{"slug": "control-data-flow-separation-stable-prompt-optimization-in-multi-agent-llms", "title": "Control-Data Flow Separation: Stable Prompt Optimization in Multi-Agent LLMs", "summary": "Researchers propose a control-data flow separation approach for stable prompt optimization in multi-agent large language model (LLM) systems, addressing the entanglement of task-relevant content and execution-critical protocols such as message routing, output formatting, and termination signals. The method aims to improve optimization stability by decoupling these roles.", "body_md": "Prompt optimization can improve multi-agent LLM systems, but the prompts being optimized often serve two entangled roles: generating task-relevant content and specifying execution-critical protocols, such as message routing, output formatting, and termination signals, on which the underlying code re", "url": "https://wpnews.pro/news/control-data-flow-separation-stable-prompt-optimization-in-multi-agent-llms", "canonical_source": "https://aiflash.com/news/112563/", "published_at": "2026-09-02 02:30:03+00:00", "updated_at": "2026-09-02 02:51:36.769280+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-agents"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/control-data-flow-separation-stable-prompt-optimization-in-multi-agent-llms", "markdown": "https://wpnews.pro/news/control-data-flow-separation-stable-prompt-optimization-in-multi-agent-llms.md", "text": "https://wpnews.pro/news/control-data-flow-separation-stable-prompt-optimization-in-multi-agent-llms.txt", "jsonld": "https://wpnews.pro/news/control-data-flow-separation-stable-prompt-optimization-in-multi-agent-llms.jsonld"}}