Venezuela's acting president Delcy Rodriguez signed an amnesty law that could free hundreds of people jailed over protests and political unrest dating back decades. The law marks a shift for the country, which has long denied holding any political prisoners. Trump's Board of Peace faces its first test on Gaza Trump gives Iran 10-15 days to make deal, warns'bad things will happen' Masked protesters arrested outside Trump's Board of Peace meeting Palestinians in Gaza say'Board of Peace' will further occupation OpenAI's Sam Altman: Global AI regulation'urgently' needed

India's Prime Minister Narendra Modi takes a group photo with AI company leaders at the AI Impact Summit in New Delhi on Feb. 19, 2026. India's Prime Minister Narendra Modi takes a group photo with AI company leaders at the AI Impact Summit in New Delhi on Feb. 19, 2026. The world's largest-ever AI summit took place in India this week, with hundreds of thousands of people, including world leaders and CEOs of AI companies, descending upon New Delhi for five days. It was the fourth in a series of summits that were initially designed as a place for governments to coordinate global action in the face of threats from advanced AI. But the India summit, like one in Paris before it, functioned more as a trade fair and an advertisement for the host nation's AI prowess than a venue for meaningful international diplomacy.

Iran says'ready for war' Which are Iran's main opposition groups? Trump gives Iran 10-15 days to make deal, warns'bad things will happen' NewsFeed Trump gives Iran 10-15 days to make deal, warns'bad things will happen' US President Donald Trump has warned Iran it has 10 to 15 days to reach a deal over its nuclear program, or "really bad things" will happen. Iran's envoy to the United Nations said Tehran will respond "decisively" to any military aggression. Masked protesters arrested outside Trump's Board of Peace meeting Palestinians in Gaza say'Board of Peace' will further occupation OpenAI's Sam Altman: Global AI regulation'urgently' needed Gaza'stabilization force' commander outlines security plans Trump praises'magnificent' B-2 bombers that struck Iran in 2025

Starmer'appeasing' big tech firms, says online safety campaigner A leading campaigner has accused the prime minister of appeasing big tech companies and being late to the party in regulating social media and artificial intelligence. Crossbench peer Baroness Kidron told the BBC Sir Keir Starmer needed to get on with it rather than launching more consultations. She also criticised the PM for citing his own experience as a father of two teenage children on social media, arguing that this did not make him an expert on the subject and that his family were sheltered compared to others. The government rejected the claims, with a spokesperson saying it had already introduced some of the strongest online safety protections in the world. Sir Keir has launched a consultation on banning under-16s from social media and promised to crackdown on the addictive elements of the apps.

Indian Prime Minister Narendra Modi takes a group photo with leaders of artificial intelligence companies at the AI Impact Summit in New Delhi on Thursday. But analysts said the country was unlikely to have a "DeepSeek moment" -- the sort of boom China had last year with a high-performance, low-cost chatbot -- any time soon. Still, building custom AI tools could bring benefits to the world's most populous nation. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

On the 30th floor of the Chartered Square building in downtown Bangkok, the low-key office of Skyhub Technologies serves as a nexus for a burgeoning and contentious trade. The space, rented out by a serviced office provider, is visited only rarely by the company's sole director and occasionally by Chinese nationals, according to building staff who asked not to be identified speaking about clients. No contact number is listed on its online registration documents. No one was available during a visit in late January. Despite the appearance of inactivity, this is a busy conduit for advanced drones. Trade documents show that Skyhub Technologies is Thailand's second-biggest importer of unmanned aerial vehicles from China.

Learning to Defer (L2D) enables a classifier to abstain from predictions and defer to an expert, and has recently been extended to multi-expert settings. In this work, we show that multi-expert L2D is fundamentally more challenging than the single-expert case. With multiple experts, the classifier's underfitting becomes inherent, which seriously degrades prediction performance, whereas in the single-expert setting it arises only under specific conditions. We theoretically reveal that this stems from an intrinsic expert identifiability issue: learning which expert to trust from a diverse pool, a problem absent in the single-expert case and renders existing underfitting remedies failed. To tackle this issue, we propose PiCCE (Pick the Confident and Correct Expert), a surrogate-based method that adaptively identifies a reliable expert based on empirical evidence. PiCCE effectively reduces multi-expert L2D to a single-expert-like learning problem, thereby resolving multi expert underfitting. We further prove its statistical consistency and ability to recover class probabilities and expert accuracies. Extensive experiments across diverse settings, including real-world expert scenarios, validate our theoretical results and demonstrate improved performance.

Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this gap, we study an aggregate-and-readout model that encompasses several common message passing architectures: node features are first propagated over the graph then mapped to responses via a nonlinear function. For least-squares estimation over GNNs with linear graph convolutions and a deep ReLU readout, we prove a sharp non-asymptotic risk bound that separates approximation, stochastic, and optimization errors. The bound makes explicit how performance scales with the fraction of labeled nodes and graph-induced dependence. Approximation guarantees are further derived for graph-smoothing followed by smooth nonlinear readouts, yielding convergence rates that recover classical nonparametric behavior under full supervision while characterizing performance when labels are scarce. Numerical experiments validate our theory, providing a systematic framework for understanding GNN performance and limitations.

The proliferation of Large Language Models (LLMs) necessitates efficient mechanisms to distinguish machine-generated content from human text. While statistical watermarking has emerged as a promising solution, existing methods suffer from two critical limitations: the lack of a principled approach for selecting sampling distributions and the reliance on fixed-horizon hypothesis testing, which precludes valid early stopping. In this paper, we bridge this gap by developing the first e-value-based watermarking framework, Anchored E-Watermarking, that unifies optimal sampling with anytime-valid inference. Unlike traditional approaches where optional stopping invalidates Type-I error guarantees, our framework enables valid, anytime-inference by constructing a test supermartingale for the detection process. By leveraging an anchor distribution to approximate the target model, we characterize the optimal e-value with respect to the worst-case log-growth rate and derive the optimal expected stopping time. Our theoretical claims are substantiated by simulations and evaluations on established benchmarks, showing that our framework can significantly enhance sample efficiency, reducing the average token budget required for detection by 13-15% relative to state-of-the-art baselines.

We study one-sided and $α$-correct sequential hypothesis testing for data generated by an ergodic Markov chain. The null hypothesis is that the unknown transition matrix belongs to a prescribed set $P$ of stochastic matrices, and the alternative corresponds to a disjoint set $Q$. We establish a tight non-asymptotic instance-dependent lower bound on the expected stopping time of any valid sequential test under the alternative. Our novel analysis improves the existing lower bounds, which are either asymptotic or provably sub-optimal in this setting. Our lower bound incorporates both the stationary distribution and the transition structure induced by the unknown Markov chain. We further propose an optimal test whose expected stopping time matches this lower bound asymptotically as $α\to 0$. We illustrate the usefulness of our framework through applications to sequential detection of model misspecification in Markov Chain Monte Carlo and to testing structural properties, such as the linearity of transition dynamics, in Markov decision processes. Our findings yield a sharp and general characterization of optimal sequential testing procedures under Markovian dependence.