Recent advancements in automatic code generation using large language model
(LLM) agent have brought us closer to the future of automated software
development. However, existing single-agent approaches face limitations in
generating and improving large-scale, complex codebases due to constraints in
context length. To tackle this challenge, we propose Self-Organized multi-Agent
framework (SoA), a novel multi-agent framework that enables the scalable and
efficient generation and optimization of large-scale code. In SoA,
self-organized agents operate independently to generate and modify code
components while seamlessly collaborating to construct the overall codebase. A
key feature of our framework is the automatic multiplication of agents based on
problem complexity, allowing for dynamic scalability. This enables the overall
code volume to be increased indefinitely according to the number of agents,
while the amount of code managed by each agent remains constant. We evaluate
SoA on the HumanEval benchmark and demonstrate that, compared to a single-agent
system, each agent in SoA handles significantly less code, yet the overall
generated code is substantially greater. Moreover, SoA surpasses the powerful
single-agent baseline by 5% in terms of Pass@1 accuracy.

最近大型语言模型 (LLM) 代理实现的自动代码生成取得了重要进展，但现有的单代理方法面对生成和改进大规模复杂代码库的限制，该论文提出了自组织多代理框架 (SoA)，这是一种新的多代理框架，能够实现大规模代码的可扩展和高效生成优化。在 SoA 中，自组织代理独立操作以生成和修改代码组件，并无缝协作构建整体代码库，其关键特点是根据问题复杂度自动增加代理数量，从而实现动态可扩展性，使得代码量可以无限增加，而每个代理管理的代码量保持恒定。该论文在 HumanEval 基准上评估了 SoA，并证明与单一代理系统相比，SoA 中的每个代理处理的代码显著减少，但生成的整体代码量大幅增加。此外，SoA 在 Pass@1 准确率方面超过了强大的单代理基准 5%。