多样性赋能智能：整合软件工程智能体的专业能力
Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents
AI总结：
针对现有软件工程智能体各有优劣的问题，提出元模块框架DEI整合多智能体专业能力，在SWE-Bench Lite基准上实现性能大幅提升，最优组解决率达55%位居榜首。
摘要：
大语言模型（LLM）智能体在解决真实世界软件工程（SWE）问题方面展现出巨大潜力。最先进的开源SWE智能体在SWE-Bench Lite基准上可解决超过27%的真实GitHub问题。然而，这些成熟的智能体框架各有优劣，在部分任务中表现出色，在另一些任务中则表现不佳。为充分利用这些智能体的多样性，我们提出DEI（Diversity Empowered Intelligence，多样性赋能智能）框架，以发挥它们各自独特的专业能力。DEI作为现有SWE智能体框架之上的元模块运行，通过管理智能体集群提升问题解决能力。实验结果表明，由DEI引导的智能体委员会能够大幅超越单个最优智能体的性能。例如，一组在SWE-Bench Lite上单个最高解决率为27.3%的开源SWE智能体，借助DEI可达到34.3%的解决率，提升了25%，并超越了大多数闭源解决方案。我们表现最佳的智能体组达到了55%的解决率，在SWE-Bench Lite上位居榜首。我们的研究成果为协作式AI系统及其解决复杂软件工程挑战的潜力相关的研究体系提供了新的贡献。
英文摘要：
Large language model (LLM) agents have shown great potential in solving real-world software engineering (SWE) problems. The most advanced open-source SWE agent can resolve over 27% of real GitHub issues in SWE-Bench Lite. However, these sophisticated agent frameworks exhibit varying strengths, excelling in certain tasks while underperforming in others. To fully harness the diversity of these agents, we propose DEI (Diversity Empowered Intelligence), a framework that leverages their unique expertise. DEI functions as a meta-module atop existing SWE agent frameworks, managing agent collectives for enhanced problem-solving. Experimental results show that a DEI-guided committee of agents is able to surpass the best individual agent's performance by a large margin. For instance, a group of open-source SWE agents, with a maximum individual resolve rate of 27.3% on SWE-Bench Lite, can achieve a 34.3% resolve rate with DEI, making a 25% improvement and beating most closed-source solutions. Our best-performing group excels with a 55% resolve rate, securing the highest ranking on SWE-Bench Lite. Our findings contribute to the growing body of research on collaborative AI systems and their potential to solve complex software engineering challenges.
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。
