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[2602.14003] Prompt-Driven Low-Altitude Edge Intelligence: Modular Agents and Generative Reasoning
Summary
The paper presents a novel framework for low-altitude edge intelligence, addressing limitations of large AI models through a prompt-to-agent cognition system that enhances flexibility and efficiency.
Why It Matters
As AI models become increasingly integral to real-time applications, this research offers solutions to overcome the constraints of deploying large models on edge devices, paving the way for more adaptable and scalable AI solutions in various industries, especially in low-altitude aerial operations.
Key Takeaways
- Introduces a prompt-to-agent edge cognition framework (P2AECF) for enhanced AI flexibility.
- Addresses computational limitations of large AI models in edge environments.
- Utilizes modular agents for dynamic task execution based on resource availability.
- Incorporates real-time feedback into inference planning for adaptive strategies.
- Demonstrates practical applications in low-altitude intelligent networks.
Computer Science > Artificial Intelligence arXiv:2602.14003 (cs) [Submitted on 15 Feb 2026] Title:Prompt-Driven Low-Altitude Edge Intelligence: Modular Agents and Generative Reasoning Authors:Jiahao You, Ziye Jia, Chao Dong, Qihui Wu View a PDF of the paper titled Prompt-Driven Low-Altitude Edge Intelligence: Modular Agents and Generative Reasoning, by Jiahao You and Ziye Jia and Chao Dong and Qihui Wu View PDF HTML (experimental) Abstract: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 ...
