arXiv Artificial Intelligence

CAI-DLLM: Convergence Aware Inference for Diffusion Language Models

16 days ago 25.08.2026 07:00 👁 2 views
CAI-DLLM: Convergence Aware Inference for Diffusion Language Models

Quick summary

arXiv:2608.22646v1 Announce Type: new Abstract: Diffusion language models can generate many tokens in parallel, but they still require repeated denoising steps during inference. This makes generation costly, especially when the model continues to recompute tokens that are already stable. To address these limitations, we propose CAI-DLLM, a training-free inference method that uses first-step confidence to guide denoising and reduce inference time. Specifically, CAI-DLLM commits easy tokens earlier, allocates more denoising steps to harder tokens, and adjusts decoding schedules across output blo

Key takeaways

  • arXiv:2608.22646v1 Announce Type: new Abstract: Diffusion language models can generate many tokens in parallel, but they still require repeated denoising steps during inference.
  • This makes generation costly, especially when the model continues to recompute tokens that are already stable.
  • To address these limitations, we propose CAI-DLLM, a training-free inference method that uses first-step confidence to guide denoising and reduce inference time.

Why it matters

“CAI-DLLM: Convergence Aware Inference for Diffusion Language Models” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗