This paper presents our approach for SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We propose a neuro-symbolic teacher-student framework that utilizes DeepSeek-R1 as a Logical Auditor to generate a high-fidelity training corpus. We distill these analytical behaviors into Qwen-3 models using Low Rank Adaptation (LoRA), focusing on teaching the mechanics of logic rather than simple label matching. Our system yields robust results across both subtasks, with a ranking score of 39.81 (96.86% accuracy) on Subtask 1 and 26.02 on Subtask 3. However, validity bias partially persists, so we conclude that while structured distillation substantially mitigates belief bias, fully disentangling logical validity from plausibility remains a central challenge for future development.