Sudan's Rapid Support Forces (RSF) have launched a series of drone attacks targeting humanitarian aid convoys and fuel trucks across North Kordofan, killing at least one person and wounding several others, officials and medical organisations said. The North Kordofan state government condemned Friday's strikes on a convoy linked to the World Food Programme (WFP), urging the international community and United Nations bodies to impose sanctions on the RSF paramilitary group's leadership. The attacks occurred along the key road connecting the state capital, el-Obeid, with Kosti in neighbouring White Nile state. Fighting between the government-aligned Sudanese Armed Forces (SAF) and the RSF has intensified across the Kordofan region since October 2025 after el-Fasher fell to the RSF, where the group committed atrocities - a "crime scene" according to the UN. According to the UN Office for the Coordination of Humanitarian Affairs (OCHA), the first strike at dawn hit three trucks in Er-Rahad.

A drone attack has hit a convoy of 16 trucks carrying desperately needed food to Sudan's famine-hit North Darfur region, the United Nations said, as warring parties trade blame for the attack. UN spokesperson Daniela Gross told reporters on Thursday that all drivers and personnel travelling with the World Food Programme (WFP) convoy were safe. At least three of the trucks caught fire, according to a WFP statement quoted by the Reuters news agency. Gross said all trucks had caught fire, according to The Associated Press news agency. It was not yet clear who was responsible for Wednesday's attack, the second in the past three months to prevent a UN convoy from delivering to North Darfur.

ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs Liping Lu 1, Zhican He 1, Duanfeng Chu 2, Rukang Wang 2, Saiqian Peng 2, Pan Zhou 3 Abstract -- This paper proposes a novel method for multilane convoy formation control that uses large language models (LLMs) to tackle coordination challenges in dynamic highway environments. Each connected and autonomous vehicle in the convoy uses a knowledge-driven approach to make real-time adaptive decisions based on various scenarios. Our method enables vehicles to dynamically perform tasks, including obstacle avoidance, convoy joining/leaving, and escort formation switching, all while maintaining the overall convoy structure. We design a Interlaced formation control strategy based on locally dynamic distributed graphs, ensuring the convoy remains stable and flexible. We conduct extensive experiments in the SUMO simulation platform across multiple traffic scenarios, and the results demonstrate that the proposed method is effective, robust, and adaptable to dynamic environments. I. INTRODUCTION With the rapid development of Connected and Automated V ehicles (CA Vs) technology, convoy coordination control has shown significant potential in improving traffic flow efficiency, driving safety, and fuel economy.

At times, Israeli tanks have deployed along main roads where aid trucks travel. And Israeli ministers have said they debated authorizing private security contractors to protect international aid convoys inside Gaza. Until recently, Israeli forces largely did not target the looters unless they were affiliated with Hamas or other militant groups, according to U.N. officials. But that appears to have changed in recent weeks. In Israeli military drone footage viewed by The Times, looters can be seen confiscating white sacks of aid from cars in southern Gaza in November.

Fact-checking based on commercial LLMs has become mainstream. Although these methods offer high explainability, it falls short in accuracy compared to traditional fine-tuning approaches, and data security is also a significant concern. In this paper, we propose a self-instruction based fine-tuning approach for fact-checking that balances accuracy and explainability. Our method consists of Data Augmentation and Improved DPO fine-tuning. The former starts by instructing the model to generate both positive and negative explanations based on claim-evidence pairs and labels, then sampling the dataset according to our customized difficulty standards. The latter employs our proposed improved DPO to fine-tune the model using the generated samples. We fine-tune the smallest-scale LLaMA-7B model and evaluate it on the challenging fact-checking datasets FEVEROUS and HOVER, utilizing four fine-tuning methods and three few-shot learning methods for comparison. The experiments demonstrate that our approach not only retains accuracy comparable to, or even surpassing, traditional fine-tuning methods, but also generates fluent explanation text. Moreover, it also exhibit high generalization performance. Our method is the first to leverage self-supervised learning for fact-checking and innovatively combines contrastive learning and improved DPO in fine-tuning LLMs, as shown in the experiments.

Field robotics applications, such as search and rescue, involve robots operating in large, unknown areas. These environments present unique challenges that compound the difficulties faced by a robot operator. The use of multi-robot teams, assisted by carefully designed autonomy, help reduce operator workload and allow the operator to effectively coordinate robot capabilities. In this work, we present a system architecture designed to optimize both robot autonomy and the operator experience in multi-robot scenarios. Drawing on lessons learned from our team's participation in the DARPA SubT Challenge, our architecture emphasizes modularity and interoperability. We empower the operator by allowing for adjustable levels of autonomy ("sliding mode autonomy"). We enhance the operator experience by using intuitive, adaptive interfaces that suggest context-aware actions to simplify control. Finally, we describe how the proposed architecture enables streamlined development of new capabilities for effective deployment of robot autonomy in the field.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. JERUSALEM (AP) -- The Israeli military said Friday that it dismissed two officers and reprimanded three others for their roles in drone strikes in Gaza that killed seven aid workers on a food-delivery mission, saying the officers had mishandled critical information and violated the army's rules of engagement. The findings of a retired general's investigation into the Monday killings marked an embarrassing admission by Israel, which faces growing accusations from key allies, including the United States, of not doing enough to protect Gaza's civilians from its war with the militant Hamas group. The findings are likely to bolster widespread skepticism over the Israeli military's decision-making.

Practical deployments of coordinated fleets of mobile robots in different environments have revealed the benefits of maintaining small distances between robots, especially as they move at higher speeds. However, this is counter-intuitive in that as speed increases, reducing the amount of space between robots also reduces the time available to the robots to respond to sudden motion variations in surrounding robots. However, in certain examples, the benefits in performance due to traveling at closer distances can outweigh the potential instability issues, for instance, autonomous trucks on highways that optimize energy by vehicle ``drafting'' or smaller robots in cluttered environments that need to maintain close, line of sight communication, etc. To achieve this kind of closely coordinated fleet behavior, this work introduces a model predictive optimal control framework that directly takes non-linear dynamics of the vehicles in the fleet into account while planning motions for each robot. The robots are able to follow each other closely at high speeds by proactively making predictions and reactively biasing their responses based on state information from the adjacent robots. This control framework is naturally decentralized and, as such, is able to apply to an arbitrary number of robots without any additional computational burden. We show that our approach is able to achieve lower inter-robot distances at higher speeds compared to existing controllers. We demonstrate the success of our approach through simulated and hardware results on mobile ground robots.

Earlier this month, attacks that took place within minutes of each other in different parts of Nigeria, and the apparent failure of the security forces to respond to them efficiently and in a timely manner, exposed how big of a threat lawlessness and impunity currently poses to the country and its people. Late on July 5, heavily armed men on motorcycles raided the Kuje Medium Security Custodial Centre on the outskirts of Abuja and released more than 900 inmates, including more than 60 Boko Haram members in detention. The Islamic State West Africa Province (ISWAP) – an offshoot of Boko Haram now allied with the ISIL (ISIS) group – claimed responsibility for the attack. Just hours before the Kuje incident, another group of heavily armed men had attacked a convoy carrying an advance security team for President Muhammadu Buhari in his home state of Katsina. A presidential spokesperson said the convoy carrying a team of security guards, as well as protocol and media officers, was on its way to Daura, Buhari's hometown, to prepare for a visit by him when the attack took place.

Convoy, founded by former Amazon executives, describes itself like a neural network for the freight industry, applying technology and data across its network of carriers to save shippers money and increase earnings for truckers. Convoy's digital trucking network links tens of thousands of small trucking companies and owner-operators via mobile phones as a way to digitize what historically has been a fragmented market leading to empty miles and less-than-full loads. "If you look at all freight transportation, including air, ocean, rail pipeline, and surface transportation on trucks, about 80% of the dollars go to trucks. But, one of the biggest challenges we're seeing is that there aren't enough trucks, there aren't enough drivers, and there are no trailers," co-founder and CEO Dan Lewis told CNBC last year. By Convoy's estimate, trucks run empty up to one-third of the time.