无需指令的低延迟代码补全框架，自动预测下一步编辑位置和内容。
NES: An Instruction-Free, Low-Latency Next Edit Suggestion Framework Powered by Learned Historical Editing Trajectories
- 基于历史编辑轨迹学习，无须用户输入即可预测下一次修改。
- 定位准确率75.6%，精确匹配率27.7%，响应时间低于250ms。
- 已部署于蚂蚁集团，服务超2万开发者，接受率达43.44%。
代码编辑是软件开发中频繁但认知负荷高的任务。现有AI工具常因需要显式自然语言指令且延迟高，打断开发者的思维流，限制了实际可用性。本文提出NES（Next Edit Suggestion），一种无需指令、低延迟的代码编辑建议框架，通过学习历史编辑轨迹隐式捕捉开发者目标与编码习惯。NES采用双模型架构：一个模型预测下一个编辑位置，另一个生成精确代码变更，均无需用户指令。在开源的SFT与DAPO数据集上训练后，达到75.6%的位置准确率和27.7%的精确匹配率，建议生成时间低于250ms。已在蚂蚁集团部署，通过无缝的Tab键交互服务超过2万名开发者，位置预测有效接受率为51.55%，代码编辑接受率为43.44%，验证了其在真实开发流程中的实用价值。
原文摘要 · Abstract (English)
Code editing is a frequent yet cognitively demanding task in software development. Existing AI-powered tools often disrupt developer flow by requiring explicit natural language instructions and suffer from high latency, limiting real-world usability. We present NES (Next Edit Suggestion), an instruction-free, low-latency code editing framework that leverages learned historical editing trajectories to implicitly capture developers' goals and coding habits. NES features a dual-model architecture: one model predicts the next edit location and the other generates the precise code change, both without any user instruction. Trained on our open-sourced SFT and DAPO datasets, NES achieves state-of-the-art performance (75.6% location accuracy, 27.7% exact match rate) while delivering suggestions in under 250ms. Deployed at Ant Group, NES serves over 20,000 developers through a seamless Tab-key interaction, achieving effective acceptance rates of 51.55% for location predictions and 43.44% for edits, demonstrating its practical impact in real-world development workflows.
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