[2505.18646] SEW: Self-Evolving Agentic Workflows for Automated Code Generation

Computer Science > Software Engineering arXiv:2505.18646 (cs) [Submitted on 24 May 2025 (v1), last revised 14 Apr 2026 (this version, v2)] Title:SEW: Self-Evolving Agentic Workflows for Automated Code Generation Authors:Siwei Liu, Jinyuan Fang, Han Zhou, Yingxu Wang, Zaiqiao Meng View a PDF of the paper titled SEW: Self-Evolving Agentic Workflows for Automated Code Generation, by Siwei Liu and 4 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have demonstrated effectiveness in code generation tasks. To enable LLMs to address more complex coding challenges, existing research has focused on crafting multi-agent systems with agentic workflows, where complex coding tasks are decomposed into sub-tasks, assigned to specialized agents. Despite their effectiveness, current approaches heavily rely on hand-crafted agentic workflows, with both agent topologies and prompts manually designed, which limits their ability to automatically adapt to different types of coding problems. To address these limitations and enable automated workflow design, we propose \textbf{S}elf-\textbf{E}volving \textbf{W}orkflow (\textbf{SEW}), a novel self-evolving framework that automatically generates and optimises multi-agent workflows. Extensive experiments on three coding benchmark datasets, including the challenging LiveCodeBench, demonstrate that our SEW can automatically design agentic workflows and optimise them through self-evolution, bringing up to 12\% improvement...