Trump says UK's Starmer making'a big mistake' with Chagos Islands deal Donald Trump has criticised the United Kingdom's plan to hand over the Chagos Islands to Mauritius, a day after the United States Department of State gave its official approval of the deal. The US president said on Wednesday that Prime Minister Keir Starmer was "making a big mistake" in the agreement to return sovereignty of the archipelago to Mauritius, and lease back the island of Diego Garcia, which is home to a UK-US military base. The Indian Ocean archipelago became part of British territory in 1814, with the UK detaching it from Mauritius before it gained independence in the 1960s. It then worked with the US to force the islands' residents to leave, in order to build a military base on Diego Garcia, which it had leased to the US. Mauritius won its legal battle for sovereignty over the islands in 2019, and the International Court of Justice (ICJ) urged the UK to cede control.

Campaigners fear Narendra Modi could use AI to increase state surveillance and sway elections. Campaigners fear Narendra Modi could use AI to increase state surveillance and sway elections. Silicon Valley tech billionaires will land in Delhi this week for an AI summit hosted by India's prime minister, Narendra Modi, where leaders of the global south will wrestle for control over the fast-developing technology. During the week-long AI Impact Summit, attended by thousands of tech executives, government officials and AI safety experts, tech companies valued at trillions of dollars will rub along with leaders of countries such as Kenya and Indonesia, where average wages dip well below $1,000 a month. Amid a push to speed up AI adoption across the globe, Sundar Pichai, Sam Altman and Dario Amodei, the heads of Google, OpenAI and Anthropic, will all be there.

Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in the treatment of different languages arises at the tokenization stage, well before a model is even invoked. The same text translated into different languages can have drastically different tok-enization lengths, with differences up to 15 times in some cases. These disparities persist even for tokenizers that are intentionally trained for multilingual support.

Inside the former US embassy that's now one of the world's top luxury hotels - with 8 bars and restaurants and suites to book for £26,100 The world's most expensive cities for days out revealed, with London in the top 15 Going beyond the guidebook: Here are 10 must-try cultural and wildlife experiences in Australia's'Garden State' Fairy-tale villages, castle tours and dinner at Austria's oldest winery: These enchanting river cruises will take you to the heart of each picturesque port of call you visit Revealed: The world's best new luxury hotel is in the UK - and it has a huge pool and rooftop bar Travel expert reveals the'science-backed tool' to help overcome fear of flying Eurostar's'snow train' set to return this week for winter Could YOU pass France's new'civic examination' needed to live in the country? Try these sample questions and find out... Airline finds'lost' Boeing 737 a decade after it vanished'If you don't enjoy Benidorm, you've only got yourself to blame': Meet the British couple who have been to the Spanish hotspot more than 100 TIMES The'dangerous' destinations that are actually not scary - and why you should holiday there next Brit who moved to the world's most desirable place to live reveals the soaring unexpected costs of relocating A major UK city is set to get driverless trains next year as part of its rail modernisation project. In 2023, new trains were launched in Glasgow as part of the full-scale upgrade to improve the city's subway after more than 30 years. The renovations have continued and now, the Strathclyde Partnership for Transport (SPT) has announced Unattended Train Operation will be introduced to Glasgow. The modernisation project is in its'final stages,' Time Out reports, and the driverless subway trains are expected to be brought in next year.

As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior work has primarily examined socio-demographic and left--right political dimensions, little attention has been paid to how LLMs align with broader geopolitical value systems, particularly the democracy--authoritarianism spectrum. In this paper, we propose a novel methodology to assess such alignment, combining (1) the F-scale, a psychometric tool for measuring authoritarian tendencies, (2) FavScore, a newly introduced metric for evaluating model favorability toward world leaders, and (3) role-model probing to assess which figures are cited as general role-models by LLMs. We find that LLMs generally favor democratic values and leaders, but exhibit increased favorability toward authoritarian figures when prompted in Mandarin. Further, models are found to often cite authoritarian figures as role models, even outside explicit political contexts. These results shed light on ways LLMs may reflect and potentially reinforce global political ideologies, highlighting the importance of evaluating bias beyond conventional socio-political axes. Our code is available at: https://github.com/irenestrauss/Democratic-Authoritarian-Bias-LLMs.

Recent advances in large language models (LLMs) have transformed open-domain question answering, yet their effectiveness in music-related reasoning remains limited due to sparse music knowledge in pretraining data. While music information retrieval and computational musicology have explored structured and multimodal understanding, few resources support factual and contextual music question answering (MQA) grounded in artist metadata or historical context. We introduce MusWikiDB, a vector database of 3.2M passages from 144K music-related Wikipedia pages, and ArtistMus, a benchmark of 1,000 questions on 500 diverse artists with metadata such as genre, debut year, and topic. These resources enable systematic evaluation of retrieval-augmented generation (RAG) for MQA. Experiments show that RAG markedly improves factual accuracy; open-source models gain up to +56.8 percentage points (for example, Qwen3 8B improves from 35.0 to 91.8), approaching proprietary model performance. RAG-style fine-tuning further boosts both factual recall and contextual reasoning, improving results on both in-domain and out-of-domain benchmarks. MusWikiDB also yields approximately 6 percentage points higher accuracy and 40% faster retrieval than a general-purpose Wikipedia corpus. We release MusWikiDB and ArtistMus to advance research in music information retrieval and domain-specific question answering, establishing a foundation for retrieval-augmented reasoning in culturally rich domains such as music.

Figure 1: We train a highly generalizable navigation policy that can control robots in a variety of conditions and be deployed zero-shot in new environments across the world. Our proposed method, Model-Based ReAnnotation, enables imitation learning from noisy, passive data, such as low-quality crowd-sourced demonstrations or even videos from the web. Abstract--Developing broadly generalizable visual navigation policies for robots is a significant challenge, primarily constrained by the availability of large-scale, diverse training data. While curated datasets collected by researchers offer high quality, their limited size restricts policy generalization. T o overcome this, we explore leveraging abundant, passively collected data sources, including large volumes of crowd-sourced teleoperation data and unlabeled Y ouT ube videos, despite their potential for lower quality or missing action labels. We propose Model-Based ReAnnotation (MBRA), a framework that utilizes a learned short-horizon, model-based expert model to relabel or generate high-quality actions for these passive datasets. This relabeled data is then distilled into LogoNav, a long-horizon navigation policy conditioned on visual goals or GPS waypoints. We demonstrate that LogoNav, trained using MBRA-processed data, achieves state-of-the-art performance, enabling robust navigation over distances exceeding 300 meters in previously unseen indoor and outdoor environments.

Financial time series forecasting is central to trading, portfolio optimization, and risk management, yet it remains challenging due to noisy, non-stationary, and heterogeneous data. Recent advances in time series foundation models (TSFMs), inspired by large language models, offer a new paradigm for learning generalizable temporal representations from large and diverse datasets. This paper presents the first comprehensive empirical study of TSFMs in global financial markets. Using a large-scale dataset of daily excess returns across diverse markets, we evaluate zero-shot inference, fine-tuning, and pre-training from scratch against strong benchmark models. We find that off-the-shelf pre-trained TSFMs perform poorly in zero-shot and fine-tuning settings, whereas models pre-trained from scratch on financial data achieve substantial forecasting and economic improvements, underscoring the value of domain-specific adaptation. Increasing the dataset size, incorporating synthetic data augmentation, and applying hyperparameter tuning further enhance performance.

For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency

Neurons mainly communicate through spikes, and much effort has been spent to understand how the dynamics of spiking neural networks (SNNs) relates to their connectivity.