We consider LLM-based algorithm development through a case study on contractionorder optimisation for tensor networks with OpenEvolve. We pay particular attention to the choice of the LLM as well as design choices such as evaluation metric and test instances. Our results highlight both the promise of verifier-guided evolutionary coding agents for algorithm development/improvement and the continuing importance of evaluation, validation, and interpretation -- and corresponding challenges -- by the human scientist.

翻译：我们以OpenEvolve框架下的张量网络收缩序优化为案例，研究基于大语言模型（LLM）的算法开发。特别关注LLM的选择、评估指标及测试实例等设计方案。研究结果既展现了验证器引导的进化编码代理在算法开发/改进中的潜力，也揭示了在评估、验证和解释过程中人类科学家的重要作用及其面临的持续挑战。