智能代理Agent是以智能方式行事的代理;它感知环境,自主采取行动以实现目标,并可以通过学习或获取知识来提高其性能。 本篇主要针对LLM-based Agent,即Agent基于大语言模型进行思考规划,获取信息,并从大模型与外界学习知识并自学习与利用。 多智能体Multi-Agent则是可以通过多个Agent进行协作配合完成更复杂的工作。 特点: ...
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A Survey on LLM-based Autonomous Agents Autonomous agents are designed to achieve specific objectives through self-guided instructions. With the emergence and growth of large language models (LLMs), there is a growing trend in utilizing LLMs as fundamental controllers for these autonomous agents. Wh...
该工作主要梳理了LLM-based Agent 中的规划(planning)能力,原文链接: Understanding the planning of LLM agents: A surveyarxiv.org/abs/2402.02716 文章中,作者将planning能力进一步细分为了五个维度: 任务分解(Task Decomposition) 规划选择(Plan Selection) 外部辅助规划(External Planner) 反馈和改进(Reflectio...
GitHub地址:https://github.com/MobileLLM/Personal_LLM_Agents_Survey Personal_LLM_Agents_Survey的使用方法 1、个人LLM代理的关键能力 (1)、任务自动化 任务自动化是个人LLM代理的核心能力,它决定了代理能够多好地响应用户命令和/或自动执行用户任务。由于UI-based任务自动化代理在这个列表中很受欢迎并与个人设备密...
Notably, LLM-based multi-agent systems (MAS) are considered a promising pathway towards realizing general artificial intelligence that is equivalent to or surpasses human-level intelligence. In this paper, we present a comprehensive survey of these studies, offering a systematic review of LLM-based ...
GitHub地址:https://github.com/MobileLLM/Personal_LLM_Agents_Survey Personal_LLM_Agents_Survey的使用方法 1、个人LLM代理的关键能力 (1)、任务自动化 任务自动化是个人LLM代理的核心能力,它决定了代理能够多好地响应用户命令和/或自动执行用户任务。由于UI-based任务自动化代理在这个列表中很受欢迎并与个人设备密...
论文链接:[2404.13501] A Survey on the Memory Mechanism of Large Language Model based Agents (arxiv.org) 这篇论文是由人大高瓴发表的论文,主要是对基于大语言模型的智能体的记忆化机制进行调研。是切入LLM …
1. Introduction 本篇工作是UCSB发表在期刊TACL上的一篇Survey,原文链接: Just a moment...本文全面回顾了自动更正大型语言模型(Automatically Correcting LLMs)的最新进展,并提出了一个分类框架,将现有的自…
🤖 How to Implement the Memory of LLM-based Agent 🤖 How to Evaluate the Memory in LLM-based Agent If you find this survey useful for your research or development, please cite our paper: @misc{zhang2024survey, title={A Survey on the Memory Mechanism of Large Language Model based Agents...