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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. T01-13Similar problemSince 2025

    Why Do Multi-Agent LLM Systems Fail?

    Authors
    Mert Cemri, Melissa Z. Pan, Shuyi Yang, Lakshya A. Agrawal, Bhavya Chopra, Rishabh Tiwari, Kurt Keutzer, Aditya Parameswaran, Dan Klein, Kannan Ramchandran, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica
    Year
    2025
    Venue
    NeurIPS 2025 Datasets and Benchmarks Track spotlight

    Korean review of T01-13

  2. 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

  3. T01-19Similar problemSince 2025

    tau^2-Bench: Evaluating Conversational Agents in a Dual-Control Environment

    Authors
    Victor Barres, Honghua Dong, Soham Ray, Xujie Si, Karthik Narasimhan
    Year
    2026
    Venue
    arXiv 2025-06-09

    Korean review of T01-19

  4. T01-12Similar problemSince 2025

    tau-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

    Authors
    Shunyu Yao, Noah Shinn, Pedram Razavi, Karthik Narasimhan
    Year
    2025
    Venue
    ICLR 2025 Poster

    Korean review of T01-12

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