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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.21460 |
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| _version_ | 1866912297410101248 |
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| author | Luo, Junyu Zhang, Weizhi Yuan, Ye Zhao, Yusheng Yang, Junwei Gu, Yiyang Wu, Bohan Chen, Binqi Qiao, Ziyue Long, Qingqing Tu, Rongcheng Luo, Xiao Ju, Wei Xiao, Zhiping Wang, Yifan Xiao, Meng Liu, Chenwu Yuan, Jingyang Zhang, Shichang Jin, Yiqiao Zhang, Fan Wu, Xian Zhao, Hanqing Tao, Dacheng Yu, Philip S. Zhang, Ming |
| author_facet | Luo, Junyu Zhang, Weizhi Yuan, Ye Zhao, Yusheng Yang, Junwei Gu, Yiyang Wu, Bohan Chen, Binqi Qiao, Ziyue Long, Qingqing Tu, Rongcheng Luo, Xiao Ju, Wei Xiao, Zhiping Wang, Yifan Xiao, Meng Liu, Chenwu Yuan, Jingyang Zhang, Shichang Jin, Yiqiao Zhang, Fan Wu, Xian Zhao, Hanqing Tao, Dacheng Yu, Philip S. Zhang, Ming |
| contents | 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 https://github.com/luo-junyu/Awesome-Agent-Papers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21460 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model Agent: A Survey on Methodology, Applications and Challenges Luo, Junyu Zhang, Weizhi Yuan, Ye Zhao, Yusheng Yang, Junwei Gu, Yiyang Wu, Bohan Chen, Binqi Qiao, Ziyue Long, Qingqing Tu, Rongcheng Luo, Xiao Ju, Wei Xiao, Zhiping Wang, Yifan Xiao, Meng Liu, Chenwu Yuan, Jingyang Zhang, Shichang Jin, Yiqiao Zhang, Fan Wu, Xian Zhao, Hanqing Tao, Dacheng Yu, Philip S. Zhang, Ming Computation and Language 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 https://github.com/luo-junyu/Awesome-Agent-Papers. |
| title | Large Language Model Agent: A Survey on Methodology, Applications and Challenges |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.21460 |