Learning Topology Preferences for Adaptive Communication Topology Generation in Multi-Agent Systems

Large language model (LLM)-based multi-agent systems (MAS) have demonstrated strong capabilities in solving complex tasks, where communication topology plays a critical role in determining performance. However, most methods leverage execution feedback at the whole-graph level, leaving the fine-grained structural decisions underlying topology generation insufficiently explored. In particular, the relative preferences revealed by comparative topology executions are rarely captured as explicit guidance for generation. To this end, we propose Topology Preference-guided Adaptive Generation (TopoPAG), a framework that transforms execution-derived topology preferences into task-specific structural guidance for autoregressive MAS topology generation. Specifically, TopoPAG learns preferences from the comparative utilities of candidate topologies and embeds them into a compact continuous structural space using graph descriptors. The predicted preference provides an initial topology representation beyond discrete template selection. During generation, this representation is adaptively refined based on the partially constructed graph to guide subsequent role and communication-link predictions. Furthermore, the learned preference weights graph generation, allowing all evaluated topologies to contribute according to their relative utility. Experiments on six reasoning and coding benchmarks demonstrate that TopoPAG consistently produces more effective and communication-efficient topologies. Our code is available at https://anonymous.4open.science/r/TopoPAG-7853.