This paper addresses SemEval-2026 Task 11, which focused on mitigating content effects in syllogistic reasoning. Logical validity is often conflated with semantic plausibility in large language models.Prior methods rely on standard fine-tuning or prompting, without explicit bias control.A rule- and template-based symbolic data augmentation framework is proposed for fine-tuning the \texttt{Qwen2-1.5B-Instruct} model and instruction-tuning the \texttt{XLM-RoBERTa-large} model. Logic-preserving synthetic data are generated through lexical rules. The system is ranked 1st in Task 1 with a perfect overall score of 100, and 6th in Task 3 with a score of 56.97. Code is publicly available at: \url{https://github.com/YNU-HPCC/semeval-2026-task11}.