Multi-hop Retrieval-Augmented Generation can fail even when relevant evidence has been retrieved. The remaining diffculty lies in how evidence is connected across reasoning steps:an intermediate answer may be unsupported, at-tached to the wrong anchor, or propagated into a later step without valid evidence. We study this failure as Reasoning Chain Hallucination(RCH), where a fuent multi-hop reasoning pro-cess contains one or more ungrounded inter-mediate steps. To address RCH, we formulate Retrieve–Verify–Generate (R-V-G) for multi-hop RAG: retrieved evidence should support an explicitly verifed reasoning structure before it is allowed to guide generation. GROUNDRAG is an evidence-verifed framework that imple-ments this requirement. GroundRAG frst ac-quires the evidence needed to instantiate inter-mediate reasoning variables, then represents the reasoning process as a typed dependency chain.Each step is checked against retrieved passages using explicit grounding criteria; unresolved steps are repaired with queries formed from ver-ifed intermediate answers, while insuffciently grounded chains are discarded before genera-tion. This design separates evidence acquisi-tion from the decision of whether a reasoning chain is safe to use. On HotpotQA, 2WikiMul-tiHopQA, and MuSiQue, GroundRAG reaches 73.8%, 69.2%, and 44.3% LLM-judged accu-racy, respectively, outperforming the strongest baseline in our evaluation.