2024/10/03 by Guibin Zhang, Yanwei Yue, Zhang, Guibin +15 · 27 citations
Computer Science · Decision Sciences · #Auction Theory and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multi-Agent Systems and Negotiation #Multiagent Systems (cs.MA) #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2410.02506
openalex publication_date 2024/10/03 · openalex created_date 2024/10/30 · openalex updated_date 2026/07/28
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 AgentPrune, which can seamlessly integrate into mainstream multi-agent systems and prunes redundant or even malicious communication messages. Technically, AgentPrune is the first to identify and formally define the 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 AgentPrune (I) achieves comparable results as state-of-the-art topologies at merely $5.6 cost compared to their $43.7, (II) integrates seamlessly into existing multi-agent frameworks with 28.1%∼72.8%\downarrow token reduction, and (III) successfully defend against two types of agent-based adversarial attacks with 3.5%∼10.8%\uparrow performance boost.