This paper describes the system submitted by team Archaeology to SemEval-2026 Task~13 on AI-generated code detection. The shared task consists of three subtasks; we participate in Subtask-A (binary classification: human-written vs.\ AI-generated code) and Subtask-B (11-class attribution of the generating model).Starting from a TF-IDF and Logistic Regression baseline, we fine-tune four pre-trained code models (CodeBERT, GraphCodeBERT, UniXcoder, and CodeT5+) with separate strategies for each subtask.For Subtask-A, we use leave-one-language-out cross-validation, code augmentation, chunked inference with trimmed-mean aggregation, and threshold calibration on a difficult dataset.For Subtask-B, we use sandwich token packing, class-balanced loss, and multi-seed ensembling with test-time augmentation. Our best submissions obtain macro-F1 scores of 0.737 on Subtask-A and 0.422 on Subtask-B.

This paper describes our approach for 5 classification tasks from Building Educational Applications (BEA) 2025 Shared Task.Our methods range from classical machine learning models to large-scale transformers with fine-tuning and prompting strategies. Despite the diversity of approaches, performance differences were often minor, suggesting a strong surface-level signal and the limiting effect of annotation noise—particularly around the “To some extent” label. Under lenient evaluation, simple models perform competitively, showing their effectiveness in low-resource settings. Our submissions ranked in the top 10 in four of five tracks.