@inproceedings{patel-etal-2026-transformertrio,
    title = "{T}ransformer{T}rio at {S}em{E}val-2026 Task 13: Navigating Domain Shift and Representation Instability in Machine-Generated Code Detection",
    author = "Patel, Avi  and
      Laddha, Manthan  and
      Sapovadiya, Pushti  and
      Mishra, Pruthwik  and
      Malviya, Shrikant",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.140/",
    pages = "1015--1026",
    ISBN = "979-8-89176-414-9",
    abstract = "Detecting machine-generated code is increasingly challenging due to advances in code generation models and domain variation across programming tasks. We present our submissions to SemEval-2026 Task 13, evaluating detection in three settings: binary human vs. machine classification, multi-class generator attribution, and four-way authorship classification including hybrid and adversarial cases. We compare feature-based, transformer-based, and hybrid approaches under domain shift and limited supervision. Results show that domain-specific signals often dominate model decisions, degrading generalization when training and test distributions diverge. Increasing model complexity does not consistently improve performance in low-resource or cross-domain settings and may amplify spurious correlations. These findings emphasize robustness and feature alignment over model sophistication for reliable detection."
}