标题： 通过检索推理资源和多智能体分析进行提示优化 显示英文标题

标题： Prompt Optimization via Retrieved Reasoning Assets and Multi-Agent Analysis

摘要： 提示优化已成为改进大型语言模型（LLMs）性能的有效替代方法，而不是重新训练。 然而，大多数现有方法将评估视为一个黑箱，仅依赖数值分数，而对提示成功或失败的原因提供有限的见解。 它们还高度依赖于试错式的改进，这难以解释和控制。 在本文中，我们引入了MA-SAPO，一种基于多智能体的评分感知提示优化框架。 与之前的方法相比，MA-SAPO显式地将评估结果与结构化推理相结合，以指导系统的编辑。 该框架具体包括两个阶段：在推理阶段，智能体协作解释指标分数，诊断弱点，并合成可重复使用的推理资产；在测试阶段，智能体检索这些资产以分析优化后的提示，并仅应用基于证据的编辑。 通过将评估信号转化为可解释的推理链，MA-SAPO生成的提示优化更加透明、可审计和可控。 在HelpSteer1/2基准上的实验表明，与单次提示、检索增强基线和先前的多智能体策略相比，取得了持续的改进，验证了我们方法的有效性。 显示英文摘要

摘要： Prompt optimization has emerged as an effective alternative to retraining for improving the performance of Large Language Models (LLMs). However, most existing approaches treat evaluation as a black box, relying solely on numerical scores while offering limited insight into why a prompt succeeds or fails. They also depend heavily on trial-and-error refinements, which are difficult to interpret and control. In this paper, we introduce MA-SAPO, a Multi-Agent framework for Score-Aware Prompt Optimization. Compared to prior methods, MA-SAPO explicitly couples evaluation outcomes with structured reasoning to guide systematic edits. The framework specifically consists of two stages: during the Reasoning Phase, agents collaboratively explain metric scores, diagnose weaknesses, and synthesize targeted refinements that are stored as reusable reasoning assets; during the Test Phase, agents retrieve these assets to analyze optimized prompts and apply only evidence-grounded edits. By turning evaluation signals into interpretable reasoning chains, MA-SAPO produces prompt refinements that are more transparent, auditable, and controllable. Experiments on the HelpSteer1/2 benchmarks demonstrate consistent improvements over single-pass prompting, retrieval-augmented baselines, and prior multi-agent strategies, validating the effectiveness of our approach.