Prompt Optimization via Retrieved Reasoning Assets and Multi-Agent Analysis

• URL: http://arxiv.org/abs/2510.16635v1
• Date: Sat, 18 Oct 2025 20:21:09 GMT
• Title: Prompt Optimization via Retrieved Reasoning Assets and Multi-Agent Analysis
• Authors: Wonduk Seo, Juhyeon Lee, Junseo Koh, Hyunjin An, Jian Park, Seunghyun Lee, Haihua Chen, Yi Bu,
• Abstract summary: We introduce MA-SAPO, a Multi-Agent framework for Score-Aware Prompt Optimization.<n>Compared to prior methods, MA-SAPO explicitly couples evaluation outcomes with structured reasoning to guide systematic edits.<n>By turning evaluation signals into interpretable reasoning chains, MA-SAPO produces prompt refinements that are more transparent, auditable, and controllable.
• Score: 5.935239028627343
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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