---
id: 20260618-T0-14
title: "多代理系统并发异常检测研究"
title_en: "Multi-Agent System Anomaly Detection"
url: https://ai.daily.yangsir.net/daily/20260618-T0-14
issue_date: 2026-06-18
publish_date: 2026-06-17T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2606.17182
---

# 多代理系统并发异常检测研究

arXiv论文提出多代理LLM系统并发异常检测方法。通过建模共享存储的读写操作，检测竞争条件和死锁等并发问题。实验显示该方法在5个代理系统上识别准确率达94%，降低系统故障率87%。

## English Version

**Multi-Agent System Anomaly Detection**

arXiv paper introduces concurrency anomaly detection for multi-agent LLM systems. By modeling shared store operations, it detects race conditions and deadlocks. Achieves 94% accuracy in testing across 5 systems, reducing failures by 87%.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2606.17182)

**详情页**：https://ai.daily.yangsir.net/daily/20260618-T0-14

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