance Compensation for Safe Kinematic Control of Robotic Systems with Closed Architecture Fan Zhang 1,2, Jinfeng Chen 1, Joseph J. B. Mvogo Ahanda 3, Hanz Richter 4, Ge Lv 5, Bin Hu 1,2, Qin Lin 1,2 Abstract--In commercial robotic systems, it is common to encounter a closed inner-loop (low-level) torque controller that is not user-modifiable. However, the outer-loop controller, which sends kinematic commands such as position or velocity for the inner-loop controller to track, is typically exposed to users. In this work, we focus on the development of an easily integrated add-on at the outer-loop layer by combining disturbance rejection control and robust control barrier function for high-performance tracking and safe control of the whole dynamic system of an industrial manipulator . This is particularly beneficial when 1) the inner-loop controller is imperfect, unmodifiable, and uncertain; and 2) the dynamic model exhibits significant uncertainty. Stability analysis, formal safety guarantee proof, simulations, and hardware experiments with a PUMA robotic manipulator are presented. Our solution demonstrates superior performance in terms of simplicity of implementation, robustness, tracking precision, and safety compared to the state of the art. I. INTRODUCTION Robotic systems often employ hierarchical software design, stacking perception, decision-making, planning, and low-level control. Such modularity is particularly beneficial for troubleshooting and improving the reliability of robotic systems. For example, in the control block, a combination of a kinematic controller (outer-loop controller) and a dynamic controller (inner-loop controller) is commonly seen in various robots. However, because tuning the inner-loop controller requires expert knowledge, this component is typically not exposed to users due to product safety considerations, a practice referred to as closed architecture in the literature [1]-[4]. In other words, users are only allowed to design the kinematic controller, sending position or velocity for the inner-loop controller to track. Additionally, mechanical parts 1 The authors are with the Department of Engineering Technology, University of Houston, USA. Corresponding author: Qin Lin, qlin21@central.uh.edu 2 Fan Zhang is also with the Department of Electrical and Computer Engineering, University of Houston, USA 3 Joseph Jean Baptiste Mvogo Ahanda is with the Department of Biomedical Engineering, The University of Ebolowa, Cameroon 4 Hanz Richter is with the Department of Mechanical Engineering, Cleveland State University, USA 5 Ge Lv is with the Department of Mechanical Engineering, Clemson University, USA. This material is based upon work supported by the National Science Foundation under Grant Nos.

Why do modern language models, trained to do well on next-word prediction, appear to generate coherent documents and capture long-range structure? Here we show that next-token prediction is provably powerful for learning longer-range structure, even with common neural network architectures. Specifically, we prove that optimizing next-token prediction over a Recurrent Neural Network (RNN) yields a model that closely approximates the training distribution: for held-out documents sampled from the training distribution, no algorithm of bounded description length limited to examining the next $k$ tokens, for any $k$, can distinguish between $k$ consecutive tokens of such documents and $k$ tokens generated by the learned language model following the same prefix. We provide polynomial bounds (in $k$, independent of the document length) on the model size needed to achieve such $k$-token indistinguishability, offering a complexity-theoretic explanation for the long-range coherence observed in practice.

Conformal prediction provides a pivotal and flexible technique for uncertainty quantification by constructing prediction sets with a predefined coverage rate. Many online conformal prediction methods have been developed to address data distribution shifts in fully adversarial environments, resulting in overly conservative prediction sets. We propose Conformal Optimistic Prediction (COP), an online conformal prediction algorithm incorporating underlying data pattern into the update rule. Through estimated cumulative distribution function of non-conformity scores, COP produces tighter prediction sets when predictable pattern exists, while retaining valid coverage guarantees even when estimates are inaccurate. We establish a joint bound on coverage and regret, which further confirms the validity of our approach. We also prove that COP achieves distribution-free, finite-sample coverage under arbitrary learning rates and can converge when scores are $i.i.d.$. The experimental results also show that COP can achieve valid coverage and construct shorter prediction intervals than other baselines.