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rise_potential_llm_agents_survey [2026/03/30 21:06] – Add inline footnotes agentrise_potential_llm_agents_survey [2026/03/30 22:17] (current) – Restructure: footnotes as references agent
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 ====== The Rise and Potential of Large Language Model Based Agents: A Survey ====== ====== The Rise and Potential of Large Language Model Based Agents: A Survey ======
  
-This landmark survey by Xi et al. (2023) from Fudan NLP Group provides the most comprehensive overview of LLM-based agents, proposing a unifying conceptual framework of **brain, perception, and action** modules.((https://arxiv.org/abs/2309.07864)) With over 1,500 citations, it is the most influential survey in the LLM agent space.+This landmark survey by Xi et al. (2023)(([[https://arxiv.org/abs/2309.07864|Xi et al. (2023) - The Rise and Potential of Large Language Model Based Agents: A Survey]])) from Fudan NLP Group provides the most comprehensive overview of LLM-based agents, proposing a unifying conceptual framework of **brain, perception, and action** modules.((https://arxiv.org/abs/2309.07864)) With over 1,500 citations, it is the most influential survey in the LLM agent space.
  
 ===== Overview ===== ===== Overview =====
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 The survey traces the concept of agents from philosophical origins (Descartes, Locke, Hume) through AI history (symbolic AI, reinforcement learning) to the modern era where LLMs serve as the foundation for general-purpose agents.((https://arxiv.org/abs/2309.07864)) The central thesis: LLMs possess the versatile capabilities needed to serve as a **starting point for designing AI agents that can adapt to diverse scenarios**. The survey traces the concept of agents from philosophical origins (Descartes, Locke, Hume) through AI history (symbolic AI, reinforcement learning) to the modern era where LLMs serve as the foundation for general-purpose agents.((https://arxiv.org/abs/2309.07864)) The central thesis: LLMs possess the versatile capabilities needed to serve as a **starting point for designing AI agents that can adapt to diverse scenarios**.
  
-Published in Science China Information Sciences (2025), the paper covers single-agent systems, multi-agent cooperation, and human-agent interaction.+Published in Science China Information Sciences (2025)(([[https://doi.org/10.1007/s11432-024-4222-0|Published in Science China Information Sciences, 2025]])), the paper covers single-agent systems, multi-agent cooperation, and human-agent interaction.
  
 ===== The Brain-Perception-Action Framework ===== ===== The Brain-Perception-Action Framework =====
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   * **Historical context**: Traces agents from philosophy through classical AI to LLM era   * **Historical context**: Traces agents from philosophy through classical AI to LLM era
   * **Research roadmap**: Identifies open challenges including robustness, safety, and evaluation   * **Research roadmap**: Identifies open challenges including robustness, safety, and evaluation
-  * **1,500+ citations**: Most-cited survey in the LLM agent field((https://arxiv.org/abs/2309.07864))+  * **1,500+ citations**: Most-cited survey in the LLM agent field(([[https://github.com/WooooDyy/LLM-Agent-Paper-List|Companion Paper List Repository]]))((https://arxiv.org/abs/2309.07864))
  
 ===== Code Example ===== ===== Code Example =====
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         return result         return result
 </code> </code>
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-===== References ===== 
- 
-  * [[https://arxiv.org/abs/2309.07864|Xi et al. (2023) - The Rise and Potential of Large Language Model Based Agents: A Survey]] 
-  * [[https://doi.org/10.1007/s11432-024-4222-0|Published in Science China Information Sciences, 2025]] 
-  * [[https://github.com/WooooDyy/LLM-Agent-Paper-List|Companion Paper List Repository]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[generative_agents|Generative Agents]]   * [[generative_agents|Generative Agents]]
   * [[agenttuning|AgentTuning: Instruction-Tuning for Agent Abilities]]   * [[agenttuning|AgentTuning: Instruction-Tuning for Agent Abilities]]
 +
 +===== References =====
  
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