Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Russia strikes children's hospital in Ukraine as Kyiv hits energy sites A Russian strike on a children's hospital in southern Ukraine has wounded at least nine people, authorities have said, shortly after Kyiv targeted Russian energy sites with drones. Four children were injured in Russia's strike on the medical facility in Kherson on Wednesday, which Ukrainian President Volodymyr Zelenskyy described as a "deliberate" attack that shows Moscow does not want peace.

Understanding how life is adapting to urban environments represents an important challenge in evolutionary biology. In this work, we investigate a widely cited example of urban adaptation, Culex pi...

The increasing adoption of large language models (LLMs) in cloud-based services has raised significant privacy concerns, as user inputs may inadvertently expose sensitive information. Existing text anonymization and de-identification techniques, such as rule-based redaction and scrubbing, often struggle to balance privacy preservation with text naturalness and utility. In this work, we propose a zero-shot, tree-search-based iterative sentence rewriting algorithm that systematically obfuscates or deletes private information while preserving coherence, relevance, and naturalness. Our method incrementally rewrites privacy-sensitive segments through a structured search guided by a reward model, enabling dynamic exploration of the rewriting space. Experiments on privacy-sensitive datasets show that our approach significantly outperforms existing baselines, achieving a superior balance between privacy protection and utility preservation.

Falling debris from destroyed Ukrainian drones disrupted railway power supply and train operations in part of the Volgograd region, the administration of the region in Russia's south said on Sunday. There were no injuries as a result of the attacks, the administration said on Telegram, quoting Governor Andrei Bocharov. Russia downed 99 drones overnight over 12 Russian regions, the Crimean Peninsula and the Black Sea, the Russian Ministry of Defence said. Meanwhile, Russia launched a barrage of drones and missiles in an overnight attack that killed three people in Ukraine's Dnipro and the nearby region on Saturday, Ukrainian officials said. Ukraine's air force said it intercepted 183 drones and 17 missiles, but hits from 10 missiles and 25 drones were recorded in nine locations.

In the field of robotics, researchers face a critical challenge in ensuring reliable and efficient task planning. Verifying high-level task plans before execution significantly reduces errors and enhance the overall performance of these systems. In this paper, we propose an architecture for automatically verifying high-level task plans before their execution in simulator or real-world environments. Leveraging Large Language Models (LLMs), our approach consists of two key steps: first, the conversion of natural language instructions into Linear Temporal Logic (LTL), followed by a comprehensive analysis of action sequences. The module uses the reasoning capabilities of the LLM to evaluate logical coherence and identify potential gaps in the plan. Rigorous testing on datasets of varying complexity demonstrates the broad applicability of the module to household tasks. We contribute to improving the reliability and efficiency of task planning and addresses the critical need for robust pre-execution verification in autonomous systems. The code is available at https://verifyllm.github.io.

North Korean troops are said to have clashed with Ukrainian forces in the Russian region of Kursk for the first time on Tuesday, the same day American voters re-elected Donald Trump for president, an isolationist who has argued against sending further military aid to Ukraine. "The first battles with North Korean soldiers open a new page of instability in the world," said Ukrainian President Volodymyr Zelenskyy in his evening address. "We must do everything to make this Russian step to expand the war – to really escalate it – to make this step a failure." Ukrainian Defence Minister Rustem Umerov said the clashes were "small scale" and that the North Korean troops were not fighting as separate formations but were embedded in Russian units disguised as Buryats from the Russian Federation. On Saturday, Ukraine's military intelligence (GUR) had said Russia transferred more than 7,000 North Korean military personnel "to areas near Ukraine" in the last week of October – a much higher figure than the 3,000 North Korean soldiers South Korean and United States intelligence had said were in Russia's Kursk region on October 30.

Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccuracies in the generated text might be obscured by the rest of the output being generally factually correct, making it extremely hard for the users to spot them. Current services that leverage LLMs usually do not provide any means for detecting unreliable generations. Here, we aim to bridge this gap. In particular, we propose a novel fact-checking and hallucination detection pipeline based on token-level uncertainty quantification. Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output. Moreover, we present a novel token-level uncertainty quantification method that removes the impact of uncertainty about what claim to generate on the current step and what surface form to use. Our method Claim Conditioned Probability (CCP) measures only the uncertainty of a particular claim value expressed by the model. Experiments on the task of biography generation demonstrate strong improvements for CCP compared to the baselines for seven LLMs and four languages. Human evaluation reveals that the fact-checking pipeline based on uncertainty quantification is competitive with a fact-checking tool that leverages external knowledge.

Russia: Cognitive Agro Pilot, an autonomous AI-based driving system for farming equipment which was designed by Sber and its ecosystem member Cognitive Pilot – has succeeded in industrial use across 35 regions of Russia when reaping the 2020 harvest. From June to October 2020, over 350 New Holland, John Deere and CLAAS autonomous combines equipped with Cognitive Agro Pilot system farmed over 160,000 hectares of field and harvested more than 720,000 tonnes of crops. With the help of Cognitive Agro Pilot as many as 590,000 metric tonnes of grain crops such as wheat, soybeans, barley, oats, sorghum, buckwheat, among others, were harvested over 130,000 hectares, and some 130,000 metric tonnes of row crops and roll crops (corn, sunflower, etc.) were harvested over 30,000 hectares in Kaliningrad, Kaluga, Kursk, Belgorod, Tambov, Penza, Rostov, Tomsk, Kurgan, Krasnodar, Krasnoyarsk and Stavropol regions. Thanks to the use of Cognitive Agro Pilot, this harvesting season stakeholders were able to save – on fuel and other related materials, shorter harvesting time (machine hours), equipment depreciation, extended active use of equipment before capital expenditures, fewer human errors, optimisation of business processes, and other parameters. According to the estimates of project members, in the next three years, every 10th harvester in Russia may become autonomous.

The Weak Completion Semantics is a novel cognitive theory which has been successfully applied to the suppression task, the selection task, syllogistic reasoning, the belief bias effect, spatial reasoning as well as reasoning with conditionals. It is based on logic programming with skeptical abduction. Each program admits a least model under the three-valued Lukasiewicz logic, which can be computed as the least fixed point of an appropriate semantic operator. The semantic operator can be represented by a three-layer feed-forward network using the core method. Its least fixed point is the unique stable state of a recursive network which is obtained from the three-layer feed-forward core by mapping the activation of the output layer back to the input layer. The recursive network is embedded into a novel network to compute skeptical abduction. This paper presents a fully connectionist realization of the Weak Completion Semantics.