CEPO：一个用于多智能体系统的通用提示优化框架
CEPO: A General Framework for Improving Prompt Optimization in Multi-Agent Systems
CEPO 通过将共享协调与智能体特定执行分离，提升了多智能体系统的提示优化效果，并在 11 个跨四个领域的基准测试中持续改进了四种现有提示优化器的性能。该框架使用共享协调器提示和智能体特定执行提示，指导它们根据各自角色更新，使共享协调规则能在所有智能体间一致优化，同时保持执行指令的独立调整。实验表明 CEPO 提升了优化预算效率，并随智能体数量增加而扩展性能。
Published on Oct 26
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Abstract
CEPO improves multi-agent prompt optimization by separating shared coordination from agent-specific execution, boosting performance across benchmarks and scaling efficiently with more agents.
Multi-agent systems (MAS) require both effective coordination and specialized task execution. Existing prompt optimization approaches typically couple these functions within agent-specific prompts. We introduce Coordinator-Executor Prompt Optimization (CEPO), a general framework for improving prompt optimization in MAS. CEPO separates system-level coordination from agent-level execution through a shared coordinator prompt and agent-specific executor prompts, and guides their updates according to their respective roles. This structure allows shared coordination rules to be refined consistently across agents while keeping execution instructions separately adjustable. CEPO provides the prompt structure and optimization guidance, while existing prompt optimizers perform the underlying search. Extensive experiments spanning 11 benchmarks across four domains demonstrate that applying CEPO to four existing prompt optimizers consistently improves their performance. Ablations and qualitative analyses support the proposed design, while controlled studies on a document-understanding MAS further demonstrate improved optimization budget efficiency and performance scaling with the number of agents.
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来源: Hugging Face Daily Papers · huggingface.co