@inproceedings{encheng-etal-2026-single,
    title = "Single-Agent Generation Surpasses Multi-Agent Systems in Semantic Diversity",
    author = "Encheng, Cui  and
      Peng, Shaowen  and
      Ito, Kazuhiro  and
      Jinsha, XU  and
      Shohei, Hisada  and
      Wakamiya, Shoko  and
      Aramaki, Eiji",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1894/",
    pages = "37993--38008",
    ISBN = "979-8-89176-395-1",
    abstract = "Multi-Agent Systems (MAS) are commonly used to improve reasoning diversity and robustness by simulating interactions among agents with distinct roles. However, prior work often entangles the contribution of the multi-agent architecture with that of prompt conditioning, making the source of observed diversity gains unclear. We address this confound with a controlled study on divergent thinking tasks, using identical prompt conditioning for MAS and single agent baseline. Under these matched conditions, single agent setups consistently outperform multi-agent systems in semantic diversity. We attribute this gap to information visibility: parallel agents often converge on overlapping ideas, whereas a single agent model can condition on its own generation to avoid redundancy. We further find that a Multi-Output strategy, which prompts a single agent to produce multiple responses within a single inference pass, achieves the highest diversity without degrading logical validity. Together, these results point to a more efficient and effective way to expand diversity, with implications for the design of more efficient agentic frameworks."
}