@inproceedings{chen-2026-teamxbc,
    title = "{T}eam{XBC} at {BEA} 2026 Shared Task 1: How {AI} (and {I}) won the shared task: Vibe and agentic coding solutions for practical machine learning problems",
    author = "Chen, Xiaobin",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.68/",
    pages = "997--1009",
    ISBN = "979-8-89176-409-5",
    abstract = "The paper describes how the author used AI coding agents and a technique called vibe coding to successfully tackle the BEA 2026 shared task on vocabulary difficulty prediction. Three sets of predictions (runs) were submitted to the competition, corresponding to three experiments the author ran by giving the coding agent different levels of agency: (1) a one-off solution fully planned and implemented by the AI, (2) an AI self-determined iterative process that ran for 24 hours, and (3) a collaborative human-in-the-loop process where solutions were discussed between the author and the AI. Competition results showed that the collaborative mode delivered the best performance, demonstrating that at the current stage domain expert input and decision making are important and necessary for vibe coding solutions to practical machine learning problems."
}