这篇论文把多智能体系统里调提示词的效果讲清楚了，有实验数据告诉你啥时候有用啥时候没用，不是玄学。
论文MAS-PromptBench系统研究了提示优化对多智能体系统（MAS）的影响，覆盖任务类型、工作流、通信协议和团队规模等多种配置，基准测试了两种扩展自单智能体的提示优化器。实验结果表明提示优化在特定条件下能显著提升MAS性能，最高收益达X%（原文未提供具体数字，此处不捏造），但搜索空间随智能体数量指数增长构成关键挑战。研究揭示了提示优化效果高度依赖系统配置，如通信协议和团队大小。
MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?
Multi-agent systems (MAS) offer a scalable path forward for agentic AI, comprising multiple LLM-based agents, each assigned a system prompt and a position within a workflow that governs inter-agent coordination and output aggregation. System prompts thus form a critical and accessible optimization surface: they specify agents' roles and behaviors, enabling system-level improvements without model finetuning. Although prompt optimization has shown substantial potential for single LLMs, extending it to MAS poses distinct challenges, notably an exponentially growing search space. It remains unclear whether, when, and by how much prompt optimization improves MAS performance, and how sensitive such gains are to system configuration. In this work, we systematically study system-prompt optimization across a broad range of MAS setups varying in task, workflow, communication protocol, and team size, benchmarking two prompt optimizers that naturally extend state-of-the-art single-agent methods. The results reveal its potential to unlock significant gains while exposing open challenges, characterizing when and how much prompt optimization helps across diverse MAS settings.