用大模型优化张量网络收缩顺序，提升算法开发效率。
Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks
- 以验证器引导的进化编码代理实现算法自动优化。
- 大模型在复杂任务中表现良好，但需人工评估与验证。
- 适合对自动化算法设计感兴趣的科研人员。
我们通过OpenEvolve案例研究，探讨了基于大语言模型（LLM）的算法开发方法，重点关注LLM选择、评估指标和测试实例的设计。研究结果表明，验证器引导的进化编码代理在算法开发与改进方面具有巨大潜力，同时也凸显出人类科学家在评估、验证和解释过程中的关键作用，以及由此带来的持续挑战。
原文摘要 · Abstract (English)
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.
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