NES：一种基于学习历史编辑轨迹的无指令、低延迟的下一步编辑建议框架
NES: An Instruction-Free, Low-Latency Next Edit Suggestion Framework Powered by Learned Historical Editing Trajectories
AI总结：
NES通过学习历史编辑轨迹，为代码编辑提供无指令、低延迟的建议，实现75.6%的定位准确率和27.7%的精确匹配率，已在蚂蚁集团服务超2万名开发者。
摘要：
代码编辑是软件开发中频繁但认知负担重的任务。现有AI工具常通过要求显式自然语言指令打断开发者流程，并存在高延迟，限制了实际应用。我们提出了NES（Next Edit Suggestion），一种无指令、低延迟的代码编辑框架，利用学习的历史编辑轨迹隐式捕捉开发者的目标和编码习惯。NES采用双模型架构：一个模型预测下一个编辑位置，另一个生成精确的代码变更，均无需用户指令。在我们开源的SFT和DAPO数据集上训练，NES实现了最先进的性能（75.6%的定位准确率，27.7%的精确匹配率），并在250毫秒内提供建议。部署于蚂蚁集团，NES通过无缝的Tab键交互服务超过2万名开发者，实现了51.55%的有效接受率（定位预测）和43.44%（编辑建议），展示了其在真实开发工作流中的实际影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
