Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation
Shiyuan Li, Yixin Liu, Qingsong Wen, Chengqi Zhang, Shirui Pan

TL;DR
This paper introduces ARG-Designer, a novel autoregressive graph generation model that automatically designs multi-agent communication topologies tailored to specific tasks, outperforming existing methods in flexibility and efficiency.
Contribution
It reframes MAS design as a conditional autoregressive graph generation task and proposes ARG-Designer to construct customized collaboration graphs from scratch.
Findings
Achieves state-of-the-art performance on six benchmarks.
Demonstrates greater token efficiency than existing approaches.
Provides highly extensible and task-specific communication topologies.
Abstract
Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains. The effectiveness of MAS is critically dependent on its collaboration topology, which has become a focal point for automated design research. However, existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures, significantly limiting their adaptability to task-specific requirements. To address these limitations, we reframe MAS design as a conditional autoregressive graph generation task, where both the system composition and structure are designed jointly. We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch. Conditioned on a…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Multimodal Machine Learning Applications · Topic Modeling
