利用LLM反思自身经验指导进化搜索，打造可解释的程序化策略，AI自我优化新范式
arXiv:2606.16496v1 Announce Type: cross Abstract: Large multimodal language models (LLMs) have emerged as powerful tools for guiding evolutionary sear…
LLM引导进化搜索，发现双变量循环码，革新量子纠错码发现效率
arXiv:2606.02418v1 Announce Type: cross Abstract: Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifyi…
探索LLM与进化搜索结合时，代码进化究竟在优化什么——对算法设计的关键追问
arXiv:2605.20086v1 Announce Type: cross Abstract: Recent work pairs LLMs with evolutionary search to iteratively generate, modify, and select code usi…
探索LLM与进化搜索结合时，执行基础设施设计对算法发现成功的关键影响，揭示三大工程设计问题。
arXiv:2605.15221v1 Announce Type: cross Abstract: AlphaEvolve and FunSearch have demonstrated the potential of combining large language models (LLMs) …