This paper describes the system submittedby DUTIRtaskC for SemEval-2026 Task 8:MTRAGEval (Task C). Multi-turn RetrievalAugmented Generation (RAG) poses significant challenges in context tracking, retrievalprecision, and hallucination mitigation. Ourproposed system addresses these by employinga multi-stage pipeline consisting of: (1) LLMbased query rewriting (powered by GPT-5.2) toresolve conversational dependencies; (2) a hybrid retrieval module combining dense embeddings (BGE-M3) and sparse retrieval (BM25)with Reciprocal Rank Fusion (RRF); (3) aconfidence-based answerability gating mechanism; and (4) a post-generation faithfulnessguard. Experimental results on the blind test setshow that our approach achieves a CompositeScore of 0.5576, ranking 4th out of 29 participating teams. Detailed analysis reveals that oursystem significantly outperforms strong baselines in faithfulness and successfully handlesunderspecified queries.