Rongye Shi, Wenjun Wu, Yirong Yang, Xinyu Liu, Haopeng Wang, Xuxin Lv, Jiabin Lou, Diyuan Hou, Xuehong Liu
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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 this paradigm by coordinating multiple vehicles in tasks such as search, inspection, and tracking. Recent advances in large language model (LLM) agents have strengthened natural-language task understanding and high-level planning, providing a flexible semantic interface between mission descriptions and collective behavior. While these advances expand semantic reasoning, applying LLM agents to UAV swarms raises challenges in grounding model decisions in executable capabilities, reconciling global task reasoning with distributed execution, and using mission-specific experience for continual adaptation. To address these challenges, we introduce AeroWeaver, an embodied-agent harness that weaves individual UAV skills into coordinated mission-level behavior. AeroWeaver connects semantic decisions to governed skills, organizes role-conditioned local agents for distributed coordination, and uses role-indexed state-action-reward experience to refine skill selection online. Experiments and runtime validation show that AeroWeaver maintains valid skill execution under tested conditions and supports body-local multi-UAV operation without a central agent generating joint actions from global context, while reward-guided online updates provide a training-free path for adaptive learning swarm agents from accumulated execution experience. Code website: https:// github.com/Admire-ljb/AeroWeaver. Collective intelligence enables multiple agents to combine local perception, information exchange, and coordinated action around a shared objective. UAV swarms provide a representative physical realization by organizing the sensing, mobility, and task capabilities of multiple aerial platforms. As missions and operating conditions change, their collective effectiveness depends on a continuous link between highlevel task organization and reliable vehicle-level execution. Recent advances in large language models (LLMs) bring complex-task understanding, knowledge organization, hierarchical reasoning, and long-horizon planning to the taskorganization layer. These capabilities make LLMs a flexible interface for interpreting mission descriptions and structuring high-level swarm behavior [1]. The resulting decisions ultimately operate through vehicle-specific observations, communication links, and actuators, making their connection to distributed physical execution a central research problem. AI-agent systems mediate this connection through the runtime surrounding the model, commonly termed a harness.
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