AI RESEARCH
CUE-R: Beyond the Final Answer in Retrieval-Augmented Generation
arXiv CS.LG
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ArXi:2604.05467v1 Announce Type: cross As language models shift from single-shot answer generation toward multi-step reasoning that retrieves and consumes evidence mid-inference, evaluating the role of individual retrieved items becomes important. Existing RAG evaluation typically targets final-answer quality, citation faithfulness, or answer-level attribution, but none of these directly targets the intervention-based, per-evidence-item utility view we study here. We