Ukrainian drones have struck a historic museum in Russia-annexed Sevastopol in Crimea, igniting a roof fire, as Russian authorities slashed nighttime train schedules amid intensifying air attacks across the peninsula and deep into Russia. Sevastopol's Russian-installed governor, Mikhail Razvozhayev, announced the damage on Telegram early on Wednesday. "This building is not just a museum, it is a symbol of resilience, which has repeatedly taken the blows of the enemy." Razvozhayev said that during World War II's Siege of Sevastopol, "the Panorama building was subjected to massed bombing by German aviation". He declared: "The enemy will pay for this sacrilege!"

SoftBank's attempt to get $6 billion OpenAI margin loan stalls SoftBank Group's efforts to secure at least $6 billion through a margin loan backed by its OpenAI stake have stalled after the company lowered its fundraising target. SoftBank Group's talks with potential creditors to raise at least $6 billion from a margin loan backed by its OpenAI stake have stalled, people familiar with the matter said, just weeks after the Japanese conglomerate cut its initial target from $10 billion. The company is considering various fundraising options, according to the people, who asked not to be identified discussing private matters. It could still move forward with the margin loan at a later stage, they added. It's unclear why the margin loan discussions stalled. Borrowers and creditors can pause and revisit fundraising discussions for various reasons, and SoftBank hasn't elaborated on its plans, the people said.

Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks. Yet, population-level screening tools remain inadequate. We present Method, a machine-learning framework for NAFLD risk prediction coupling gradient-boosted decision trees with conformal prediction to yield calibrated, distribution-free coverage guarantees on individual risk estimates. It integrates a mutual-information-based stability selection procedure to identify a compact, clinically interpretable feature subset via bootstrap resampling, constructing prediction sets whose marginal coverage provably exceeds a user-specified confidence level. We evaluated Method on a multicenter cohort from Guangzhou, China (primary n=2,187; external validation n=412) using 78 candidate features across demographics, metabolic biomarkers, and lifestyle factors. Method achieves an AUROC of 0.912 internally and 0.891 externally, outperforming deep neural networks, TabNet, support vector machines, and logistic regression. Conformal prediction sets achieve 91.3% empirical coverage at the 90% nominal level. A three-tier risk stratification derived from these scores separates the population into distinct groups, with the high-risk subgroup showing a 12-month progression rate 4.7 times that of the low-risk tier. The selected features -- notably waist circumference, ALT, GGT, triglycerides, fasting glucose, and BMI -- align with established metabolic risk factors, providing biological plausibility.

Existing methods rely predominantly on token-level signals, leaving the geometric structure of intermediate hidden states underused. In this paper, we take the geometric complexity of hidden-state matrices as a measure of the global uncertainty of LLMs, while treating token-level uncertainty estimation as a local metric. We show that hidden-state geometric entropy (global uncertainty) and token-level entropy (local uncertainty) are statistically near-orthogonal, capturing distinct failure regimes for reliability prediction. In particular, global geometry recovers the confident-but-wrong failure mode that local signals systematically miss. Building on this, we propose Global-Local Uncertainty (GLU), an unsupervised, single-pass score that fuses the two signals via a multiplicative gate. Across three model families and six benchmarks, GLU matches or outperforms all unsupervised baselines while requiring only a single forward pass and remaining length-normalized and architecture-agnostic. Code is available on https://github.com/qcri/GLU.git.

Mixture-of-Experts large language models (LLMs) scale efficiently through sparse activation, yet their deployment is fundamentally constrained by the large static parameter footprint of experts. Existing compression approaches either remove entire experts, disrupting routing topology and harming performance, or rely on unstructured weight pruning with limited practical efficiency. To address the limitations, we propose TENP, a structured Trapezoidal ExpertNeuron Pruning framework. Using a few samples, we identify and retain important experts, while applying expert neuron pruning (ENP) to less important experts, reserving model parameters in a trapezoidal pattern from shallow to deep layers. When evaluating expert importance, we jointly consider both the magnitude of the expert output and its ability to change the direction of the input vector. For ENP, we measure each neuron's projected contribution to the expert output to identify and retain important neurons. We conduct extensive experiments on the Qwen and DeepSeek models. Under a routing expert sparsity of 40% and an average of 63.76% activated expert parameters, the DeepSeek model suffers only a 1-point drop in accuracy compared to the full-parameter model. Moreover, it outperforms the full-parameter model by 10% on code generation tasks.

World's largest chipmaker does not rule out price rises as costs increase The world's largest chipmaker has told the BBC that inflation is pushing up the cost of doing business, and did not rule out price rises. Taiwan Semiconductor Manufacturing Company (TSMC) makes the most advanced chips designed by companies such as Nvidia, AMD and Apple, so any increase in pricing could ripple through to the cost of AI infrastructure, and potentially over time, the prices customers pay for their electronic devices. However, the firm's chief financial officer, Wendell Huang, said it would not introduce sudden fourfold, fivefold price rises. We reflect our value, he said, pointing to its technology leadership and manufacturing excellence. In an exclusive and wide-ranging interview, Huang also denied that the AI boom was a bubble and that the firm's global expansion was due to geopolitical pressure.

In the five years since Myanmar's military chief led a coup to overthrow the democratically elected government, civil war has torn the country apart. Thousands have been killed and millions displaced by the conflict between the military and an alliance of ethnic and rebel groups. More than two years ago, the rebels made a series of sweeping gains, but things have taken a turn for the worse for them. Forced conscription and increased drone power has put the military on the offensive in most parts of the country. The BBC's Quentin Sommerville travelled to Myanmar without the permission of the authorities - the only way to report from rebel-held territory.

Israel has carried out strikes across southern Lebanon, despite a warning from Iran not to continue attacks in the country. The Lebanese health ministry said eight people were killed in Tyre, where the Israeli military issued a new order for residents to leave the southern city, including its Christian quarter for the first time. Israel and Iran paused hostilities on Monday, after an Israeli strike on Beirut targeting the Iranian-backed armed group Hezbollah triggered their first exchange of fire since a truce in April. Iran warned that it could hit Israel again if it did not stop attacks in Lebanon. But Israel vowed to continue its campaign against Hezbollah.

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