Large language models exhibit content effects, where surface plausibility interferes with formal logical reasoning. In SemEval-2026 Task 11, this appears as a performance gap between plausibility-aligned and plausibility-conflicting syllogisms, reflecting directional content bias. We address this issue using inference-time activation steering, modeling bias as a geometric deviation between plausibility-driven and validity-driven representations. We introduce a layered steering framework that combines Activation Transport (ACT) with input-adaptive contrastive steering (K-CAST), applied to layers identified through sensitivity analysis. This architecture-aware strategy enables targeted interventions without retraining.On BERT, sequential multi-layer steering improves validity accuracy from 77.1% to 82.3% while reducing bias by 75%. In contrast, for the decoder-only Qwen2.5-1.5B-Instruct, a single mid-to-late layer intervention reduces bias from 0.26 to 0.04 with modest accuracy gains, whereas multi-layer steering offers no additional benefit. These results reveal a fundamental architectural distinction: encoder-based models benefit from distributed low-intensity corrections, while decoder-only instruction-tuned models concentrate reasoning signals within a narrow late-layer band. Our findings demonstrate that effective bias mitigation requires architecture-aware activation steering.