RankEvolve：基于大语言模型驱动的进化算法实现检索算法的自动化发现

RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution

February 18, 2026

作者: Jinming Nian, Fangchen Li, Dae Hoon Park, Yi Fang

cs.AI

摘要

诸如BM25和狄利克雷平滑查询似然这类检索算法仍是高效强健的一阶排序器，但其改进多依赖于参数调优与人工经验。本研究探索在评估器引导下，通过进化搜索驱动大语言模型自动发现更优的词汇检索算法。我们提出RankEvolve——一种基于AlphaEvolve的程序演化框架，将候选排序算法表示为可执行代码，并依据在BEIR和BRIGHT的12个IR数据集上的检索效果进行迭代变异、重组与筛选。该系统以BM25和狄利克雷平滑查询似然两个种子程序为起点，最终演化出的算法不仅具有新颖性且效果显著，在完整BEIR/BRIGHT基准以及TREC DL 19/20数据集上均展现出良好的迁移性能。实验结果表明，评估器引导的LLM程序演化为自动发现新型排序算法提供了可行路径。

English

Retrieval algorithms like BM25 and query likelihood with Dirichlet smoothing remain strong and efficient first-stage rankers, yet improvements have mostly relied on parameter tuning and human intuition. We investigate whether a large language model, guided by an evaluator and evolutionary search, can automatically discover improved lexical retrieval algorithms. We introduce RankEvolve, a program evolution setup based on AlphaEvolve, in which candidate ranking algorithms are represented as executable code and iteratively mutated, recombined, and selected based on retrieval performance across 12 IR datasets from BEIR and BRIGHT. RankEvolve starts from two seed programs: BM25 and query likelihood with Dirichlet smoothing. The evolved algorithms are novel, effective, and show promising transfer to the full BEIR and BRIGHT benchmarks as well as TREC DL 19 and 20. Our results suggest that evaluator-guided LLM program evolution is a practical path towards automatic discovery of novel ranking algorithms.