AI RESEARCH

S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA

arXiv CS.AI

ArXi:2604.23783v1 Announce Type: cross Retrieval-Augmented Generation (RAG) grounds language models in external evidence, but multi-hop question answering remains difficult because iterative pipelines must control what to retrieve next and when the available evidence is adequate. In practice, systems may answer from incomplete evidence chains, or they may accumulate redundant or distractor-heavy text that interferes with later retrieval and reasoning. We propose S2G-RAG (Structured Sufficiency and Gap-judging RAG), an iterative framework with an explicit controller, S2G-Judge.