arXiv AI By Rui Ye, Keduan Huang, Qimin Wu, Yuzhu Cai, Tian Jin, Xianghe Pang, Xiangrui Liu, Jiaqi Su, Chen Qian, Bohan Tang, Kaiqu Liang, Jiaao Chen, Yue Hu, Zhenfei Yin, Rongye Shi, Bo An, Yang Gao, Wenjun Wu, Lei Bai, Siheng Chen

MASLab: A Unified and Comprehensive Codebase for LLM-based Multi-Agent Systems

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arXiv:2505. 16988v2 Announce Type: replace-cross Abstract: LLM-based multi-agent systems (MAS) have demonstrated significant potential in enhancing single LLMs to address complex and diverse tasks in practical applications.

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arXiv AI
2d ago

Understanding Issues, Causes and Solutions in Open-Source LLM-based Multi-Agent Systems

The paper investigates challenges in open‑source large‑language‑model (LLM) based multi‑agent systems (MAS). By analyzing 944 issues extracted from 21 projects, it finds that orchestration and execution problems are most common, with workflow, tool integration, and memory issues as primary causes. The predominant remedy identified is optimizing workflow, and the study offers empirically grounded implications for improving orchestration, tool integration, and memory mechanisms in LLM‑based MAS.

By Asad Ur Rehman, Syed Mohammad Kashif, Ruiyin Li, Peng Liang, Zengyang Li, Arif Ali Khan
arXiv AI
Jul 16

AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities

arXiv:2607. 13705v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.

By Zichen Ding, Jiaye Ge, Shufan Jiang, Kai Chen, Mo Li, Qingqiu Li, Zehao Li, Zonglin Li, Tiaohao Liang, Shudong Liu, Zerun Ma, Zixing Shang, Wenhui Tian, Zun Wang, Liwei Wu, Zhenyu Wu, Jun Xu, Bowen Yang, Dingbo Yuan, Qi Zhang, Songyang Zhang, Peiheng Zhou, Dongsheng Zhu