TopoRanker: From Successful Graphs to Utility-Aware Topology Ranking for Multi-Agent Reasoning

Learning communication topologies for multi-agent systems requires not only identifying structures that can successfully solve a task, but also distinguishing their collaborative utility. Successful-graph reconstruction treats observed feasible structures as supervision targets, yet does not explicitly compare the cost differences among multiple successful graphs or identify which roles and communication links contribute meaningfully to task completion. We propose TopoRanker, which shifts communication topology learning from successful-graph imitation to verifier-driven structural utility learning. During training, TopoRanker retains the complete execution outcomes of multiple candidate graphs for the same task, captures differences in overall utility through graph-level preferences, and derives local structural credit by removing nodes and edges. A task-conditioned graph utility model jointly learns from these complementary signals. At test time, the model ranks candidate graphs before execution and runs only the selected communication topology. Across eight benchmarks, compared with the fixed multi-agent baseline achieving the highest accuracy on each benchmark, TopoRanker improves accuracy on six tasks and matches it on one, yielding a macro-average accuracy gain of $4.91$ percentage points while reducing token consumption by $51.95\%$ on average across benchmarks. On HumanEval, a $0.77$-percentage-point decrease in accuracy is accompanied by a $64.79\%$ reduction in token usage. Further selector analyses and supervision ablations demonstrate the benefits of jointly learning from global preferences and local structural feedback, while revealing task-dependent trade-offs between performance and communication cost.