The large artificial intelligence models (LAMs) show strong capabilities in perception, reasoning, and multi-modal understanding, and can enable advanced capabilities in low-altitude edge intelligence. However, the deployment of LAMs at the edge remains constrained by some fundamental limitations. First, tasks are rigidly tied to specific models, limiting the flexibility. Besides, the computational and memory demands of full-scale LAMs exceed the capacity of most edge devices. Moreover, the current inference pipelines are typically static, making it difficult to respond to real-time changes of tasks. To address these challenges, we propose a prompt-to-agent edge cognition framework (P2AECF), enabling the flexible, efficient, and adaptive edge intelligence. Specifically, P2AECF transforms high-level semantic prompts into executable reasoning workflows through three key mechanisms. First, the prompt-defined cognition parses task intent into abstract and model-agnostic representations. Second, the agent-based modular execution instantiates these tasks using lightweight and reusable cognitive agents dynamically selected based on current resource conditions. Third, the diffusion-controlled inference planning adaptively constructs and refines execution strategies by incorporating runtime feedback and system context. In addition, we illustrate the framework through a representative low-altitude intelligent network use case, showing its ability to deliver adaptive, modular, and scalable edge intelligence for real-time low-altitude aerial collaborations.

翻译：大型人工智能模型（LAMs）在感知、推理与多模态理解方面展现出强大能力，可为低空边缘智能提供高级功能。然而，LAMs在边缘侧的部署仍受若干根本性限制制约。首先，任务与特定模型刚性绑定，限制了灵活性。此外，完整规模LAMs的计算与内存需求超出多数边缘设备的承载能力。同时，现有推理流程通常呈静态化，难以响应任务的实时动态变化。为应对这些挑战，本文提出一种提示至智能体的边缘认知框架（P2AECF），以实现灵活、高效且自适应的边缘智能。具体而言，P2AECF通过三项核心机制将高层语义提示转化为可执行的推理工作流：其一，提示定义认知将任务意图解析为抽象且与模型无关的表示；其二，基于智能体的模块化执行通过动态选取轻量级可复用认知智能体（依据当前资源状况）实现任务实例化；其三，扩散控制推理规划通过融合运行时反馈与系统上下文，自适应构建并优化执行策略。此外，我们通过一个代表性低空智能网络用例阐释该框架，展示其能为实时低空协同任务提供自适应、模块化且可扩展的边缘智能。