AutoSG: 仅从任务提示出发的LLM驱动的昂贵优化求解器生成
AutoSG: LLM-Driven Solver Generation Solely from Task Prompts for Expensive Optimization
AI总结：
提出AutoSG框架，通过检索增强生成、单步自优化和无实例评估机制，从自然语言提示直接生成可执行定制求解器，解决昂贵优化中的幻觉、结构破坏和评估成本问题。
摘要：
昂贵优化任务在现实应用中普遍存在，需要高度专业化的求解器。虽然LLM驱动的自动求解器生成显示出前景，但当前范式在处理昂贵优化时面临三个关键问题：由于领域知识不足导致的事实幻觉、在细化过程中频繁破坏先前建立的局部最优结构，以及在训练实例上执行带来的高昂评估成本和受限的泛化能力。为了解决这些问题，我们引入了AutoSG，一个完全自动化的流程，直接将自然语言提示转换为可执行的定制求解器。AutoSG具有三个核心创新：一个检索增强的求解器生成模块，严格将代码基于经过验证的文献；一个单步自优化算子，在保留关键结构组件的同时引入特定任务的改进；以及一个基于Elo的无实例LLM-as-a-Judge评估机制，快速建立全局排名。在多种昂贵优化任务上的广泛评估证实，AutoSG显著优于人工设计的最先进框架和现有的LLM生成的求解器。
英文摘要：
Expensive optimization tasks are ubiquitous in real-world applications, demanding highly specialized solvers. While LLM-driven automated solver generation shows promise, current paradigms face three critical issues when tackling expensive optimization: factual hallucinations due to deficient domain knowledge, the frequent dismantling of previously established locally optimal structures during refinement, and the prohibitive evaluation costs alongside restricted generalization caused by executing on training instances. To address these issues, we introduce AutoSG, a fully automated workflow directly translating natural language prompts into executable customized solvers. AutoSG features three core innovations: a retrieval-augmented solver generation module strictly grounding code in verified literature; a one-step self-refinement operator introducing task-specific improvements while preserving critical structural components; and an instance-free Elo-based LLM-as-a-Judge evaluation mechanism rapidly establishing global rankings. Extensive evaluations across diverse expensive optimization tasks confirm AutoSG significantly outperforms human-designed state-of-the-art frameworks and existing LLM-generated solvers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
