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arXiv AI By Chenqi Li, Minghui Min, Dusit Niyato, Wei Ni

Diffusion Language Models for Mobile Edge Agentic AI: Foundations, Applications, and Challenges

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Diffusion language models (DLMs) provide a non‑autoregressive approach for mobile edge agentic AI, refining tokens through iterative denoising instead of left‑to‑right decoding. They can update multiple uncertain tokens in parallel and use bidirectional context, allowing flexible quality‑latency trade‑offs and early exits that reduce response delay and communication overhead. The survey reviews DLM foundations, resource‑efficient architectures, training and inference acceleration, compression, deployment strategies, and discusses open issues such as long‑context management, split inference, and trustworthy execution.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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