如何引导大语言模型生成：用于自动启发式设计的双代理引导搜索
How to Guide LLM Generation: Dual-Surrogate Guided Search for Automated Heuristic Design
AI总结：
研究如何在有限预算下引导大语言模型进行自动启发式设计，提出双代理引导搜索方法，通过两个互补代理评分及不确定性感知规则选择行动，在多样套件中表现出色，超越简单排名和固定偏好。
AI中文摘要：
大语言模型通过从任务描述和评估器反馈中生成可执行启发式代码，使自动启发式设计越来越实用。然而，在有限的查询和评估预算下，搜索效率关键取决于生成前的决策。现有方法通常用预定义规则选择行动，仅间接建模每个具体操作-父行动的预期结果。因此，我们提出双代理引导搜索（\method{}），一种用于基于大语言模型的自动启发式设计中操作-父选择的代理引导行动选择模块。\method{}通过用两个互补代理对生成前行动进行评分来引导大语言模型代码生成过程。具体而言，提出一个转换代理来预测由操作-父行动诱导的子表示的潜在分布，同时提出一个实例条件效用代理来估计采样子潜在的预期性能。此外，我们提出一个不确定性感知获取规则，结合预测效用、效用不确定性和转换不确定性来选择下一个大语言模型生成行动。在一个多样化的启发式设计套件中，\method{}与强大的基于大语言模型的自动启发式设计基线具有竞争力，消融和行动选择分析表明其行为超越了简单的存档排名或固定操作偏好。
英文摘要：
Large language models (LLMs) have made automated heuristic design (AHD) increasingly practical by generating executable heuristic code from task descriptions and evaluator feedback. Yet under a limited query and evaluation budget, search efficiency depends critically on a pre-generation decision. Before each LLM query and black-box evaluation, the system must choose which archived heuristics to reuse as parents and which generation operator should transform them. Existing methods typically choose such actions with predefined rules, leaving the expected outcome of each concrete operator-parent action only indirectly modeled. Therefore, we propose \emph{\fullmethod{}} (\method{}), a surrogate-guided action-selection module for operator-parent selection in LLM-based AHD. \method{} guides the LLM code-generation process by scoring pre-generation actions with two complementary surrogates. Specifically, a transition surrogate is proposed to predict the latent distribution of the child representation induced by an operator-parent action, while an instance-conditioned utility surrogate is proposed to estimate the expected performance of sampled child latents. Moreover, we propose an uncertainty-aware acquisition rule that combines predicted utility, utility uncertainty, and transition uncertainty to select the next LLM generation action. Across a diverse heuristic-design suite, \method{} is competitive with strong LLM-AHD baselines, and ablation and action-selection analyses suggest that its behavior goes beyond simple archive ranking or fixed operator preferences.
