We describe our system for SemEval-2026 Task 11 Subtask 1 (English syllogistic validity). Our approach fine-tunes Qwen2.5-7B-Instruct with LoRA and a symbolic data augmentation (SDA) scheme that replaces real-world entities with abstract placeholders, explicitly decoupling logical form from content. The resulting model achieves 96.34% accuracy and a total content effect (TCE) of 2.15, yielding a primary score of 44.86. We provide detailed ablations and negative results (prompting, self consistency, contrastive decoding, structured chain-of-thought, andDPO)tocharacterizewhy direct LoRA training with SDA is the most ro bust configuration for this task. Finally, we use a specialist–generalist complementarity setting where a strong API model (ACC 99.48, TCE 1.06, score 57.68) is corrected by the SDA spe cialist on a single disagreement, producing a merged output with ACC 100 and TCE 0.