Large Language Model (LLM)-guided evolutionary search is increasingly used for automated algorithm discovery, yet most current methods track search progress primarily through executable programs and scalar fitness. Even when natural-language reasoning is used through heuristic descriptions or reflection, it typically remains transient mutation context or unstructured memory, rather than organized as persistent population-level state over strategic directions. As a result, evolutionary search can struggle to distinguish syntactically different implementations of the same idea, preserve lower-fitness but strategically promising directions, or detect when an entire family of strategies has saturated. We introduce \model, a modular strategy-space layer that turns language-level strategic reasoning into first-class population-level evolutionary state in LLM-driven program search. \model represents each candidate program with an explicit natural-language strategy, clusters the archive by strategy semantics, retrieves behaviorally complementary inspirations, and periodically navigates the strategy landscape to avoid saturated directions. Without modifying the underlying evolutionary algorithms, \model improves existing evolutionary backbones across algorithm discovery, systems optimization, and agent-scaffold design tasks in most settings. Across four systems benchmarks, \model achieves a 20.6% average relative improvement, with the best single run on Prism scoring 3$\times$ higher. These results suggest that persistent strategy representations provide a practical mechanism for improving the effectiveness and cost-efficiency of LLM-guided evolutionary search, pointing toward compound AI systems whose search capabilities benefit from the structured accumulation and reuse of algorithmic strategies.

翻译：大型语言模型（LLM）引导的演化搜索越来越多地用于自动化算法发现，然而当前大多数方法主要通过可执行程序和标量适应度来跟踪搜索进程。即使通过启发式描述或反思使用了自然语言推理，它通常仍是瞬态突变上下文或非结构化记忆，而不是作为持续的人口层面状态组织成战略方向。因此，演化搜索可能难以区分同一想法的语法不同实现，保留适应度较低但战略上有前景的方向，或者检测何时某个策略系列已饱和。我们引入\model，一个模块化的策略空间层，它将语言层面的战略推理转化为LLM驱动程序搜索中第一级的人口层面演化状态。\model用显式的自然语言策略表示每个候选程序，按策略语义对档案进行聚类，检索行为互补的灵感，并定期导航策略景观以避免饱和方向。在不修改底层演化算法的情况下，\model在大多数设置中改进了现有演化基干在算法发现、系统优化和代理框架设计任务上的表现。在四个系统基准测试中，\model实现了20.6%的平均相对改进，其中Prism上的最佳单次运行得分高出3倍。这些结果表明，持久策略表示为提升LLM引导演化搜索的有效性和成本效率提供了实用机制，指向那些搜索能力得益于算法策略的结构化积累与重用的复合AI系统。