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    <abstract>The rise of code-generating LLMs such as DeepSeek, Qwen, and Meta-LLaMA has improved developer productivity but also increased risks of plagiarism, copyright misuse, and insecure machine-generated code. While AI-text detection is well studied, machine-generated source-code detection especially across multiple languages, LLM families, and OOD conditions-remains underexplored. SemEval-2026 Task 13 addresses this via two subtasks: (A) binary human–machine code detection and (B) multi-class authorship attribution across ten LLM families. For Subtask A, we fine-tune RoBERTa, CodeBERT, GraphCodeBERT, and StarCoderBase-1B, introducing a stratified sampling strategy with class-weighted loss to mitigate imbalance and OOD shifts. For Subtask B, we mitigate the extreme human-class imbalance using undersampling, inverse-frequency weights, syntactic noising, and curriculum-based dual-path training with TinyStarCoderPy and CodeBERT. Both results show that long-context modeling, distribution-aware sampling, and noise-robust training are crucial for reliable in real-world settings. Overall, long-context modeling, distribution-aligned sampling, and lightweight noise-robust training emerge as key factors for reliable machine-generated code detection and authorship attribution.</abstract>
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%0 Conference Proceedings
%T Osint at SemEval-2026 Task 13: A Distribution-Aware Framework for Machine-Generated Code Detection and Multi-Source Authorship Attribution
%A Agrahari, Shifali
%A Anand, Abhishek
%A Kannaujiya, Shubham
%A Singh, Sanasam Ranbir
%A Kumar, Sujit
%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 agrahari-etal-2026-osint
%X The rise of code-generating LLMs such as DeepSeek, Qwen, and Meta-LLaMA has improved developer productivity but also increased risks of plagiarism, copyright misuse, and insecure machine-generated code. While AI-text detection is well studied, machine-generated source-code detection especially across multiple languages, LLM families, and OOD conditions-remains underexplored. SemEval-2026 Task 13 addresses this via two subtasks: (A) binary human–machine code detection and (B) multi-class authorship attribution across ten LLM families. For Subtask A, we fine-tune RoBERTa, CodeBERT, GraphCodeBERT, and StarCoderBase-1B, introducing a stratified sampling strategy with class-weighted loss to mitigate imbalance and OOD shifts. For Subtask B, we mitigate the extreme human-class imbalance using undersampling, inverse-frequency weights, syntactic noising, and curriculum-based dual-path training with TinyStarCoderPy and CodeBERT. Both results show that long-context modeling, distribution-aware sampling, and noise-robust training are crucial for reliable in real-world settings. Overall, long-context modeling, distribution-aligned sampling, and lightweight noise-robust training emerge as key factors for reliable machine-generated code detection and authorship attribution.
%U https://aclanthology.org/2026.semeval-1.360/
%P 2866-2876

Markdown (Informal)

[Osint at SemEval-2026 Task 13: A Distribution-Aware Framework for Machine-Generated Code Detection and Multi-Source Authorship Attribution](https://aclanthology.org/2026.semeval-1.360/) (Agrahari et al., SemEval 2026)

• Osint at SemEval-2026 Task 13: A Distribution-Aware Framework for Machine-Generated Code Detection and Multi-Source Authorship Attribution (Agrahari et al., SemEval 2026)

ACL

• Shifali Agrahari, Abhishek Anand, Shubham Kannaujiya, Sanasam Ranbir Singh, and Sujit Kumar. 2026. Osint at SemEval-2026 Task 13: A Distribution-Aware Framework for Machine-Generated Code Detection and Multi-Source Authorship Attribution. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2866–2876, San Diego, California, USA. Association for Computational Linguistics.