面向高效移动智能体 AI 与边缘通用智能的无线上下文工程

Electrical Engineering and Systems Science > Signal Processing arXiv:2602.07321 (eess) [Submitted on 7 Feb 2026 (v1), last revised 16 Sep 2026 (this version, v2)] Title:Wireless Context Engineering for Efficient Mobile Agentic AI and Edge General Intelligence Authors:Changyuan Zhao, Jiacheng Wang, Yunting Xu, Zan Li, Abbas Jamalipour, Dong In Kim View a PDF of the paper titled Wireless Context Engineering for Efficient Mobile Agentic AI and Edge General Intelligence, by Changyuan Zhao and 5 other authors View PDF HTML (experimental) Abstract:Future wireless networks demand increasingly powerful intelligence to support sensing, communication, and autonomous decision-making. While scaling laws suggest improving performance by enlarging model capacity, practical edge deployments are fundamentally constrained by latency, energy, and memory, making unlimited model scaling infeasible. This creates a critical need to maximize the utility of limited inference-time inputs by filtering redundant observations and focusing on high-impact data. In large language models and generative artificial intelligence (AI), context engineering has emerged as a key paradigm to guide inference by selectively structuring and injecting task-relevant information. Inspired by this success, we extend context engineering to wireless systems, providing a systematic way to enhance edge AI performance without increasing model complexity. In dynamic environments, for example, beam prediction can benefit from augmenting instantaneous channel measurements with contextual cues such as user mobility trends or environment-aware propagation priors. We formally introduce wireless context engineering and propose a Wireless Context Communication Framework (WCCF) to adaptively orchestrate wireless context under inference-time constraints. This work provides researchers with a foundational perspective and practical design dimensions to manage the wireless context of wireless edge intelligence. An ISAC-enabled beam prediction case study illustrates the effectiveness of the proposed paradigm under constrained sensing budgets. Comments: Subjects: Signal Processing (eess.SP) Cite as: arXiv:2602.07321 [eess.SP] (or arXiv:2602.07321v2 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2602.07321 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Changyuan Zhao [view email] [v1] Sat, 7 Feb 2026 02:36:54 UTC (1,406 KB) [v2] Wed, 16 Sep 2026 08:24:07 UTC (1,408 KB) Full-text links: Access Paper: View a PDF of the paper titled Wireless Context Engineering for Efficient Mobile Agentic AI and Edge General Intelligence, by Changyuan Zhao and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP new | recent | 2026-02 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)