We describe our system for SemEval-2026 Task 13 Subtask A, which focuses on detecting whether source code is written by a human or generated by an AI system. We propose a hybrid approach that combines semantic embeddings from CodeBERT with lightweight, language-agnostic structural features extracted using Tree-sitter. We compute normalized structural ratios such as nesting depth, logic density, complexity per line, average line length, and punctuation frequency. These structural signals are concatenated with CodeBERT embeddings and passed to a linear classifier for binary prediction. Experimental results on the official validation split show that combining semantic and normalized structural representations substantially improves the model’s detection performance on seen-language distributions. However, results on unseen test data reveal significant performance degradation under cross-language distribution shifts. On the official leaderboard, our system ranked 47th out of 81 participating teams.