@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Single-Agent Generation Surpasses Multi-Agent Systems in Semantic Diversity
%A Encheng, Cui
%A Peng, Shaowen
%A Ito, Kazuhiro
%A Jinsha, X. U.
%A Shohei, Hisada
%A Wakamiya, Shoko
%A Aramaki, Eiji
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F encheng-etal-2026-single
%X 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.
%U https://aclanthology.org/2026.findings-acl.1894/
%P 37993-38008
Markdown (Informal)
[Single-Agent Generation Surpasses Multi-Agent Systems in Semantic Diversity](https://aclanthology.org/2026.findings-acl.1894/) (Encheng et al., Findings 2026)
ACL
- Cui Encheng, Shaowen Peng, Kazuhiro Ito, XU Jinsha, Hisada Shohei, Shoko Wakamiya, and Eiji Aramaki. 2026. Single-Agent Generation Surpasses Multi-Agent Systems in Semantic Diversity. In Findings of the Association for Computational Linguistics: ACL 2026, pages 37993–38008, San Diego, California, United States. Association for Computational Linguistics.