The growing adoption of large language models for code generation poses challenges for code quality, security, and authorship verification—particularly when test conditions involve unseen programming languages, generators, or application domains. We present our system, which combines three code-pretrained transformer encoders (CodeT5p-220M, CodeBERT, UniXcoder) with a structure-first Flow-Augmented AST (FA-AST) encoder implemented as a Gated Graph Neural Network. On Subtask A our best single model achieves macro F1 of 0.559; a post-competition layered rank-fusion ensemble across all three encoders raises this to 0.643. On Subtask C we obtain 0.585 officially; a three-stage ensemble combining neural probabilities with LightGBM-based features and class-priority routing raises this to 0.652. Our contributions include a language-agnostic structural detector, a diversity-driven rank-fusion strategy exploiting low inter-model correlation for binary classification, and a meta-learner stacking pipeline for multi-class detection under distribution shift.