MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
📰 ArXiv cs.AI
Learn to jointly optimize prompts for LLM-based multi-agent systems using MASPO, improving collaborative task performance
Action Steps
- Implement MASPO to jointly optimize role-specific prompts across agents
- Evaluate the performance of MASPO using metrics such as collaboration efficiency and system goal achievement
- Compare the results of MASPO with traditional prompt optimization methods
- Apply MASPO to real-world multi-agent systems to improve collaborative task performance
- Test the scalability of MASPO in large-scale multi-agent systems
Who Needs to Know This
Researchers and engineers working on LLM-based multi-agent systems can benefit from this technique to improve system performance and achieve holistic goals
Key Insight
💡 MASPO enables joint prompt optimization across interacting agents, aligning local objectives with holistic system goals
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🤖 Jointly optimize prompts for LLM-based multi-agent systems with MASPO! 🚀
Key Takeaways
Learn to jointly optimize prompts for LLM-based multi-agent systems using MASPO, improving collaborative task performance
Full Article
Title: MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
Abstract:
arXiv:2605.06623v1 Announce Type: new Abstract: Large language model (LLM)-based Multi-agent systems (MAS) have shown promise in tackling complex collaborative tasks, where agents are typically orchestrated via role-specific prompts. While the quality of these prompts is pivotal, jointly optimizing them across interacting agents remains a non-trivial challenge, primarily due to the misalignment between local agent objectives and holistic system goals. To address this, we introduce MASPO, a novel
Abstract:
arXiv:2605.06623v1 Announce Type: new Abstract: Large language model (LLM)-based Multi-agent systems (MAS) have shown promise in tackling complex collaborative tasks, where agents are typically orchestrated via role-specific prompts. While the quality of these prompts is pivotal, jointly optimizing them across interacting agents remains a non-trivial challenge, primarily due to the misalignment between local agent objectives and holistic system goals. To address this, we introduce MASPO, a novel
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