CEPO Unveils General Framework to Boost Prompt Optimization in Multi‑Agent Systems
agents
| Source: HF Papers | Original article
Researchers unveil CEPO, a general framework that separates coordination and task execution to enhance prompt optimization in multi‑agent systems.
A new research paper unveils Coordinator‑Executor Prompt Optimization (CEPO), a framework designed to sharpen prompt engineering for multi‑agent systems (MAS). The authors – Jongha Kim, Seohyun Lee and Jonathan Minho Chung – propose separating system‑level coordination from agent‑level execution by using a shared coordinator prompt alongside individual executor prompts. The framework does not replace existing prompt optimizers; instead, it dictates how prompts are structured and how updates are guided, allowing any optimizer to operate within the split architecture.
The contribution matters because current prompt‑optimization methods typically bundle coordination and task execution into a single agent‑specific prompt, limiting flexibility and often degrading performance as MAS scale. By decoupling these functions, CEPO lets each component be tuned for its distinct role, leading to measurable improvements. The authors report consistent gains across 11 benchmarks, four domains, four multi‑agent system configurations and four different optimizers, suggesting the approach is broadly applicable rather than tied to a single use case.
The next steps will reveal whether CEPO gains traction beyond the paper’s experimental suite. Researchers and developers building collaborative AI agents—from autonomous fleets to conversational assistants—are likely to test the plug‑in principle in their pipelines. Watch for follow‑up studies that integrate CEPO with popular optimization tools, as well as real‑world deployments that could demonstrate its impact on coordination efficiency, robustness, and overall system performance.
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