SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial intelligence (AI) by refining tokens through iterative denoising rather than left-to-right decoding. Compared with autoregressive Transformer-based large language models (LLMs), DLMs can update multiple uncertain tokens in parallel and exploit bidirectional context throughout the generation process, enabling more flexible quality-latency trade-offs beyond fixed sequential decoding. These properties are particularly attractive for edge agents, where partial refinement, early exit, and cons

Read original source ↗ Open in workspace

recordType

paper

region

Global