@inproceedings{shen-etal-2025-understanding,
title = "Understanding the Information Propagation Effects of Communication Topologies in {LLM}-based Multi-Agent Systems",
author = "Shen, Xu and
Liu, Yixin and
Dai, Yiwei and
Wang, Yili and
Miao, Rui and
Tan, Yue and
Pan, Shirui and
Wang, Xin",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.623/",
pages = "12358--12372",
ISBN = "979-8-89176-332-6",
abstract = "The communication topology in large language model-based multi-agent systems fundamentally governs inter-agent collaboration patterns, critically shaping both the efficiency and effectiveness of collective decision-making. While recent studies for communication topology automated design tend to construct sparse structures for efficiency, they often overlook why and when sparse and dense topologies help or hinder collaboration. In this paper, we present a causal framework to analyze how agent outputs, whether correct or erroneous, propagate under topologies with varying sparsity. Our empirical studies reveal that moderately sparse topologies, which effectively suppress error propagation while preserving beneficial information diffusion, typically achieve optimal task performance. Guided by this insight, we propose a novel topology design approach, EIB-Learner, that balances error suppression and beneficial information propagation by fusing connectivity patterns from both dense and sparse graphs. Extensive experiments show the superior effectiveness, communication cost, and robustness of EIB-Learner."
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%0 Conference Proceedings
%T Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems
%A Shen, Xu
%A Liu, Yixin
%A Dai, Yiwei
%A Wang, Yili
%A Miao, Rui
%A Tan, Yue
%A Pan, Shirui
%A Wang, Xin
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F shen-etal-2025-understanding
%X The communication topology in large language model-based multi-agent systems fundamentally governs inter-agent collaboration patterns, critically shaping both the efficiency and effectiveness of collective decision-making. While recent studies for communication topology automated design tend to construct sparse structures for efficiency, they often overlook why and when sparse and dense topologies help or hinder collaboration. In this paper, we present a causal framework to analyze how agent outputs, whether correct or erroneous, propagate under topologies with varying sparsity. Our empirical studies reveal that moderately sparse topologies, which effectively suppress error propagation while preserving beneficial information diffusion, typically achieve optimal task performance. Guided by this insight, we propose a novel topology design approach, EIB-Learner, that balances error suppression and beneficial information propagation by fusing connectivity patterns from both dense and sparse graphs. Extensive experiments show the superior effectiveness, communication cost, and robustness of EIB-Learner.
%U https://aclanthology.org/2025.emnlp-main.623/
%P 12358-12372
Markdown (Informal)
[Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems](https://aclanthology.org/2025.emnlp-main.623/) (Shen et al., EMNLP 2025)
ACL
- Xu Shen, Yixin Liu, Yiwei Dai, Yili Wang, Rui Miao, Yue Tan, Shirui Pan, and Xin Wang. 2025. Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 12358–12372, Suzhou, China. Association for Computational Linguistics.