Skill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems
📰 ArXiv cs.AI
Learn to evolve meta-skills for automatic multi-agent systems using LLMs, improving model capability and experience retention
Action Steps
- Implement Skill-MAS to evolve meta-skills for automatic MAS generation
- Use LLMs as the foundation for MAS generation
- Apply gradient updates to internalize experience and improve model capability
- Evaluate the performance of Skill-MAS using metrics such as search efficiency and experience retention
- Compare the results with existing methods to demonstrate the effectiveness of Skill-MAS
Who Needs to Know This
Researchers and developers working on multi-agent systems and LLMs can benefit from this approach to improve their models' performance and adaptability
Key Insight
💡 Evolving meta-skills for automatic multi-agent systems can improve model capability and experience retention, leading to better performance in complex tasks
Share This
🤖 Evolve meta-skills for automatic multi-agent systems with Skill-MAS! 🚀 Improve model capability and experience retention using LLMs
Full Article
Title: Skill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems
Abstract:
arXiv:2606.18837v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks. However, existing methods face a dilemma between model capability and experience retention. Inference-time MAS leverages frozen frontier LLMs but repeats identical searches without learning from past experience. Conversely, Training-time MAS internalizes experience via gradient updates but is constraine
Abstract:
arXiv:2606.18837v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks. However, existing methods face a dilemma between model capability and experience retention. Inference-time MAS leverages frozen frontier LLMs but repeats identical searches without learning from past experience. Conversely, Training-time MAS internalizes experience via gradient updates but is constraine
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