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.

The request for fast response and safe operation after natural and man-made disasters in urban environments has spurred the development of robotic systems designed to assist in search and rescue operations within complex rubble sites. Traditional Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) face significant limitations in such confined and obstructed environments. This paper introduces a novel vine robot designed to navigate dense rubble, drawing inspiration from natural growth mechanisms found in plants. Unlike conventional robots, vine robots are soft robots that can grow by everting their material, allowing them to navigate through narrow spaces and obstacles. The prototype presented in this study incorporates pneumatic muscles for steering and oscillation, an equation-based robot length control plus feedback pressure regulating system for extending and retracting the robot body. We conducted a series of controlled experiments in an artificial rubble testbed to assess the robot performance under varying environmental conditions and robot parameters, including volume ratio, environmental weight, oscillation, and steering. The results show that the vine robot can achieve significant penetration depths in cluttered environments with mixed obstacle sizes and weights, and can maintain repeated trajectories, demonstrating potential for mapping and navigating complex underground paths. Our findings highlight the suitability of the vine robot for urban search and rescue missions, with further research planned to enhance its robustness and deployability in real-world scenarios.

The death toll from a Russian drone strike that destroyed an apartment block in Ukraine's southern port city of Odesa on Saturday has risen to 10. Ukraine's interior ministry reported that rescue workers on Sunday morning retrieved the remains of an infant and the baby's mother, raising the number of children killed in the attack to three. "The mother tried to cover the 8-month-old child with her own [body]. She tried to save them. They were found in a firm embrace," the ministry said in a Telegram post. On Saturday, Ukrainian authorities reported that a baby was among those killed after falling debris from an Iranian-made Shahed drone hit the apartment building – one of eight Russian-launched drones reported by officials.

When a devastating earthquake struck Turkey in the early hours of February 6, 2023, the five-storey building in Hatay where 13-year-old Mehmet Koc lived, collapsed, burying him in rubble and killing his older brother Emre, 14, and his mother Didem. But it took 76 hours before rescuers could pull him from the mound of concrete and twisted metal that remained of his home. Later in hospital, doctors determined that his legs were so badly crushed and injured, that both needed to be amputated just below the hip. Hearing of the earthquake in London where he lived and worked, Mehmet's father, Hasan, caught the next available flight to Turkey and travelled to Hatay, in the southeast, desperate for news of his family. The 58-year-old encountered a scene of utter destruction.