Retrieval-Augmented Generation (RAG) can fail under knowledge conflicts, where a model’s parametric prior competes with retrieved evidence and leads to unfaithful outputs. Existing mitigation methods typically operate on the final decoding distribution or rely on weight editing with access to ground-truth targets. Through layer-wise analysis, we identify a **parametric suppression** pattern: in many conflict cases, evidence-supported answers become salient in intermediate representations but are weakened by later layer outputs. To address this, we propose CoRect (**Co**ntext-Aware Logit Contrast for Hidden State **Rect**ification), a training-free inference-time method. By contrasting contextualized and context-free forward passes, CoRect first selects an evidence-supported token direction without using gold labels. It then rectifies hidden states in layers whose outputs suppress this direction. Across Question Answering (QA) and summarization benchmarks, CoRect improves average faithfulness and reduces hallucinations compared with strong decoding baselines.