Authors: Kexun Zhang, Weiran Yao, Zuxin Liu, Yihao Feng, Zhiwei Liu, Rithesh Murthy, Tian Lan, Lei Li, Renze Lou, Jiacheng Xu, Bo Pang, Yingbo Zhou, Shelby Heinecke, Silvio Savarese, Huan Wang, Caiming Xiong
Abstract: 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.
Source: http://arxiv.org/abs/2408.07060v1
