@inproceedings{chandrasekar-etal-2026-medhastra-semeval,
title = "{M}ed{H}astra at {S}em{E}val-2026 Task 13: Stylometric Ensembles and Transformer Fine-Tuning for Robust {AI} Code Detection, Attribution, and Adversarial Analysis",
author = "Chandrasekar, Shruti and
S, Vedajanaani R and
P, Vijayalakshmi",
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-1.264/",
pages = "2098--2103",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes Team MedHastra{'}s submission to SemEval-2026 Task 13 on detecting machine-generated code across diverse programming languages, generators, and application scenarios. We participated in all three subtasks: (A) binary detection of AI-generated code under out-of-distribution conditions, (B) multi-class attribution across ten large language model families, and (C) classification of human, fully AI-generated, hybrid, and adversarial code.For Subtask A, we implemented a stylometric ensemble combining structural formatting features with word- and character-level TF-IDF representations, trained using Random Forest, Gradient Boosting, and Logistic Regression with soft voting. For Subtasks B and C, we fine-tuned CodeBERT to leverage contextual code representations, incorporating class balancing strategies such as downsampling and weighted cross-entropy.Our results demonstrate that handcrafted stylometric features struggle under strong distribution shift, while transformer-based contextual modeling is more effective for fine-grained attribution and hybrid/adversarial detection. The study highlights the importance of robust contextual representations for realistic AI-assisted programming scenarios."
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<abstract>This paper describes Team MedHastra’s submission to SemEval-2026 Task 13 on detecting machine-generated code across diverse programming languages, generators, and application scenarios. We participated in all three subtasks: (A) binary detection of AI-generated code under out-of-distribution conditions, (B) multi-class attribution across ten large language model families, and (C) classification of human, fully AI-generated, hybrid, and adversarial code.For Subtask A, we implemented a stylometric ensemble combining structural formatting features with word- and character-level TF-IDF representations, trained using Random Forest, Gradient Boosting, and Logistic Regression with soft voting. For Subtasks B and C, we fine-tuned CodeBERT to leverage contextual code representations, incorporating class balancing strategies such as downsampling and weighted cross-entropy.Our results demonstrate that handcrafted stylometric features struggle under strong distribution shift, while transformer-based contextual modeling is more effective for fine-grained attribution and hybrid/adversarial detection. The study highlights the importance of robust contextual representations for realistic AI-assisted programming scenarios.</abstract>
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%0 Conference Proceedings
%T MedHastra at SemEval-2026 Task 13: Stylometric Ensembles and Transformer Fine-Tuning for Robust AI Code Detection, Attribution, and Adversarial Analysis
%A Chandrasekar, Shruti
%A S, Vedajanaani R.
%A P, Vijayalakshmi
%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 chandrasekar-etal-2026-medhastra-semeval
%X This paper describes Team MedHastra’s submission to SemEval-2026 Task 13 on detecting machine-generated code across diverse programming languages, generators, and application scenarios. We participated in all three subtasks: (A) binary detection of AI-generated code under out-of-distribution conditions, (B) multi-class attribution across ten large language model families, and (C) classification of human, fully AI-generated, hybrid, and adversarial code.For Subtask A, we implemented a stylometric ensemble combining structural formatting features with word- and character-level TF-IDF representations, trained using Random Forest, Gradient Boosting, and Logistic Regression with soft voting. For Subtasks B and C, we fine-tuned CodeBERT to leverage contextual code representations, incorporating class balancing strategies such as downsampling and weighted cross-entropy.Our results demonstrate that handcrafted stylometric features struggle under strong distribution shift, while transformer-based contextual modeling is more effective for fine-grained attribution and hybrid/adversarial detection. The study highlights the importance of robust contextual representations for realistic AI-assisted programming scenarios.
%U https://aclanthology.org/2026.semeval-1.264/
%P 2098-2103
Markdown (Informal)
[MedHastra at SemEval-2026 Task 13: Stylometric Ensembles and Transformer Fine-Tuning for Robust AI Code Detection, Attribution, and Adversarial Analysis](https://aclanthology.org/2026.semeval-1.264/) (Chandrasekar et al., SemEval 2026)
ACL