MedHastra at SemEval-2026 Task 13: Stylometric Ensembles and Transformer Fine-Tuning for Robust AI Code Detection, Attribution, and Adversarial Analysis

AbstractThis 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.