Can LLMs evolve to build entire software?
SELF-EVOLVING MULTI-AGENT COLLABORATION NET- WORKS FOR SOFTWARE DEVELOPMENT
October 24, 2024
https://arxiv.org/pdf/2410.16946This paper presents a novel self-evolving multi-agent collaboration network (EvoMAC) for software development. Inspired by neural networks, EvoMAC iteratively adapts its agents and connections during testing to improve code generation based on environmental feedback. Key points relevant to LLM-based multi-agent systems:
- Self-evolution: EvoMAC uses a novel textual backpropagation algorithm to adjust agent prompts and workflow during test time based on feedback, enabling dynamic adaptation to task requirements.
- Requirement-oriented benchmark: A new benchmark, rSDE-Bench, is introduced, featuring complex software requirements and automated evaluation, moving beyond function-level coding tasks.
- Effectiveness of multi-agent collaboration: EvoMAC consistently outperforms single-agent and previous multi-agent systems, highlighting the power of collaboration and self-evolution in LLM-based systems.