Low-altitude Unmanned Aerial Vehicle (UAV) networks rely on robust semantic segmentation as a foundational enabler for distributed sensing-communication-control co-design across heterogeneous agents within the network. However, segmentation foundation models deteriorate quickly under weather, lighting, and viewpoint drift. Resource-limited UAVs cannot run gradient-based test-time adaptation, while resource-massive UAVs adapt independently, wasting shared experience. To address these challenges, we propose AdaptFly, a prompt-guided test-time adaptation framework that adjusts segmentation models without weight updates. AdaptFly features two complementary adaptation modes. For resource-limited UAVs, it employs lightweight token-prompt retrieval from a shared global memory. For resource-massive UAVs, it uses gradient-free sparse visual prompt optimization via Covariance Matrix Adaptation Evolution Strategy. An activation-statistic detector triggers adaptation, while cross-UAV knowledge pool consolidates prompt knowledge and enables fleet-wide collaboration with negligible bandwidth overhead. Extensive experiments on UAVid and VDD benchmarks, along with real-world UAV deployments under diverse weather conditions, demonstrate that AdaptFly significantly improves segmentation accuracy and robustness over static models and state-of-the-art TTA baselines. The results highlight a practical path to resilient, communication-efficient perception in the emerging low-altitude economy.

The real-time performance, adversarial resiliency, and privacy preservation are the most important metrics that need to be balanced to practice collision avoidance in large-scale multi-UAV (Unmanned Aerial Vehicle) systems. Current frameworks tend to prescribe monolithic solutions that are not only prohibitively computationally complex with a scaling cost of $O(n^2)$ but simply do not offer Byzantine fault tolerance. The proposed hierarchical framework presented in this paper tries to eliminate such trade-offs by stratifying a three-layered architecture. We spread the intelligence into three layers: an immediate collision avoiding local layer running on dense graph attention with latency of $<10 ms$, a regional layer using sparse attention with $O(nk)$ computational complexity and asynchronous federated learning with coordinate-wise trimmed mean aggregation, and lastly, a global layer using a lightweight Hashgraph-inspired protocol. We have proposed an adaptive differential privacy mechanism, wherein the noise level $(ε\in [0.1, 1.0])$ is dynamically reduced based on an evaluation of the measured real-time threat that in turn maximized the privacy-utility tradeoff. Through the use of Distributed Hash Table (DHT)-based lightweight audit logging instead of heavyweight blockchain consensus, the median cost of getting a $95^{th}$ percentile decision within 50ms is observed across all tested swarm sizes. This architecture provides a scalable scenario of 500 UAVs with a collision rate of $< 2.0\%$ and the Byzantine fault tolerance of $f < n/3$.

In this conversation, Helen Warrell, FT investigations reporter and former defense and security editor, and James O'Donnell, MIT Technology Review's senior AI reporter, consider the ethical quandaries and financial incentives around AI's use by the military. Welcome back to, a new collaboration between the Financial Times and MIT Technology Review. In this conversation, Helen Warrell, investigations reporter and former defense and security editor, and James O'Donnell, 's senior AI reporter, consider the ethical quandaries and financial incentives around AI's use by the military. It is July 2027, and China is on the brink of invading Taiwan. Autonomous drones with AI targeting capabilities are primed to overpower the island's air defenses as a series of crippling AI-generated cyberattacks cut off energy supplies and key communications. In the meantime, a vast disinformation campaign enacted by an AI-powered pro-Chinese meme farm spreads across global social media, deadening the outcry at Beijing's act of aggression.

Mysterious drones have been spotted at night at airports across Europe. How worried should we be? First comes the warning, that disembodied voice over the tannoy: Your attention please. Please move to the shelter on the minus second floor. Then comes the mosquito-like whine of the incoming Russian drones, massing in their hundreds just above the clouds.

The Long-Range (LoRa) protocol, known for its extensive range and low power, has increasingly been adopted in tags worn by mentally incapacitated persons (MIPs) and others at risk of going missing. We study the sequential decision-making process for a mobile sensor to locate a periodically broadcasting LoRa tag with the fewest moves (hops) in general, unknown environments, guided by the received signal strength indicator (RSSI). While existing methods leverage reinforcement learning for search, they remain vulnerable to domain shift and signal fluctuation, resulting in cascading decision errors that culminate in substantial localization inaccuracies. To bridge this gap, we propose LoRaCompass, a reinforcement learning model designed to achieve robust and efficient search for a LoRa tag. For exploitation under domain shift and signal fluctuation, LoRaCompass learns a robust spatial representation from RSSI to maximize the probability of moving closer to a tag, via a spatially-aware feature extractor and a policy distillation loss function. It further introduces an exploration function inspired by the upper confidence bound (UCB) that guides the sensor toward the tag with increasing confidence. We have validated LoRaCompass in ground-based and drone-assisted scenarios within diverse unseen environments covering an area of over 80km^2. It has demonstrated high success rate (>90%) in locating the tag within 100m proximity (a 40% improvement over existing methods) and high efficiency with a search path length (in hops) that scales linearly with the initial distance.

Biden administration's diplomatic dispute led to U.S. expulsion from Niger, eliminating drone surveillance capabilities needed to combat Sahel region terrorism.

Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Ukraine is working to resume prisoner exchanges with Russia that could bring 1,200 Ukrainians home, President Volodymyr Zelenskyy says, a day after his national security chief announced progress in negotiations. "We are counting on the resumption of POW exchanges," Zelenskyy wrote on X on Sunday.

Military experts warn that Chinese missile strikes on U.S. air bases could cripple American airpower in the Pacific, as both nations pursue different strategies for air superiority.

Al Jazeera speaks to man who left Gaza through controversial'displacement flight' NewsFeed Man tells Al Jazeera he left Gaza through Al Majd'displacement flight' This Palestinian man, who wants to remain anonymous, left Gaza through'Al Majd Europe', a controversial group using unofficial, Israeli-coordinated channels that required registration, screening, and payments to unknown individuals. Investigators probe group that arranged'trafficking' flights out of Gaza Football's Pep Guardiola calls on fans to attend Palestine charity match Ukraine's Kyiv pounded by hundreds of Russian drones

An explosion at a police station in Indian-administered Kashmir has killed at least nine people and injured 32 others, mainly police personnel. Officials said the blast was an accident. It happened as officers were inspecting confiscated explosives. Football's Pep Guardiola calls on fans to attend Palestine charity match Ukraine's Kyiv pounded by hundreds of Russian drones Italian prosecutors investigate Bosnian war'sniper safaris'