来源: https://arxiv.org/abs/2503.21460
[2503.21460] Large Language Model Agent: A Survey on Methodology, Applications and Challenges
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Computer Science > Computation and Language
arXiv:2503.21460 (cs)[Submitted on 27 Mar 2025] Title:Large Language Model Agent: A Survey on Methodology, Applications and Challenges Authors:Junyu Luo, Weizhi Zhang, Ye Yuan, Yusheng Zhao, Junwei Yang, Yiyang Gu, Bohan Wu, Binqi Chen, Ziyue Qiao, Qingqing Long, Rongcheng Tu, Xiao Luo, Wei Ju, Zhiping Xiao, Yifan Wang, Meng Xiao, Chenwu Liu, Jingyang Yuan, Shichang Zhang, Yiqiao Jin, Fan Zhang, Xian Wu, Hanqing Zhao, Dacheng Tao, Philip S. Yu, Ming Zhang View a PDF of the paper titled Large Language Model Agent: A Survey on Methodology, Applications and Challenges, by Junyu Luo and 25 other authors View PDF HTML (experimental)
Abstract:The era of intelligent agents is upon us, driven by revolutionary advancements in large language models. Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence. This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy, linking architectural foundations, collaboration mechanisms, and evolutionary pathways. We unify fragmented research threads by revealing fundamental connections between agent design principles and their emergent behaviors in complex environments. Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time, while also addressing evaluation methodologies, tool applications, practical challenges, and diverse application domains. By surveying the latest developments in this rapidly evolving field, we offer researchers a structured taxonomy for understanding LLM agents and identify promising directions for future research. The collection is available at this https URL.
Comments: 329 papers surveyed, resources are at this https URL
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2503.21460[cs.CL]
(or arXiv:2503.21460v1[cs.CL] for this version)
https://doi.org/10.48550/arXiv.2503.21460
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arXiv-issued DOI via DataCite
Submission history From: Junyu Luo[view email][v1] Thu, 27 Mar 2025 12:50:17 UTC (814 KB)
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