POET: Prior-Aware Preference Optimization for Multi-Agent Communication Topology Generation

LLM-based multi-agent systems rely on communication topologies to organize agent participation and information exchange. Successful and failed executions establish graph preferences, yet using these preferences to learn task-adaptive topologies requires interpreting structural differences in the context of task difficulty. We introduce POET (Prior-aware Topology Preference Optimization), a framework for learning task-adaptive communication topologies through prior-guided graph preferences. POET interprets graph complexity relative to task difficulty and uses role diversity to distinguish team compositions beyond size and connectivity. These priors guide the selection of successful and failed graph pairs within each query and their influence in a weighted preference objective, while execution outcomes determine the preference direction. This turns recorded executions into task-aware supervision for the topology generator without additional LLM calls during preference learning. Experiments across six benchmarks show that POET achieves the highest average accuracy among the evaluated methods, while the learned generators transfer to a different LLM backbone without retraining.