@inproceedings{zhou-etal-2026-identifying,
title = "Identifying Collective Intelligence Factor in {LLM} Agent Groups for Generalizable Multi-Agent System Design",
author = "Zhou, Zhilun and
Liu, Zihan and
Liu, Jiahe and
Wang, Yihan and
Shao, Qingyu and
Xu, Fengli and
Jin, Depeng and
Li, Yong",
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.624/",
pages = "12827--12842",
ISBN = "979-8-89176-395-1",
abstract = "Large language model (LLM)-based multi-agent systems (MASs) have shown impressive performance in solving a wide range of complex problems. However, previous studies mainly focus on designing customized MAS for specific tasks, while a critical research problem remains unclear: Do LLM agent groups exhibit a form of ``general intelligence'' that reflects their general ability across various tasks? Researchers have found a Collective Intelligence (CI) factor in human groups that captures their general capability. Inspired by this, in this study, we aim to investigate whether an analogous CI factor also exists in LLM agent groups, which is crucial for building generalizable MAS. Motivated by human cognitive psychology experiments, we construct 108 LLM agent groups with diverse group sizes, LLM compositions, and communication topologies. We systematically evaluate these groups across a wide range of tasks and analyze their performances. Our results demonstrate that an Artificial Collective Intelligence (ACI) factor can be extracted from LLM agent groups to predict the generalization performance on new tasks. Inspired by this, we train a model to predict the ACI based on the features of MAS, and show that it can be used as a plug-in to enhance the generalization ability of MAS optimization methods."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhou-etal-2026-identifying">
<titleInfo>
<title>Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zhilun</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zihan</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiahe</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yihan</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qingyu</namePart>
<namePart type="family">Shao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fengli</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Depeng</namePart>
<namePart type="family">Jin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yong</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Large language model (LLM)-based multi-agent systems (MASs) have shown impressive performance in solving a wide range of complex problems. However, previous studies mainly focus on designing customized MAS for specific tasks, while a critical research problem remains unclear: Do LLM agent groups exhibit a form of “general intelligence” that reflects their general ability across various tasks? Researchers have found a Collective Intelligence (CI) factor in human groups that captures their general capability. Inspired by this, in this study, we aim to investigate whether an analogous CI factor also exists in LLM agent groups, which is crucial for building generalizable MAS. Motivated by human cognitive psychology experiments, we construct 108 LLM agent groups with diverse group sizes, LLM compositions, and communication topologies. We systematically evaluate these groups across a wide range of tasks and analyze their performances. Our results demonstrate that an Artificial Collective Intelligence (ACI) factor can be extracted from LLM agent groups to predict the generalization performance on new tasks. Inspired by this, we train a model to predict the ACI based on the features of MAS, and show that it can be used as a plug-in to enhance the generalization ability of MAS optimization methods.</abstract>
<identifier type="citekey">zhou-etal-2026-identifying</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.624/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>12827</start>
<end>12842</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design
%A Zhou, Zhilun
%A Liu, Zihan
%A Liu, Jiahe
%A Wang, Yihan
%A Shao, Qingyu
%A Xu, Fengli
%A Jin, Depeng
%A Li, Yong
%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 zhou-etal-2026-identifying
%X Large language model (LLM)-based multi-agent systems (MASs) have shown impressive performance in solving a wide range of complex problems. However, previous studies mainly focus on designing customized MAS for specific tasks, while a critical research problem remains unclear: Do LLM agent groups exhibit a form of “general intelligence” that reflects their general ability across various tasks? Researchers have found a Collective Intelligence (CI) factor in human groups that captures their general capability. Inspired by this, in this study, we aim to investigate whether an analogous CI factor also exists in LLM agent groups, which is crucial for building generalizable MAS. Motivated by human cognitive psychology experiments, we construct 108 LLM agent groups with diverse group sizes, LLM compositions, and communication topologies. We systematically evaluate these groups across a wide range of tasks and analyze their performances. Our results demonstrate that an Artificial Collective Intelligence (ACI) factor can be extracted from LLM agent groups to predict the generalization performance on new tasks. Inspired by this, we train a model to predict the ACI based on the features of MAS, and show that it can be used as a plug-in to enhance the generalization ability of MAS optimization methods.
%U https://aclanthology.org/2026.findings-acl.624/
%P 12827-12842
Markdown (Informal)
[Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design](https://aclanthology.org/2026.findings-acl.624/) (Zhou et al., Findings 2026)
ACL
- Zhilun Zhou, Zihan Liu, Jiahe Liu, Yihan Wang, Qingyu Shao, Fengli Xu, Depeng Jin, and Yong Li. 2026. Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design. In Findings of the Association for Computational Linguistics: ACL 2026, pages 12827–12842, San Diego, California, United States. Association for Computational Linguistics.