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蓝图2代码:通过蓝图规划和修复来实现可靠的代码生成的多代理管道
Kehao Mao1, Baokun Hu1, Ruixin Lin1
1School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
Frontiers in artificial intelligence
|November 3, 2025
概括
蓝图2Code是一个新的多代理框架,通过模仿复杂代码生成的人类工作流来增强自动编程. 该系统显著提高了从自然语言提示生成可靠代码的准确性和稳定性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 软件工程 软件工程 软件工程
背景情况:
- 自动编程和代码生成对于开发人员的生产力和可访问性至关重要.
- 大型语言模型 (LLM) 已经将自然语言推进到代码合成,但在需要多步推理和强有力的解决方案的复杂任务中扎.
- 现有的基于LLM的代码生成方法在任务复杂性增加时,在准确性和可靠性方面存在局限性.
研究的目的:
- 引入Blueprint2Code,这是一个多代理框架,旨在克服当前LLM在复杂代码生成中的局限性.
- 模拟类似人类的编程工作流程,以改进程序合成.
- 为了提高生成代码的准确性,稳定性和可靠性.
主要方法:
- 开发了Blueprint2Code,这是一个多代理框架,包括四个代理:预览,蓝图,编码和调试.
- 实施了一个闭环系统,集成了任务理解,规划,实施和代改进.
- 通过使用基准数据集评估框架在复杂编程任务上的表现.
主要成果:
- 与现有方法相比,Blueprint2Code在复杂的编程任务上表现出优越的性能.
- 在基准数据集上实现了高通过@1准确性:HumanEval (96.3%),MBPP (88.4%),HumanEval-ET (86.5%),MBPP-ET (59.4%) 和APPS (24.6%).
- 该框架的多代理方法有效地解决了理解问题的意图和多步推理的挑战.
结论:
- 蓝图2Code通过模拟人类编程工作流提供了复杂代码生成的有效解决方案.
- 多代理框架显著提高了自然语言程序合成的准确性和稳定性.
- 这种方法代表了自动编程和基于LLM的代码生成能力的实质性进步.
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