[2510.16635] MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization

Computer Science > Multiagent Systems arXiv:2510.16635 (cs) [Submitted on 18 Oct 2025 (v1), last revised 30 Mar 2026 (this version, v2)] Title:MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization Authors:Wonduk Seo, Juhyeon Lee, Junseo Koh, Wonseok Choi, Hyunjin An, Jian Park, Seunghyun lee, Haihua Chen, Yi Bu View a PDF of the paper titled MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization, by Wonduk Seo and 8 other authors View PDF HTML (experimental) Abstract:Prompt optimization has become a practical way to improve the performance of Large Language Models (LLMs) without retraining. However, most existing frameworks treat evaluation as a black box, relying solely on outcome scores without explaining why prompts succeed or fail. Moreover, they involve repetitive trial-and-error refinements that remain implicit, offering limited interpretability or actionable guidance for systematic improvement. In this paper, we propose MA-SAPO: a new Multi-Agent Reasoning for Score Aware Prompt Optimization framework that links evaluation outcomes directly to targeted refinements. Specifically, in the Training Phase, multiple agents interpret evaluation scores, diagnose weaknesses, and generate concrete revision directives, which are stored as reusable reasoning assets. In the Test Phase, an analyzer agent retrieves relevant exemplars and assets for a new prompt, and a refiner agent applies evidence-based edits to improve the prompt and its response. By gro...