On Russian state TV a news bulletin shows images of a five-storey building reduced to rubble. Teams of rescuers are sifting through debris. On a severely damaged façade there's a sign: What happened here early on Friday has sparked Russian accusations, Ukrainian denials, an emergency session of the United Nations Security Council and vows of retribution by the Kremlin. The town of Starobilsk is in Russian-occupied eastern Ukraine: in the Luhansk region which Moscow claims to have annexed. Russian officials accuse Ukraine of a carrying out a drone attack on the college dormitory.

Russia and Ukraine exchanged 205 prisoners of war on Friday, hours after rescue workers ended their search of a destroyed block of flats in Kyiv in which 24 people were killed, including three girls. Most of the Ukrainian prisoners had been held since 2022, said President Zelensky. The swap was part of a short-lived ceasefire ending this week with the launch of massive Russian strikes across Ukraine, including a missile attack that reduced 18 flats to rubble. Among the victims was 12-year-old Lyubava Yakovleva, whose father was killed during the war. Meanwhile, Russian officials said four people, including a child, were killed when Ukrainian drones hit the city of Ryazan, south-east of Moscow.

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Five people, including three young children, have been killed in the latest overnight Russian attacks on Ukraine, President Volodymyr Zelenskyy has said, as United States-led efforts to end the nearly war continue to progress at a slow, bogged-down pace. The Ukrainian leader said on Wednesday that a Russian drone had struck a private family home in the town of Bohodukhiv in Ukraine's northeastern Kharkiv region late on Tuesday, killing four and seriously injuring their pregnant mother, the sole survivor.

Will Hamas agree to hand over its weapons? Has another Nakba been averted? 'When the bombs in Gaza stop, the true pain starts' The ceasefire between Israel and Hamas brought thousands of people back to their homes in Gaza City, to assess the damage, see what can be salvaged, and start to rebuild. In Jabalia, Sheikh Radwan, Abu Iskandar and beyond, people returned to flattened neighbourhoods, and to the knowledge that, still among the rubble, some of the explosive robots that had caused it sat, silent and undetonated. The "robots" had become a common fear in northern Gaza since the Israeli army first used them on Jabalia refugee camp in May 2024.

'It's safe now': See students pulled from rubble of collapsed Indonesian school Dramatic rescue footage shows the boys in Indonesia pulled to safety after their school building collapsed on Monday. The three students, Yusuf, Haikal and Dani were all trapped under the rubble for several hours. It is thought around 38 people are still stuck and unaccounted for. Six students have died so far. Watch: Moments as 6.9 magnitude earthquake hit Philippines At least 69 people are killed after it struck on Tuesday night with officials declaring a state of calamity.

An overnight Russian drone and missile attack on Ukraine's capital has killed at least 14 people including three children, say officials. Al Jazeera's Zein Basravi has been at the scene of a strike on an apartment building there.

Intelligent agents, such as robots, are increasingly deployed in real-world, human-centric environments. To foster appropriate human trust and meet legal and ethical standards, these agents must be able to explain their behavior. However, state-of-the-art agents are typically driven by black-box models like deep neural networks, limiting their interpretability. We propose a method for generating natural language explanations of agent behavior based only on observed states and actions -- without access to the agent's underlying model. Our approach learns a locally interpretable surrogate model of the agent's behavior from observations, which then guides a large language model to generate plausible explanations with minimal hallucination. Empirical results show that our method produces explanations that are more comprehensible and correct than those from baselines, as judged by both language models and human evaluators. Furthermore, we find that participants in a user study more accurately predicted the agent's future actions when given our explanations, suggesting improved understanding of agent behavior.

President Donald Trump's controversially announced plans for the US to'take over and own' Gaza last night. While the proclamation drew criticism for'bringing more suffering to the region,' users on social media have used AI to transform the city into a gentrified metropolis with a large building featuring a'Trump Tower' sign glowing in lights at the city center. The rubble-filled streets were transformed into paved roadways lined with towering skyscrapers and areas where buildings had crumbled featured a green golf course surrounded by resorts. The AI-generated images were met with amusement, but others angered at the insensitivity of the creations and warned how'it would be the biggest blackpill ever if a great Biblical city was paved over.' Trump, who spent his career as a property developer, has long talked up Gaza's coastal location and pleasant climate as a perfect holiday vacation. In his vision, US reconstruction would create thousands of jobs and spare Palestinians the pain and expense of rebuilding once again.

Credit assignment, the process of attributing credit or blame to individual agents for their contributions to a team's success or failure, remains a fundamental challenge in multi-agent reinforcement learning (MARL), particularly in environments with sparse rewards. Commonly-used approaches such as value decomposition often lead to suboptimal policies in these settings, and designing dense reward functions that align with human intuition can be complex and labor-intensive. In this work, we propose a novel framework where a large language model (LLM) generates dense, agent-specific rewards based on a natural language description of the task and the overall team goal. By learning a potential-based reward function over multiple queries, our method reduces the impact of ranking errors while allowing the LLM to evaluate each agent's contribution to the overall task. Through extensive experiments, we demonstrate that our approach achieves faster convergence and higher policy returns compared to state-of-the-art MARL baselines.

This paper introduces a comprehensive framework for Post-Disaster Search and Rescue (PDSR), aiming to optimize search and rescue operations leveraging Unmanned Aerial Vehicles (UAVs). The primary goal is to improve the precision and availability of sensing capabilities, particularly in various catastrophic scenarios. Central to this concept is the rapid deployment of UAV swarms equipped with diverse sensing, communication, and intelligence capabilities, functioning as an integrated system that incorporates multiple technologies and approaches for efficient detection of individuals buried beneath rubble or debris following a disaster. Within this framework, we propose architectural solution and address associated challenges to ensure optimal performance in real-world disaster scenarios. The proposed framework aims to achieve complete coverage of damaged areas significantly faster than traditional methods using a multi-tier swarm architecture. Furthermore, integrating multi-modal sensing data with machine learning for data fusion could enhance detection accuracy, ensuring precise identification of survivors.