Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems

• URL: http://arxiv.org/abs/2410.02506v1
• Date: Thu, 3 Oct 2024 14:14:31 GMT
• Title: Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems
• Authors: Guibin Zhang, Yanwei Yue, Zhixun Li, Sukwon Yun, Guancheng Wan, Kun Wang, Dawei Cheng, Jeffrey Xu Yu, Tianlong Chen,
• Abstract summary: $texttAgentPrune$ can seamlessly integrate into mainstream multi-agent systems. textbf(I) integrates seamlessly into existing multi-agent frameworks with $28.1%sim72.8%downarrow$ token reduction. textbf(III) successfully defend against two types of agent-based adversarial attacks with $3.5%sim10.8%uparrow$ performance boost.
• Score: 42.137278756052595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in large language model (LLM)-powered agents have shown that collective intelligence can significantly outperform individual capabilities, largely attributed to the meticulously designed inter-agent communication topologies. Though impressive in performance, existing multi-agent pipelines inherently introduce substantial token overhead, as well as increased economic costs, which pose challenges for their large-scale deployments. In response to this challenge, we propose an economical, simple, and robust multi-agent communication framework, termed $\texttt{AgentPrune}$, which can seamlessly integrate into mainstream multi-agent systems and prunes redundant or even malicious communication messages. Technically, $\texttt{AgentPrune}$ is the first to identify and formally define the \textit{communication redundancy} issue present in current LLM-based multi-agent pipelines, and efficiently performs one-shot pruning on the spatial-temporal message-passing graph, yielding a token-economic and high-performing communication topology. Extensive experiments across six benchmarks demonstrate that $\texttt{AgentPrune}$ \textbf{(I)} achieves comparable results as state-of-the-art topologies at merely $\$5.6$ cost compared to their $\$43.7$, \textbf{(II)} integrates seamlessly into existing multi-agent frameworks with $28.1\%\sim72.8\%\downarrow$ token reduction, and \textbf{(III)} successfully defend against two types of agent-based adversarial attacks with $3.5\%\sim10.8\%\uparrow$ performance boost.

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