The rapid advancement of Large Language Models (LLMs) has significantly impacted software engineering, posing challenges for determining the origin and authenticity of source code. This paper presents the MALTO team’s submission for SemEval-2026 Task 13, explicitly focusing on Subtask B (Authorship Attribution among 11 classes) and Subtask C (Hybrid Code Detection). To address severe class imbalance and the complex boundaries of mixed human-machine code, we propose a unified framework that leverages an ensemble of UniXcoder and CodeT5. Our approach integrates a robust Tree-sitter-based Universal Canonicalization strategy, Data Augmentation, and a novel 3-Phase Curriculum Training schedule enhanced by Hard Negative Mining. Specifically, UniXcoder’s cross-modal representations excel at distinguishing among semantically overlapping LLM families (Subtask B), whereas CodeT5’s identifier-aware architecture is superior at detecting subtle structural anomalies in hybrid and adversarial snippets (Subtask C). By aggregating these complementary strengths, our soft-voting ensemble overcomes the limitations of individual models, demonstrating strong robustness against imbalanced distributions and effectively discriminating between purely human, purely machine, hybrid, and adversarial code snippets.