@inproceedings{arslan-etal-2026-malto,
    title = "{MALTO} at {S}em{E}val-2026 Task 13: Detecting Human, {AI}, and Hybrid Code via Hard Negative Mining and Curriculum-Driven Ensembles",
    author = {Arslan, H{\"u}seyin  and
      Munis, Evren Ayberk  and
      Khudonogov, Timofei  and
      Akgun, Mert  and
      Besli, Murat  and
      Meherrem, Ayhan  and
      Savelli, Claudio  and
      Giobergia, Flavio},
    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-1.304/",
    pages = "2413--2420",
    ISBN = "979-8-89176-414-9",
    abstract = "The rapid advancement of Large Language Models (LLMs) has significantly impacted software engineering, posing challenges for determining the origin and authenticity of source code. This paper presents the MALTO team{'}s submission for SemEval-2026 Task 13, explicitly focusing on Subtask B (Authorship Attribution among 11 classes) and Subtask C (Hybrid Code Detection). To address severe class imbalance and the complex boundaries of mixed human-machine code, we propose a unified framework that leverages an ensemble of UniXcoder and CodeT5. Our approach integrates a robust Tree-sitter-based Universal Canonicalization strategy, Data Augmentation, and a novel 3-Phase Curriculum Training schedule enhanced by Hard Negative Mining. Specifically, UniXcoder{'}s cross-modal representations excel at distinguishing among semantically overlapping LLM families (Subtask B), whereas CodeT5{'}s identifier-aware architecture is superior at detecting subtle structural anomalies in hybrid and adversarial snippets (Subtask C). By aggregating these complementary strengths, our soft-voting ensemble overcomes the limitations of individual models, demonstrating strong robustness against imbalanced distributions and effectively discriminating between purely human, purely machine, hybrid, and adversarial code snippets."
}