Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing
Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis rather than edit prediction. This setting is poorly matched to modern AI-assisted IDEs, where edit prediction must infer the next code change from recent edits, the current file, and cursor-local context. Such changes are usually sparse and preserve most source tokens, so regenerating a full code segment wastes computation. We propose Diff Diffusion, a diffusion-inspired LLM framework for code editing that predicts edit paths from an original snippet to its revised form. Diff Diffusion represents denoising as structured ed...
| Authors | Xiaolong Xu, Hao Jiang, Bo Jiang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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