The Rise and Potential of Large Language Model Based Agents: A Survey

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv non-exclusive distribution license" does not allow redistributing the text itself. Original — arxiv.org/pdf/2309.07864

Summary

Earlier AI-agent research mostly refined algorithms or training schemes for narrow skills such as Go or chess. As a result, the field lacked a general foundation for building adaptable agents. This survey from the Fudan NLP Group argues that large language models can fill that gap because of their knowledge, instruction following, reasoning, planning and generalization abilities. The authors first trace the agent concept from philosophy into AI and explain why an LLM makes a suitable core controller. They then organize the literature around a modular framework with three parts:

a brain (the LLM, handling language, knowledge, memory, reasoning and planning),
a perception module that extends input beyond text,
an action module covering text, tools and embodied behavior.

Applications are reviewed for single agents, cooperating or competing multi-agent systems, and human–agent collaboration. The survey also covers simulated agent societies and the social behavior that emerges in them. As a review, it reports no new experiments. Its contribution is a shared taxonomy, a curated paper list and an agenda of open issues, including evaluation, safety, scaling and the path to AGI.

Key points

arXiv:2309.07864 (v3, Sept 2023), written by a large team from the Fudan NLP Group. A companion paper list is maintained at github.com/WooooDyy/LLM-Agent-Paper-List.
The survey uses the five-level World Scope scheme (Corpus, Internet, Perception, Embodiment, Social). Plain LLMs sit at level two. Adding perception and action could lift agents to levels three and four, and agent societies could reach level five.
Perception is split into textual, visual, auditory and other inputs. Action is split into textual output, tool use and embodied action.
Agent societies are studied in text-based, virtual sandbox and physical environments. Physical settings are flagged as needing stricter safety rules, because errors there can cause irreversible harm.
Among the open problems, the authors propose "Agent as a Service" (AaaS/LLMAaaS), extending the LMaaS idea. They note concerns about privacy, controllability, robustness and misuse.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: AI & machine learning

Cited by

348 papers of our library cite it (of the 140673 whose bibliographies we read); 11 of them have a page here, 337 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2309.07864 · arXiv 2309.07864

@misc{xi2023rise,
  title = {{The Rise and Potential of Large Language Model Based Agents: A Survey}},
  author = {Xi, Zhiheng and Chen, Wenxiang and Guo, Xin and He, Wei and Ding, Yiwen and Hong, Boyang and Zhang, Ming and Wang, Junzhe and Jin, Senjie and Zhou, Enyu and Zheng, Rui and Fan, Xiaoran and Wang, Xiao and Xiong, Limao and Zhou, Yuhao and Wang, Weiran and Jiang, Changhao and Zou, Yicheng and Liu, Xiangyang and Yin, Zhangyue and Dou, Shihan and Weng, Rongxiang and Cheng, Wensen and Zhang, Qi and Qin, Wenjuan and Zheng, Yongyan and Qiu, Xipeng and Huang, Xuanjing and Gui, Tao},
  year = {2023},
  doi = {10.48550/arxiv.2309.07864},
  eprint = {2309.07864},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2309.07864}
}

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