This paper presents the system for SemEval-2026 Task 13, addressing both binary detection (Subtask A) and multi-class attribution (Subtask B). For Subtask A, we propose a robust multi-view training framework using UniXcoder-base, incorporating domain-specific structural prefixes, delexicalization with symmetric KL consistency loss, and token dropout. Our system achieves a high macro F1 of 0.845 on the out-of-distribution test set, demonstrating strong generalization across five unseen languages and two unseen domains. For Subtask B, we provide a rigorous diagnostic analysis of majority-class bias in transformer-based detectors. We reveal a significant performance gap where an 88.4% accuracy masks a near-complete failure in minority-class attribution (0.086 Macro F1), highlighting that standard fine-tuning is insufficient for fine-grained generator identification. Our results expose distinct regimes in code detection and motivate the need for imbalance-aware, structure-focused modeling in future work.