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This paper introduces GRACE-RAG, a retrieval-governed, graph-augmented RAG architecture that externalizes structural reasoning from generation to a structured retrieval layer, enabling lightweight deployment in closed-domain institutional settings. Experiments show up to 20% quality gains with mid-scale models, reducing computational and latency footprint.
RAGless is a semantic retrieval system that matches user questions to pre-generated question variants for closed-domain FAQ, eliminating the LLM generation step in standard RAG for improved precision.
RAGognizer introduces a hallucination-aware fine-tuning approach that integrates a lightweight detection head into LLMs for joint optimization of language modeling and hallucination detection in RAG systems. The paper presents RAGognize, a dataset of naturally occurring closed-domain hallucinations with token-level annotations, and demonstrates state-of-the-art hallucination detection while reducing hallucination rates without degrading language quality.