@inproceedings{wang-etal-2026-routemoa,
    title = "{R}oute{M}o{A}: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents",
    author = "Wang, Jize  and
      Wu, Han  and
      You, Zhiyuan  and
      Song, Yiming  and
      Wang, Yijun  and
      Shan, Zifei  and
      Li, Yining  and
      Zhang, Songyang  and
      Le, Xinyi  and
      Chen, Cailian  and
      Guan, Xinping  and
      Tao, Dacheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.558/",
    pages = "12190--12208",
    ISBN = "979-8-89176-390-6",
    abstract = "Mixture-of-Agents (MoA) improves LLM performance through layered collaboration, but its dense topology raises costs and latency. Existing methods employ LLM judges to filter responses, yet still require all models to perform inference before judging, failing to cut costs effectively. They also lack model selection criteria and struggle with large model pools, where full inference is costly and can exceed context limits. To address this, we propose **RouteMoA**, an efficient mixture-of-agents framework with dynamic routing. It employs a lightweight *scorer* to perform initial screening by predicting coarse-grained performance from the query, narrowing candidates to a high-potential subset without inference. A *mixture of judges* then refines these scores through lightweight self- and cross-assessment based on existing model outputs, providing posterior correction without additional inference. Finally, a *model ranking* mechanism selects models by balancing performance, cost, and latency. RouteMoA outperforms MoA across varying tasks and model pool sizes, reducing cost by 89.8{\%} and latency by 63.6{\%} in the large-scale model pool. Code is available at https://github.com/Jize-W/RouteMoA."
}