TeleAI at SemEval-2026 Task 13: Data-Centric Full-Parameter Fine-Tuning with Multi-Level Ensembling for Generated Code Detection

AbstractThis paper presents our top-ranking system for SemEval-2026 Task 13 on code generation detection under multi-lingual and distribution-shift settings. Our approach achieved 1st place in Subtasks A and B, and 2nd place in Subtask C in the official evaluation.Our framework integrates data-centric analysis, full-parameter model adaptation, and multi-level ensemble learning. We first analyze label and length distributions and apply repeated oversampling to address class imbalance. We then optimize prompts in a data-driven manner to improve inference stability. Based on Qwen3-30B-A3B-Instruct, we conduct full-parameter fine-tuning with diverse training configurations and integrate multiple checkpoints using soft voting, hard voting, logits-based voting, and LightGBM stacking.Experimental results demonstrate substantial improvements over zero-shot baselines and consistent gains from ensemble strategies, validating the effectiveness of systematic adaptation and ensembling for robust code generation detection.