We present a training-free hybrid retrieve-then-rerank system for multi-turn retrieval-augmented generation, submitted to allthree subtasks of SemEval-2026 Task 8(MTRAGEval): passage retrieval (Task A),generation with reference passages (Task B),and end-to-end RAG (Task C). Our system ad-dresses the core multi-turn challenges—non-standalone questions, unanswerable queries,and shifting passage relevance—across fourdomain-specific corpora: ClapNQ, Cloud,FiQA, and Govt. Queries are reformulatedthrough LLM-driven rewriting, decompositioninto sub-queries, and Hypothetical DocumentEmbeddings (HyDE). Retrieved candidatesfrom dense vector search (BGE-base-en-v1.5)and BM25 lexical matching are fused via Re-ciprocal Rank Fusion and reranked by a cross-encoder (BGE-reranker-large). Llama-3.3-70B-Instruct generates extractive, context-groundedresponses with built-in abstention for unanswer-able queries. Using only open-source mod-els without fine-tuning, the system achievesnDCG@5 of 0.4098 on Task A (22nd/38), aharmonic mean of 0.7462 on Task B (9th/26),and 0.5796 on Task C (2nd/29), coming within1.1% of the top submission. We attribute thestrong Task C result to the synergy betweenmulti-signal query reformulation and faithfulextractive generation.