计算机工程2026，Vol.52Issue(1)：22-32,11.DOI:10.19678/j.issn.1000-3428.0252754
基于大语言模型的多智能体系统异常综述(特邀)
A Review of Anomaly in Large Language Model-Based Multi-Agent Systems(Invited)
摘要
Abstract
Large Language Model(LLM)-based Multi-Agent System(MAS)has demonstrated significant potential in handling complex tasks.Their distributed nature and interaction uncertainty can lead to diverse anomalies that threaten system reliability.This paper presents a comprehensive review,identifying and classifying these anomalies systematically.Seven representative multi-agent systems and their corresponding datasets are selected,accounting for 13 418 operational traces,and a hybrid data analysis method is employed,combining preliminary LLM analysis with expert manual validation.A fine-grained,four-level anomaly classification framework is constructed,encompassing the following anomalies:model understanding and perception,agent interaction,task execution,and external environment.Typical cases are analyzed to reveal the underlying logic and external causes of each type of anomaly.Statistical analysis indicates that model understanding and perception anomalies account for the highest proportion,with"context hallucination"and"task instruction misunderstanding"being the primary issues.Agent interaction anomalies represent 16.8％,primarily caused by"information concealment".Task execution anomalies account for 27.1％,mainly characterized by"repetitive decision errors".External environment anomalies account for 18.3％,with"memory conflicts"as the predominant factor.In addition,the model perception and understanding of anomalies often act as root causes,triggering anomalies at other levels,highlighting the importance of enhancing fundamental model capabilities.These classification and root cause analyses aim to provide theoretical support and practical reference for building highly reliable LLM-based multi-agent systems.关键词
大语言模型/智能体/多智能体系统/异常统计/异常分类Key words
Large Language Model(LLM)/agent/Multi-Agent System(MAS)/anomaly statistics/anomaly classification分类
信息技术与安全科学引用本文复制引用
张珑耀,温东新,马庄宇,舒燕君,李庆,刘明义,左德承..基于大语言模型的多智能体系统异常综述(特邀)[J].计算机工程,2026,52(1):22-32,11.基金项目
国家重点研发计划(2024YFB4506000) （2024YFB4506000）
国家自然科学基金(61202091,62171155). （61202091,62171155）
