@inproceedings{huang-etal-2026-modeling,
    title = "Modeling {LLM} Agent Reviewer Dynamics in Elo-Ranked Review System",
    author = "Huang, Hsiang-Wei  and
      Lu, Junbin  and
      Chen, Kuang-Ming  and
      Shangguan, Jianxu  and
      Hwang, Jenq-Neng",
    editor = "Gupta, Vivek  and
      Ding, Kaize  and
      Kokel, Harsha  and
      Zhao, Yue  and
      Agarwal, Amit  and
      Wang, Yu  and
      Glass, Michael  and
      Zhang, Yu  and
      Srinivas, Kavitha  and
      Chen, Xiusi  and
      Hassanzadeh, Oktie  and
      Zhu, Qi  and
      Chang, Shuaichen  and
      Luo, Yuan",
    booktitle = "Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the {LLM} Era ({SURG}e{LLM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.surgellm-1.11/",
    pages = "182--189",
    ISBN = "979-8-89176-406-4",
    abstract = "In this work, we explore the Large Language Model (LLM) agent reviewer dynamics in an Elo-ranked review system using real-world conference paper submissions. Multiple LLM agent reviewers with different personas engage in multi round review interactions moderated by an Area Chair. We compare a baseline setting with conditions that incorporate Elo ratings and reviewer memory. Our simulation results showcase several interesting findings, including how incorporating Elo improves Area Chair decision accuracy, as well as reviewers' adaptive review strategies that exploits our Elo system without improving review effort. These findings show how the Elo system affects peer review and offer insights for improving AI conference evaluation. Our code is available at https://github.com/hsiangwei0903/EloReview."
}