SYSUpporter at SemEval-2026 Task 13: Leveraging Stylistic Signals and Language-Aware Truncation for Machine-Generated Code Detection

AbstractThis paper describes our system for SemEval-2026 Task 13 Subtask B, which requires attributing source code to either a human author or one of 10 LLM families. Guided by dataset analysis, we identify three practical challenges: formatting fingerprints discarded by tokenizers, heterogeneous code lengths, and extreme class imbalance. We build on unixcoder-base with Explicit Stylistic Prompting, Language-Aware Truncation, and imbalance-aware training (Focal Loss, GeM pooling, multi-sample dropout, and bucket batching). Our system achieves 0.434 Macro F1 on the official hidden test set, ranking 4th out of 34 teams with only 125M parameters. Controlled 5-fold cross-validation confirms that each component contributes to the final system, and a formatting-normalization study quantifies the model’s reliance on formatting cues.