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Résumé : Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation, yet their propensity to produce factually incorrect or fabricated content—commonly termed 'hallucinations'—poses significant challenges in high-stakes research assistance applications. This paper introduces RAMAF (Retrieval-Augmented Multi-Agent Framework), a novel architecture that combines dense passage retrieval, cross-encoder re-ranking, and a coordinated ensemble of specialized verification agents to substantially reduce hallucination rates in LLM-driven research assistance systems. Our framework employs three cooperative agents: a Retrieval Agent responsible for sourcing evidence from curated knowledge bases, a Verification Agent that cross-checks candidate responses against retrieved documents, and a Synthesis Agent that merges evidence-grounded statements into coherent, citation-backed outputs. Experimental evaluation on the HaluEval, TruthfulQA, and PubMedQA benchmarks demonstrates that RAMAF achieves a hallucination rate of 4.1%—a 80.8% relative reduction compared to a GPT-4 baseline (21.4%) and a 72.3% reduction over single-agent RAG systems (14.8%). Factuality scores (FactScore) improve from 0.71 to 0.95, while ROUGE-L gains of 68.4% indicate significantly better alignment with reference answers. Keywords: Retrieval-Augmented Generation, Multi-Agent Systems, Hallucination Mitigation, Large Language Models, Research Assistance, Fact Verification, Dense Passage Retrieval