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CEPO: General Framework for Improving Prompt Optimization in Multi-Agent Systems

A paper submitted to arXiv on 26 October 2026 introduces Coordinator-Executor Prompt Optimization (CEPO), a framework that separates system-level coordination from agent-level execution in multi-agent systems through a shared coordinator prompt and agent-specific executor prompts. The authors report that applying CEPO to four existing prompt optimizers consistently improved their performance across 11 benchmarks spanning four domains, with ablations and a controlled document-understanding multi-agent study showing better optimization budget efficiency and performance scaling with the number of agents. CEPO supplies only the prompt structure and optimization guidance, leaving the underlying search to existing optimizers.

read2 min views1 publishedNov 2, 2026
CEPO: General Framework for Improving Prompt Optimization in Multi-Agent Systems
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  [Submitted on 26 Oct 2026]


[View PDF](https://arxiv.org/pdf/2610.34722)

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Abstract: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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