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    <abstract>Machine-generated code detection is examined under out-of-distribution conditions where robust generalization is required. A hybrid feature representation is used in which code snippets are encoded through character-level TF–IDF patterns together with explicit structural indicators capturing properties such as verbosity and formatting behavior. Variability across generators is handled through clustering-based expert specialization, and predictions are produced using an ensemble of logistic regression and Naïve Bayes models with calibrated thresholds. Experimental results show that the proposed approach performs competitively despite relying on simple linear classifiers. The findings suggest that persistent structural patterns in code provide reliable cross-domain signals for identifying machine-generated programs.</abstract>
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%0 Conference Proceedings
%T Stylometry at SemEval-2026 Task 13: Clustered Stylometric Modeling for Machine-Generated Code Detection
%A Santhanam, Sruthi
%A Sarkar, Parthib
%A Sharma, Yashvardhan
%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 santhanam-etal-2026-stylometry
%X Machine-generated code detection is examined under out-of-distribution conditions where robust generalization is required. A hybrid feature representation is used in which code snippets are encoded through character-level TF–IDF patterns together with explicit structural indicators capturing properties such as verbosity and formatting behavior. Variability across generators is handled through clustering-based expert specialization, and predictions are produced using an ensemble of logistic regression and Naïve Bayes models with calibrated thresholds. Experimental results show that the proposed approach performs competitively despite relying on simple linear classifiers. The findings suggest that persistent structural patterns in code provide reliable cross-domain signals for identifying machine-generated programs.
%U https://aclanthology.org/2026.semeval-1.172/
%P 1319-1325

Markdown (Informal)

[Stylometry at SemEval-2026 Task 13: Clustered Stylometric Modeling for Machine-Generated Code Detection](https://aclanthology.org/2026.semeval-1.172/) (Santhanam et al., SemEval 2026)

• Stylometry at SemEval-2026 Task 13: Clustered Stylometric Modeling for Machine-Generated Code Detection (Santhanam et al., SemEval 2026)

ACL

• Sruthi Santhanam, Parthib Sarkar, and Yashvardhan Sharma. 2026. Stylometry at SemEval-2026 Task 13: Clustered Stylometric Modeling for Machine-Generated Code Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 1319–1325, San Diego, California, USA. Association for Computational Linguistics.