@inproceedings{zhang-etal-2026-hetero,
    title = "Hetero-Designer: Automated Design of Multi-Agent Systems with Heterogeneous {LLM}s",
    author = "Zhang, Zhiheng  and
      Zhang, Yuanzhe  and
      Yu, Bohan  and
      Zeng, Daojian  and
      Liu, Kang  and
      Zhao, Jun",
    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.1272/",
    pages = "27553--27574",
    ISBN = "979-8-89176-390-6",
    abstract = "LLM-based Multi-agent systems (MAS) have shown strong capabilities across a wide range of domains. Their success largely hinges on the collaboration topology design, which has emerged as a central research focus in the automated MAS design.However, existing approaches are fundamentally constrained by their reliance on homogeneous LLMs, which significantly limits overall system intelligence.In response to this limitation, we for the first time propose the concept of **Automated Design of Heterogeneous-LLMs-based MAS (ADHM)**.ADHM sheds light on a promising avenue for advancing collective intelligence, which focuses on the automated design of cost-effective MAS composed of diverse LLMsand roles to suit various queries.Toward this challenging goal, we propose **Hetero-Designer**, a novel pipeline that efficiently encodes intricate dependencies among queries, LLMs and roles through a novel Binary-Star Transformer and constructs Hetero-MAS in an autoregressive graph generation process. Extensive experiments demonstrate that **Hetero-Designer** is: (1) high-performing on various benchmarks, (2) economical in reducing overhead, (3) extensible to unseen LLMs and roles."
}