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T01. LLM agent orchestration and harness design

External research on how multiple language-model agents are planned, routed, and evaluated.

  1. T01-14Candidate approachSince 2025

    Multi-Agent Collaboration via Evolving Orchestration

    Authors
    Yufan Dang, Chen Qian, Xueheng Luo, Jingru Fan, Zihao Xie, Ruijie Shi, Weize Chen, Cheng Yang, Xiaoyin Che, Ye Tian, Xuantang Xiong, Lei Han, Zhiyuan Liu, Maosong Sun
    Year
    2025
    Venue
    NeurIPS 2025

    Korean review of T01-14

  2. T01-4Candidate approach

    Generative Agents: Interactive Simulacra of Human Behavior

    Authors
    Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein
    Year
    2023
    Venue
    UIST 2023

    Korean review of T01-4

  3. T01-5Candidate approach

    Reflexion: Language Agents with Verbal Reinforcement Learning

    Authors
    Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao
    Year
    2023
    Venue
    NeurIPS 2023

    Korean review of T01-5

  4. T01-6Candidate approach

    CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

    Authors
    Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem
    Year
    2023
    Venue
    NeurIPS 2023

    Korean review of T01-6

From research to product use

Operating capabilities, pilots, and technologies in development are identified separately.

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