基于大型语言模型 (LLM) 的多代理系统 (MAS) 在处理复杂的协作任务, 方面表现出了希望，其中代理通常通过特定于角色的提示进行编排。虽然这些提示的质量至关重要,，但跨交互代理共同优化它们仍然是一个不小的挑战,，这主要是由于本地代理目标和整体系统目标之间的不一致。为了解决这个,，我们引入了 MASPO, 一个新颖的框架，旨在自动、迭代地完善整个系统的提示。 MASPO 的核心创新是其联合评估机制,，该机制不仅根据其本地有效性, 评估提示，还根据其促进后续代理取得成功的能力来评估提示。这有效地弥合了本地互动和全球结果之间的差距，而无需依赖真实标签。此外，, MASPO 采用数据驱动的进化束搜索来有效地导航高维提示空间。对 6 个不同任务的广泛实证评估表明，MASPO 始终优于最先进的即时优化方法,，平均准确度提高了 2.9。我们在此 https URL 发布我们的代码。
Large language model (LLM)-based Multi-agent systems (MAS) have shown promise in tackling complex collaborative tasks, where agents are typically orchestrated via role-specific prompts. While the quality of these prompts is pivotal, jointly optimizing them across interacting agents remains a non-trivial challenge, primarily due to the misalignment between local agent objectives and holistic system goals. To address this, we introduce MASPO, a novel framework designed to automatically and iteratively refine prompts across the entire system. A core innovation of MASPO is its joint evaluation mechanism, which assesses prompts not merely by their local validity, but by their capacity to facilitate downstream success for successor agents. This effectively bridges the gap between local interactions and global outcomes without relying on ground-truth labels. Furthermore, MASPO employs a data-driven evolutionary beam search to efficiently navigate the high-dimensional prompt space. Extensive empirical evaluations across 6 diverse tasks demonstrate that MASPO consistently outperforms state-of-the-art prompt optimization methods, achieving an average accuracy improvement of 2.9. We release our code at this https URL.
科目: 人工智能 (cs.AI); 计算和语言 (cs.CL); 机器学习 (cs.LG); 多代理系统 (cs.MA)
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)