研究人员开发了一种名为BaSE（基于老虎机的自进化）的新方法，用于优化进化搜索系统中大型语言模型（LLM）调用的分配。该方法通过在并行轨迹中动态分配LLM调用来解决LLM引导搜索中结果不一致的问题。BaSE在平均适应度方面比现有基线提高了12.3%，尤其是在高方差场景下提高了可靠性，而无需更改底层模型或提示。 AI
影响 优化LLM资源分配，可能提高复杂搜索任务的效率和可靠性。
排序理由 该集群包含一篇研究论文，详细介绍了在进化搜索中优化LLM使用的新方法。
在 arXiv cs.NE (Neural & Evolutionary) 阅读 →
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2605.29268v1 Announce Type: cross Abstract: LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocu…
LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocumented. We ask how a fixed budget of LLM calls sho…
LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocumented. We ask how a fixed budget of LLM calls sho…
LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocumented. We ask how a fixed budget of LLM calls sho…