How to optimize LLM agent networks for performance?
Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network Design
September 4, 2024
https://arxiv.org/pdf/2409.01411This paper introduces Anaconda, an algorithm for optimizing communication among AI agents in a network to improve performance in complex tasks. It allows agents to dynamically choose whom to communicate with based on their needs and constraints.
Key points for LLM-based systems:
- Faster than traditional methods: Anaconda is particularly beneficial in large networks where communication speed impacts performance.
- Adapts to different network structures: It works with various communication setups, including fully connected or disconnected networks.
- Balances accuracy and speed: It finds a balance between finding the absolute best solution and making quick decisions. This is important for LLM-based agents that need to be responsive.
- Open for further improvement: The paper suggests that the algorithm's performance can be further enhanced, hinting at potential advancements in LLM-based multi-agent communication.