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arXiv 2602.14003cs.AI
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基于提示的低空边缘智能:模块化代理与生成推理

Prompt-Driven Low-Altitude Edge Intelligence: Modular Agents and Generative Reasoning

Jiahao You, Ziye Jia, Chao Dong, Qihui Wu

AI总结:

本文提出P2AECF框架,通过模块化代理和生成推理实现灵活、高效和适应的低空边缘智能。

摘要:

大型人工智能模型(LAMs)在感知、推理和多模态理解方面表现出强大的能力,能够为低空边缘智能带来先进功能。然而,将LAMs部署到边缘仍然受到一些基本限制的制约。首先,任务与特定模型紧密绑定,限制了灵活性。此外,全规模LAMs的计算和内存需求超过了大多数边缘设备的能力。此外,当前的推理管道通常是静态的,难以应对任务的实时变化。为了解决这些挑战,我们提出了一种提示到代理边缘认知框架(P2AECF),使边缘智能能够灵活、高效和适应。具体而言,P2AECF通过三个关键机制将高层语义提示转化为可执行的推理流程。首先,提示定义的认知将任务意图解析为抽象且模型无关的表示。其次,基于代理的模块化执行利用轻量且可重用的认知代理动态选择执行任务,根据当前资源条件。第三,扩散控制的推理规划通过结合运行时反馈和系统上下文,自适应地构建和优化执行策略。此外,我们通过一个具有代表性的低空智能网络使用案例来说明该框架,展示了其在实时低空空中协作中提供适应性、模块化和可扩展的边缘智能的能力。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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