@inproceedings{tang-etal-2026-scalable,
    title = "A Scalable Multi-{LLM} Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement",
    author = "Tang, Shengji  and
      Cao, Jianjian  and
      Lin, Weihao  and
      Hong, Jiale  and
      Zhang, Bo  and
      Hu, Shuyue  and
      Bai, Lei  and
      Chen, Tao  and
      Ouyang, Wanli  and
      Ye, Peng",
    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.578/",
    pages = "12679--12697",
    ISBN = "979-8-89176-390-6",
    abstract = "Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks, leading to suboptimal performance. To address this, we propose SMCS, a Scalable Multi-LLM Collaboration System designed to effectively coordinate multiple open-source LLMs. The system consists of two core components: a Retrieval-based Prior Selection (RPS) module, which dynamically selects the most suitable LLMs for each input, and an Exploration{--}Exploitation-Driven Posterior Enhancement (EPE) module, which fosters response diversity and selects high-quality outputs through a hybrid scoring mechanism. Experiments on eight mainstream benchmarks validate the effectiveness of our system: by integrating fifteen open-source LLMs, SMCS outperforms prevailing closed-source LLMs, e.g., GPT-4.1(**+5.36{\%}**) and GPT-o3-mini(**+5.28{\%}**) across multiple tasks. Remarkably, it even exceeds the average of best results on different datasets with open-source LLMs (**+2.86{\%}**), significantly advancing the empirical performance frontier of open-source collaboration. The code is released at https://github.com/magent4aci/SMCS."
}