FLOWROUTER: EXPERIENCE-GUIDED ROUTING FOR QUERY-ADAPTIVE MULTI-AGENT WORKFLOWS

Large language model (LLM)-based multi-agent systems can improve problem solving through collaborative reasoning, yet a uniform workflow may incur redundant computation for some queries while providing insufficient reasoning capacity for others. Current methods include both manually designed and automatically optimized workflows, but adapting complete workflow configurations to individual queries remains challenging. To address this, we propose FlowRouter, an experience-guided framework for query-adaptive multi-agent workflow routing. FlowRouter adopts an explicit graph-based factorization of workflow construction into conditionally dependent decisions over topology, collaboration scale, and position-wise LLM assignment. The resulting routing decisions are jointly optimized under a shared execution-level performance–cost reward, with retrieved workflow experience providing auxiliary guidance. Across four benchmarks, FlowRouter achieves the highest macro-average performance among the evaluated methods while maintaining a favorable inference-cost trade-off. Further analysis shows that topology selection varies significantly across MATH subfields but not across official difficulty levels, suggesting that workflow adaptation reflects characteristics beyond scalar difficulty.