How to optimize multi-agent submodular maximization with unreliable communication?
Optimality Gap of Decentralized Submodular Maximization under Probabilistic Communication
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This paper studies how reliable communication is crucial for the performance of decentralized, multi-agent AI systems that aim to optimize some global objective. When messages between agents can be lost, the system's performance degrades predictably, and the researchers provide ways to calculate and potentially improve this degradation.
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For LLM-based multi-agent systems, this research highlights that even if LLMs are good at making individual decisions, the reliability of their communication will heavily influence the overall system performance. The paper provides a framework to analyze and potentially improve this communication bottleneck by strategically allowing some agents to send duplicate messages.