@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://aclanthology.org/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"
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<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</abstract>
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%0 Conference Proceedings
%T CUETLuminaries0227 at SemEval-2026 Task 13: Invariance-Oriented Representation Learning for Robust AI-Generated Code Detection
%A Chowdhury, Shiti
%A Faisal, Adnan
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F chowdhury-faisal-2026-cuetluminaries0227
%X 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
%U https://aclanthology.org/2026.semeval-1.81/
%P 567-572
Markdown (Informal)
[CUETLuminaries0227 at SemEval-2026 Task 13: Invariance-Oriented Representation Learning for Robust AI-Generated Code Detection](https://aclanthology.org/2026.semeval-1.81/) (Chowdhury & Faisal, SemEval 2026)
ACL