Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra
  Large-Scale Code Generation and Optimization
Yoichi Ishibashi Yoshimasa Nishimura
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.