AeroWeaver is a new embodied‑agent harness that integrates large language model (LLM) decision making with the executable skills of individual UAVs, enabling distributed, adaptive swarm execution. It connects semantic mission decisions to governed skills, organizes role‑conditioned local agents for coordination, and refines skill selection online using role‑indexed state‑action‑reward experience. Experiments demonstrate that AeroWeaver maintains valid skill execution without a central joint‑action generator and supports reward‑guided, training‑free adaptive learning from accumulated execution experience.
By Jiabin Lou, Yirong Yang, Haopeng Wang, Xuxin Lv, Xinyu Liu, Diyuan Hou, Xuehong Liu, Rongye Shi, Wenjun Wu
Collective intelligence is a collaborative autonomy paradigm in which multiple agents pursue shared objectives through local perception, information exchange, and coordinated action. UAV swarms embody...
arXiv:2605. 13221v2 Announce Type: replace Abstract: In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC).
By Hanwen Zhang, Dusit Niyato, Wei Zhang, Xin Lou, Malcolm Yoke Hean Low
arXiv:2602. 01131v2 Announce Type: replace Abstract: With the rapid expansion of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) serve as pivotal aerial base stations supporting diverse services from users, ranging from latency-sensitive critical missions to bandwidth-intensive data streaming.
By Yue Zhong, Jiawen Kang, Yongju Tong, Hong-Ning Dai, Dong In Kim, Abbas Jamalipour, Shengli Xie
arXiv:2606. 01324v1 Announce Type: cross Abstract: The evolution toward 6G wireless networks envisions a seamlessly intelligent, Open-RAN-enabled architecture where unmanned aerial vehicles (UAVs) play a pivotal role in extending coverage, enhancing resilience, and ensuring reliable connectivity for ground users deployment.
By Marwan Dhuheir, Thang X. Vu, Symeon Chatzinotas
arXiv:2607. 23734v1 Announce Type: cross Abstract: Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VANETs) in smart city environments.
By Muhammad Umar Farooq Qaisar, Lin Zhang, Zhen Chen, Wajdy Othman, Shehzad Ashraf Chaudhry, Chang Liu
arXiv:2603. 16141v2 Announce Type: replace-cross Abstract: Autonomous Unmanned Aerial Vehicle (UAV) swarms are increasingly used as rapidly deployable aerial relays and sensing platforms, yet practical deployments must operate under partial observability and intermittent peer-to-peer connectivity.
By Enguang Fan, Yifan Chen, Zihan Shan, Klara Nahrstedt, Matthew Caesar, Jae Kim
arXiv:2607. 18604v1 Announce Type: cross Abstract: The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers.
By Zijiang Yan, Hao Zhou, Wael Jaafar, Jianhua Pei, Ping Wang, Halim Yanikomeroglu, Hina Tabassum
The paper introduces Hierarchical Reinforcement and Collective Learning (HRCL), a framework that combines multi‑agent reinforcement learning (MARL) with decentralized coordination. HRCL uses MARL at a high level to generate strategic guidance that limits the decision space for low‑level agents, enabling efficient short‑term coordination while considering long‑term effects. Experiments on synthetic, energy‑management, and drone‑swarm scenarios demonstrate faster convergence and significant reductions in system‑wide and individual costs compared to standalone MARL.
By Chuhao Qin, Evangelos Pournaras
arXiv:2608. 05792v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC.
By Kai Li, Conggai Li, Sarah Ali Siddiqui, Syed Sohail Ahmed, Xin Yuan, Shenghong Li, Wei Ni
arXiv:2608. 01128v1 Announce Type: cross Abstract: In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.
By Yixin Zhang, Zhuohui Yao, Wenchi Cheng, Walid Saad
The paper proposes Neuro‑Symbolic Agentic AI (NSAAI) as a framework that blends neural grounding, symbolic reasoning, and closed‑loop interaction to enhance decision‑making for networked low‑altitude UAVs. It outlines NSAAI’s strengths in data efficiency, compositional generalization, continual learning, and zero‑shot transfer, and presents a reference architecture covering task management, planning, verification, skill execution, and network interaction. An urban fire‑inspection simulation demonstrates how a UAV can coordinate sensing, cloud access, and verified image‑delivery skills under intermittent connectivity, illustrating NSAAI’s potential for reusable skills, evidence‑grounded decisions, and adaptive mission execution.
By Yuqi Ping, Tianhao Liang, Nanchi Su, Guangyu Lei, Junwei Wu, Qinyu Zhang, Tingting Zhang