We describe MLA-CI (Multi-Layer Adversarial for Content Invariance), a DeBERTa-v3-base system for SemEval-2026 Task 11 Subtask 1 on content-invariant syllogistic reasoning. MLA-CI combines multi-layer feature extraction, gradient-reversal adversarial training, structure-preserving template augmentation, implausible-class oversampling, and focal loss. Our principal contribution is a systematic ablation study, confirmed across three random seeds, showing that adversarial training at standard strength is counterproductive: removing gradient reversal improves the mean validation score from 26.41 ± 0.99 to 38.15 ± 5.32. Per-condition analysis reveals that gradient reversal over-suppresses plausibility-correlated features, creating an inverted content effect that disproportionately harms plausible-valid accuracy. A sweep over seven adversarial pressure values reveal that only very light adversarial pressure value (≤ 0.1) preserves accuracy, while the submitted adversarial pressure value (1.0 or above) cause severe degradation. We conclude that data-level debiasing through structure-preserving augmentation is more effective and robust than model-level adversarial debiasing for this task.