RADAR: Routing Agents via Difficulty-Aware Recovery

Multi-agent systems (MAS) have emerged as a dominant paradigm where agent routing is a bottleneck for balancing performance and cost. However, existing agent routing methods suffer from the prohibitive cost of exhaustive benchmark construction and the neglect of query complexity essential for mixed tasks. To address this issue, we propose the RADAR (Routing Agents via Difficulty-Aware Recovery) framework. RADAR enables accurate decision-making under limited evaluation cost through two complementary modules. MatrixComp module employs structure-aware matrix completion to recover global performance profiles from sparse observations. Furthermore, DiffMatch module extracts reasoning meta-features for fine-grained alignment via a difficulty-calibrated retrieval mechanism. Extensive experiments demonstrate that RADAR maintains performance equal with state-of-the-art baselines while reducing the cost of benchmark construction by at least 33%.