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On September 7, 2026, arXiv cs.AI published a review article titled “Diffusion Language Models for Mobile Edge Agentic AI: Foundations, Applications, and Challenges,” which proposed diffusion language models (DLMs) as a non-self-reverting alternative to mobile edge agentic artificial intelligence. Unlike traditional self-reverting Transformers, DLMs use an iterative denoising mechanism to support parallel updates of multiple uncertain tokens and utilize bidirectional context, thereby providing a more flexible balance between quality and latency. The review analyzed the applicability of DLMs under constraints such as latency, memory, energy consumption, bandwidth, privacy, and reliability, covering scenarios including resource-efficient architectures, training and inference acceleration, model compression, edge/cloud deployment strategies, communication-aware services, and IoT and wireless applications. The article also discussed long context state management…
Diffusion 语言模型（DLMs）通过迭代去噪而非左到右解码，为移动边缘智能体人工智能提供了非自回归替代方案。与自回归 Transformer 相比，DLMs 可并行更新多个不确定 token 并利用双向上下文，实现更灵活的质量 - 延迟权衡。该综述分析了 DLMs 在延迟、内存、能耗、带宽、隐私及可靠性约束下的适用性，涵盖资源高效架构、训练推理加速、压缩、边缘/云部署、通信感知服务、物联网/无线应用及评估。文章还讨论了长上下文状态管理、分割推理、可信执行、多模态接地和可复现基准测试等开放问题，旨在连接 DLMs 建模特性与未来移动边缘智能的系统级需求。