AI RESEARCH
Why Retrieval-Augmented Generation Fails: A Graph Perspective
arXiv CS.CL
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ArXi:2605.14192v1 Announce Type: new Retrieval-Augmented Generation (RAG) has become a powerful and widely used approach for improving large language models by grounding generation in retrieved evidence. However, RAG systems still produce incorrect answers in many cases. Why RAG fails despite having access to external information remains poorly understood. We present a model-internal study of retrieval-augmented generation that examines how retrieved evidence influences answer generation.