What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems
📰 ArXiv cs.AI
Learn how to optimize multi-agent system communication using action-state protocols to reduce token usage and improve performance
Action Steps
- Analyze existing inter-agent communication strategies to identify areas for optimization
- Design action-state communication protocols to constrain natural language and reduce token usage
- Implement and test the new communication protocols in a multi-agent system
- Evaluate system performance and inference cost before and after implementing the optimized communication protocols
- Refine and adjust the protocols as needed to achieve optimal results
Who Needs to Know This
Researchers and developers working on multi-agent systems, particularly those using large language models, can benefit from this knowledge to improve system efficiency and reduce costs
Key Insight
💡 Constraining natural language in multi-agent communication can significantly improve system efficiency and reduce costs
Share This
🤖 Improve multi-agent system performance with action-state communication protocols! 📊 Reduce token usage and costs with optimized agent communication 🚀
Key Takeaways
Learn how to optimize multi-agent system communication using action-state protocols to reduce token usage and improve performance
Full Article
Title: What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems
Abstract:
arXiv:2606.05304v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language. However, this free-form communication can rapidly inflate token usage, consume the shared context window, and ultimately affect both system performance and inference cost. We analyze five common inter-agent communication strate
Abstract:
arXiv:2606.05304v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language. However, this free-form communication can rapidly inflate token usage, consume the shared context window, and ultimately affect both system performance and inference cost. We analyze five common inter-agent communication strate
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