@inproceedings{sun-etal-2026-topodim,
title = "{T}opo{DIM}: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems",
author = {Sun, Rui and
Ding, Jie and
Gong, Chenghua and
Gu, Tianjun and
Jiang, Yihang and
Zhang, Juyuan and
Pan, Liming and
L{\"u}, Linyuan},
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.207/",
pages = "4252--4269",
ISBN = "979-8-89176-395-1",
abstract = "Optimizing communication topology in LLM{--}based multi-agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio-temporal interaction paradigms, where the sequential execution of multi-round dialogues incurs high latency and computation. Motivated by the recent insights that evaluation and debate mechanisms can improve problem-solving in multi-agent systems, we propose TopoDIM, a framework for one-shot Topology generation with Diverse Interaction Modes. Designed for decentralized execution to enhance adaptability and privacy, TopoDIM enables agents to autonomously construct heterogeneous communication without iterative coordination, achieving token efficiency and improved task performance. Experiments demonstrate that TopoDIM reduces total token consumption by 46.41{\%} while improving average performance by 1.50{\%} over state-of-the-art methods. Moreover, the framework exhibits strong adaptability in organizing communication among heterogeneous agents. Code is available at: https://github.com/Sundiasy/TopoDIM."
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<abstract>Optimizing communication topology in LLM–based multi-agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio-temporal interaction paradigms, where the sequential execution of multi-round dialogues incurs high latency and computation. Motivated by the recent insights that evaluation and debate mechanisms can improve problem-solving in multi-agent systems, we propose TopoDIM, a framework for one-shot Topology generation with Diverse Interaction Modes. Designed for decentralized execution to enhance adaptability and privacy, TopoDIM enables agents to autonomously construct heterogeneous communication without iterative coordination, achieving token efficiency and improved task performance. Experiments demonstrate that TopoDIM reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. Moreover, the framework exhibits strong adaptability in organizing communication among heterogeneous agents. Code is available at: https://github.com/Sundiasy/TopoDIM.</abstract>
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%0 Conference Proceedings
%T TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems
%A Sun, Rui
%A Ding, Jie
%A Gong, Chenghua
%A Gu, Tianjun
%A Jiang, Yihang
%A Zhang, Juyuan
%A Pan, Liming
%A Lü, Linyuan
%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 sun-etal-2026-topodim
%X Optimizing communication topology in LLM–based multi-agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio-temporal interaction paradigms, where the sequential execution of multi-round dialogues incurs high latency and computation. Motivated by the recent insights that evaluation and debate mechanisms can improve problem-solving in multi-agent systems, we propose TopoDIM, a framework for one-shot Topology generation with Diverse Interaction Modes. Designed for decentralized execution to enhance adaptability and privacy, TopoDIM enables agents to autonomously construct heterogeneous communication without iterative coordination, achieving token efficiency and improved task performance. Experiments demonstrate that TopoDIM reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. Moreover, the framework exhibits strong adaptability in organizing communication among heterogeneous agents. Code is available at: https://github.com/Sundiasy/TopoDIM.
%U https://aclanthology.org/2026.findings-acl.207/
%P 4252-4269
Markdown (Informal)
[TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems](https://aclanthology.org/2026.findings-acl.207/) (Sun et al., Findings 2026)
ACL
- Rui Sun, Jie Ding, Chenghua Gong, Tianjun Gu, Yihang Jiang, Juyuan Zhang, Liming Pan, and Linyuan Lü. 2026. TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems. In Findings of the Association for Computational Linguistics: ACL 2026, pages 4252–4269, San Diego, California, United States. Association for Computational Linguistics.