@inproceedings{chowdhury-faisal-2026-cuetluminaries0227,
    title = "{CUETL}uminaries0227 at {S}em{E}val-2026 Task 13: Invariance-Oriented Representation Learning for Robust {AI}-Generated Code Detection",
    author = "Chowdhury, Shiti  and
      Faisal, Adnan",
    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-1.81/",
    pages = "567--572",
    ISBN = "979-8-89176-414-9",
    abstract = "Large language models increasingly generate high-quality source code, making reliable detection of machine-generated code essential for maintaining authorship integrity and software accountability. However, detection systems often degrade under distribution shift, particularly across programming languages and application domains. SemEval-2026 Task 13 Subtask A addresses this challenge through a structured OOD evaluation framework that assesses binary machine-generated code detection across unseen languages and application domains. To mitigate this limitation,we propose a robustness-oriented framework that enhances feature-fused UniXcoder representations with supervised contrastive learning, adversarial language-invariant training and uncertainty-aware filtering to promote stable and shift-resilient representations. Our proposed system achieves a macro-F1 of 0.5411 on the official test set and maintains stable performance under severe language{--}domain shift. Our results demonstrate that domain-level semantic variation is the primary source of degradation under distribution shift, reinforcing the importance of invariance-oriented representations for stable OOD performance"
}