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However, existing systems rely on generating natural language queries at each hop and maintaining a strict architectural separation between retriever and generator, preventing them from leveraging the full representational capacity of the LLM. We propose \\textbf{LAnR} (Latent Abstraction for RAG), a unified framework in which a single LLM jointly performs encoding, retrieval, and generation entirely within its own latent space. Rather than generating textual queries, LAnR produces dense retrieval vectors from the hidden states of a designated \\texttt{[PRED]} token and uses them to match against encoded document representations from the same model. 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Rather than generating textual queries, LAnR produces dense retrieval vectors from the hidden states of a designated \\texttt{[PRED]} token and uses them to match against encoded document representations from the same model. 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