CORA-Diff: Confidence-Oriented Residual Acceptance for Efficient Diffusion Language Model Inference
Diffusion language models (DLMs) update many tokens in parallel, yet practical decoders often use a fixed denoising horizon. Many predictions stabilize early, but blockwise decoding continues until all positions are resolved, causing repeated dense forward passes. Existing accelerators often rely on learned filters, modified scores, dependency models, or cache-specific mechanisms. We ask whether native trajectory signals can identify residual positions likely to match the deterministic dense endpoint. We propose CORA-Diff, a training-free method that preserves the original transfer rule and applies confidence-and-persistence gating only to positions that rule leaves unresolved. Accepted t...
| Authors | Yifan Wu, Yufeng Zhang, Kenli Li |
|---|---|
| Primary category | cs.AI |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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