AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results show that serving diffusion language models needs parallelism at the level of each denoising step, which differs from AR serving in how admission and eviction interact with an already shared forward pass.
This work proposes CAI-DLLM, a training-free inference method that uses first-step confidence to guide denoising and reduce inference time, and evaluates CAI-DLLM on LLaDA-8B-Instruct and Dream-7B-Instruct across math, code, reasoning, commonsense, and long-context tasks.
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