CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation
Researchers have introduced CodeCoR, a multi-agent framework for code generation that employs four large language model (LLM) agents to generate prompts, code, test cases, and repair advice. By pruning low-quality outputs at each stage, CodeCoR aims to reduce error propagation in sequential code generation workflows. Experiments on HumanEval and MBPP datasets show that CodeCoR achieves an average Pass@1 score of 77.13%, outperforming existing baselines.
Why it matters: This work demonstrates a significant advance in code generation reliability by integrating self-reflection and output pruning in a multi-agent LLM framework, addressing a key limitation of sequential LLM-based approaches.
Full story at: arXiv Software Engineering ↗