Why is Israel still in southern Lebanon? A war to shape Lebanon's future An Israeli drone strike has killed two people in southern Lebanon, according to the country's Ministry of Public Health, a day after Israeli warplanes launched a series of deadly strikes on the country's eastern mountain range and south. It's the latest in near-daily Israeli violations of the United States-brokered ceasefire between Israel and the Lebanese armed group Hezbollah that began in November. It did not provide details on their condition. A security source told Al Jazeera that the drone fired a missile at a car, directly hitting the vehicle and causing it to catch fire.

European diplomats reveal the'tough guy' US negotiator leading the charge on Greenland: 'He hates us' A former Marine was unmasked as the'Zodiac killer' after a bombshell new investigation. I suffered a horrific side effect of a drug used by millions of Americans... and my face'melted off' The ICE backlash isn't the end of Kristi Noem It may have just saved her career FedEx driver accused of abducting and killing little girl while delivering her Christmas present says he shouldn't be executed because he has autism Senator accused of steamy affair with her bodyguard in bombshell lawsuit from his WIFE: 'Bring MDMA so I can guide you' Hunter Biden's stripper baby mama asks for him to be ARRESTED over claims he is still failing to pay her child support Family of Tyler Robinson's transgender lover speaks out for first time since Charlie Kirk assassination and reveals where he is now Dodgers agree with Kyle Tucker'on $240m deal' as champs beat out Mets, Blue Jays for top free agent World's sexiest hockey star and OnlyFans model Mikayla Demaiter spills out of little dress in latest post Nicole Richie addresses her daughter's new identity after unveiling transformation on her 18th birthday Trump gushes over'young beautiful' hockey players and teases rebranding of famed presidential wall Trump's AG secretary sparks mockery with tone-deaf $3 dinner advice as food costs soar Karoline Leavitt reveals the thinking behind Trump's call to cancel elections Microsoft's paperclip mascot delights users as it returns - 18 years after it was axed from Office It was the original virtual assistant, released years before Siri, Alexa, and Bixby. Now, almost two decades after it was axed, Microsoft's Clippy is officially back. The friendly anthropomorphic paper clip has been spotted as an Easter egg in Microsoft's latest announcement about a new AI companion called Mico. Mico - whose name is a nod to Microsoft Copilot - is a small blob with a friendly smiley face, and doesn't look much like its much-loved predecessor.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Ukraine's Zelenskyy to meet European leaders in London over military aid Ukrainian President Volodymyr Zelenskyy is due to meet European leaders in the United Kingdom for talks on military aid to stave off future Russian aggression if a ceasefire stops the war now in its fourth year. Zelenskyy and British Prime Minister Keir Starmer are expected to be joined at the Foreign Office in London on Friday by NATO Secretary-General Mark Rutte, Danish Prime Minister Mette Frederiksen and Dutch Prime Minister Dick Schoof.

As the Netherlands gears up for a snap parliamentary election on October 29, less than halfway through parliament's usual four-year term following the collapse of the ruling coalition, the likelihood of another win for the country's far-right Party for Freedom (PVV) is mounting. An outright win is next to impossible. The Netherlands has always had a coalition government formed by a minimum of two parties due to its proportional representation electoral system, under which seats in parliament are awarded to parties in proportion to the number of votes they win. It then partnered with three other far-right parties - the Farmer-Citizen Movement (BBB), New Social Contract (NSC), and the People's Party for Freedom and Democracy (VVD) - to form a coalition government. But in June, PVV made a dramatic exit from the coalition government over a disagreement on immigration policy.

Next week, five teens from Palestine will head to Panama to compete in one of the world's largest youth robotics competitions. To win--and then teach STEM to their peers displaced by the Israel-Hamas war. For the entirety of the past year, as the teenage roboticists of Team Palestine have been working on their latest project, their homeland has been engulfed in Israel's war with Hamas . Earlier this month, that all changed. With a fragile ceasefire in place, Israeli forces began to pull back from parts of Gaza, and the teens put the final touches on the project they hope will bring them victory: a robot that can maneuver through a series of simulated challenges based on the effects of climate change.

Large Language Models (LLMs) have inherent limitations of faithfulness and factuality, commonly referred to as hallucinations. Several benchmarks have been developed that provide a test bed for factuality evaluation within the context of English-centric datasets, while relying on supplementary informative context like web links or text passages but ignoring the available structured factual resources. To this end, Knowledge Graphs (KGs) have been identified as a useful aid for hallucination mitigation, as they provide a structured way to represent the facts about entities and their relations with minimal linguistic overhead. We bridge the lack of KG paths and multilinguality for factual language modeling within the existing hallucination evaluation benchmarks and propose a KG-based multilingual, multihop benchmark called MultiHal framed for generative text evaluation. As part of our data collection pipeline, we mined 140k KG-paths from open-domain KGs, from which we pruned noisy KG-paths, curating a high-quality subset of 25.9k. Our baseline evaluation shows an absolute scale improvement by approximately 0.12 to 0.36 points for the semantic similarity score, 0.16 to 0.36 for NLI entailment and 0.29 to 0.42 for hallucination detection in KG-RAG over vanilla QA across multiple languages and multiple models, demonstrating the potential of KG integration. We anticipate MultiHal will foster future research towards several graph-based hallucination mitigation and fact-checking tasks.

We provide a rigorous random matrix theory analysis of spiked cross-covariance models where the signals across two high-dimensional data channels are partially aligned. These models are motivated by multi-modal learning and form the standard generative setting underlying Partial Least Squares (PLS), a widely used yet theoretically underdeveloped method. We show that the leading singular values of the sample cross-covariance matrix undergo a Baik-Ben Arous-Peche (BBP)-type phase transition, and we characterize the precise thresholds for the emergence of informative components. Our results yield the first sharp asymptotic description of the signal recovery capabilities of PLS in this setting, revealing a fundamental performance gap between PLS and the Bayes-optimal estimator. In particular, we identify the SNR and correlation regimes where PLS fails to recover any signal, despite detectability being possible in principle. These findings clarify the theoretical limits of PLS and provide guidance for the design of reliable multi-modal inference methods in high dimensions.

Differentially private (DP) decentralized Federated Learning (FL) allows local users to collaborate without sharing their data with a central server. However, accurately quantifying the privacy budget of private FL algorithms is challenging due to the co-existence of complex algorithmic components such as decentralized communication and local updates. This paper addresses privacy accounting for two decentralized FL algorithms within the $f$-differential privacy ($f$-DP) framework. We develop two new $f$-DP-based accounting methods tailored to decentralized settings: Pairwise Network $f$-DP (PN-$f$-DP), which quantifies privacy leakage between user pairs under random-walk communication, and Secret-based $f$-Local DP (Sec-$f$-LDP), which supports structured noise injection via shared secrets. By combining tools from $f$-DP theory and Markov chain concentration, our accounting framework captures privacy amplification arising from sparse communication, local iterations, and correlated noise. Experiments on synthetic and real datasets demonstrate that our methods yield consistently tighter $(ε,δ)$ bounds and improved utility compared to Rényi DP-based approaches, illustrating the benefits of $f$-DP in decentralized privacy accounting.

Knowledge graph alignment is the task of matching equivalent entities (that is, instances and classes) and relations across two knowledge graphs. Most existing methods focus on pure entity-level alignment, computing the similarity of entities in some embedding space. They lack interpretable reasoning and need training data to work. In this paper, we propose FLORA, a simple yet effective method that (1) is unsupervised, i.e., does not require training data, (2) provides a holistic alignment for entities and relations iteratively, (3) is based on fuzzy logic and thus delivers interpretable results, (4) provably converges, (5) allows dangling entities, i.e., entities without a counterpart in the other KG, and (6) achieves state-of-the-art results on major benchmarks.

Large Language Model (LLM) conditioning refers to instructing an LLM to generate content in accordance with the norms and values of a specific culture, beliefs of a particular political orientation, or any desired text-specified semantic conditioning. Unfortunately, prompt engineering does not ensure that LLMs behave in accordance with a desired conditioning due to the inductive bias of the pre-training and alignment datasets. Prior works have focused on fine-tuning LLMs by directly conditioning the LoRA weights; however, such methods introduce a large number of parameters. As a remedy, we propose Zhyper, a parameter-efficient factorized hypernetwork framework that generates context-aware LoRA adapters from textual descriptions. Experiments on multiple benchmarks show that Zhyper achieves competitive performance with up to 26x fewer parameters than the state-of-the-art baselines. Furthermore, we extend Zhyper to cultural alignment, demonstrating improved generalization to out-of-domain settings and a better capturing of fine-grained contextual values. Large Language Models (LLMs) have transformed Natural Language Processing (NLP), Computer Vision (CV), and machine learning (ML) more broadly. They achieve state-of-the-art performance in text generation and comprehension across diverse domains, including code synthesis (Rozi ` ere et al., 2023), mathematical reasoning (Ahn et al., 2024), scientific writing (Geng et al., 2025; Eger et al., 2025), multimodal tasks such as text-image understanding and generation (Alayrac et al., 2022), and evaluation of machine translation and related tasks (Gu et al., 2025). This success stems from scaling to millions and billions of parameters. However, this scaling requires large computational resources, motivating the search for parameter-efficient fine-tuning (PEFT) techniques. Recent advances have made it possible to adapt LLMs to task-specific criteria, which is crucial for a broader applicability and acceptance of NLP systems. A recent stream of research leverages PEFT techniques (Ding et al., 2023; Weyssow et al., 2023; Prottasha et al., 2024), e.g., Low-Rank Adaptions (LoRA) (Hu et al., 2021) to adapt for desired task-specific values in an LLM. LoRA achieves this by freezing most of the pre-trained model's parameters and introducing trainable low-rank matrices, yielding weight correction terms. However, stand-alone LoRA approaches are primarily tailored for a single-task adaptation and may lose their effectiveness in a setting where an LLM needs to be adapted to various downstream settings.