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

• URL: http://arxiv.org/abs/2604.23783v1
• Date: Sun, 26 Apr 2026 16:17:54 GMT
• Title: S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA
• Abstract summary: We propose S2G-RAG (Structured Sufficiency and Gap-judging RAG), an iterative framework with an explicit controller, S2G-Judge.<n>At each turn, S2G-Judge predicts whether the current evidence memory supports answering and, if not, outputs structured gap items that describe the missing information.<n>These gap items are then mapped into the next retrieval query, producing stable multi-turn retrieval trajectories.
• Score: 20.283357470094774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. At each turn, S2G-Judge predicts whether the current evidence memory supports answering and, if not, outputs structured gap items that describe the missing information. These gap items are then mapped into the next retrieval query, producing stable multi-turn retrieval trajectories. To reduce noise accumulation, S2G-RAG maintains a sentence-level Evidence Context by extracting a compact set of relevant sentences from retrieved documents. Experiments on TriviaQA, HotpotQA, and 2WikiMultiHopQA show that S2G-RAG improves multi-hop QA performance and robustness under multi-turn retrieval. Furthermore, S2G-RAG can be integrated into existing RAG pipelines as a lightweight component, without modifying the search engine or retraining the generator.

Related papers

• Don't Lose Entities from Retrieval to Generation: Dual Entity Recovery RAG for multi-hop QA [6.545870094085958]
  We show that both forms of decomposition share the same vulnerability, the loss of entity information, and that this loss breaks the pipeline at two separate points.<n>We propose Dual Entity Recovery RAG (DER-RAG), which keeps the grounding entity explicit from retrieval through to generation with two lightweight components.
  arXiv  Detail & Related papers  (2026-09-05T12:52:40Z)
• GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning [27.83191719981676]
  We present textscGTA-RAG, a graph-trajectory-augmented RL framework for retrieval-augmented reasoning.<n>Method consistently outperforms RL-based RAG baselines with both Qwen2.5-3B and Qwen2.5-7B backbones.
  arXiv  Detail & Related papers  (2026-08-23T16:05:20Z)
• Hierarchical Evidence-Driven Reasoning for Long Document Understanding [95.51688464037096]
  We introduce HIEVI-RAG, a hierarchical, evidence-driven multimodal RAG framework for closed-domain document understanding.<n>Our framework significantly outperforms existing open-source baselines and exceeds the strongest reported baseline by an average of 8.05% in accuracy.
  arXiv  Detail & Related papers  (2026-07-06T03:08:28Z)
• Uncertainty-Aware Hybrid Retrieval for Long-Document RAG [18.83395586542378]
  Uncertainty-aware Multi-Granularity RAG (UMG-RAG) is a training-free hybrid retrieval framework that treats chunk granularity as query-specific reliability estimation.<n>UMG-RAG uses existing dense and sparse retrievers as complementary experts across multiple chunk granularities.<n>We introduce UMGP-RAG, a parent promotion variant that uses fine-grained hits to locate relevant evidence while returning broader non-redundant parent chunks for local coherence.
  arXiv  Detail & Related papers  (2026-06-11T16:30:45Z)
• Rethinking RAG in Long Videos: What to Retrieve and How to Use It? [56.38819694781005]
  We introduce V-RAGBench, a benchmark of $langle$query, evidence chunk, answer$rangle$ triplets that enables faithful, decoupled evaluation of retrieval and generation.<n>We also present CARVE, a method that runs parallel retrievers across configurations and employs chunk-adaptive reranking to identify the winning configuration for each chunk.
  arXiv  Detail & Related papers  (2026-06-11T10:05:49Z)
• AdaGATE: Adaptive Gap-Aware Token-Efficient Evidence Assembly for Multi-Hop Retrieval-Augmented Generation [6.314380942821944]
  We propose AdaGATE, a training-free evidence controller for multi-hop RAG.<n>AdaGATE frames evidence selection as a token-constrained repair problem.<n>We evaluate AdaGATE on HotpotQA under clean, redundancy, and noise injected retrieval conditions.
  arXiv  Detail & Related papers  (2026-05-04T14:45:49Z)
• PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering [57.89576196160413]
  Large language models (LLMs) remain brittle on multi-hop question answering (MHQA)<n>We propose textbfPlanned Active Retrieval and Reasoning RAG (PAR$2$-RAG), a two-stage framework that separates emphcoverage from emphcommitment.
  arXiv  Detail & Related papers  (2026-03-30T23:52:54Z)
• Mitigating KG Quality Issues: A Robust Multi-Hop GraphRAG Retrieval Framework [11.158010513386664]
  We propose C2RAG (Constraint-Checked Retrieval-Augmented Generation), a framework aimed at robust multi-hop retrieval over the imperfect KG.<n>C2RAG consistently outperforms the latest baselines by 3.4% EM and 3.9% F1 on average.
  arXiv  Detail & Related papers  (2026-03-16T05:08:41Z)
• MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries [2.756584457554517]
  Map-Disambiguate-Enrich-Reduce (MDER) generates context-derived triple descriptions and integrates them with entity-level summaries.<n>Decompose-Resolve (DR) decomposes user queries into resolvable triples and grounds them in the Knowledge Graph.<n>MDER and DR form an LLM-driven QA pipeline that is robust to sparse, incomplete, and complex relational data.
  arXiv  Detail & Related papers  (2026-03-11T18:38:44Z)
• Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation [64.09110141948693]
  Retrieval-Augmented Generation (RAG) systems remain brittle under realistic retrieval noise.<n>We propose BAR-RAG, which reframes the reranker as a boundary-aware evidence selector that targets the generator's Goldilocks Zone.<n>Bar-RAG consistently improves end-to-end performance under noisy retrieval.
  arXiv  Detail & Related papers  (2026-02-03T16:08:23Z)
• A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces [34.59674580962045]
  We introduce A-RAG, an Agentic RAG framework that exposes hierarchical retrieval interfaces directly to the model.<n>A-RAG provides three retrieval tools: keyword search, semantic search, and chunk read, enabling the agent to adaptively search and retrieve information across multiple granularities.<n> Experiments on multiple open-domain QA benchmarks show that A-RAG consistently outperforms existing approaches with comparable or lower retrieved tokens.
  arXiv  Detail & Related papers  (2026-02-03T12:07:21Z)
• Evaluating Retrieval-Augmented Generation Systems on Unanswerable, Uncheatable, Realistic, Multi-hop Queries [53.99620546358492]
  Real-world use cases often present RAG systems with complex queries for which relevant information is missing from the corpus or is incomplete.<n>Existing RAG benchmarks rarely reflect realistic task complexity for multi-hop or out-of-scope questions.<n>We present the first pipeline for automatic, difficulty-controlled creation of un$underlinec$heatable, $underliner$ealistic, $underlineu$nanswerable, and $underlinem$ulti-hop.
  arXiv  Detail & Related papers  (2025-10-13T21:38:04Z)
• LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding [37.12229829548839]
  We propose LAD-RAG, a novel layout-Aware Dynamic RAG framework.<n>LAD-RAG constructs a symbolic document graph that captures layout structure and cross-page dependencies.<n>Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DocVQA demonstrate that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning.
  arXiv  Detail & Related papers  (2025-10-08T17:02:04Z)
• ImpRAG: Retrieval-Augmented Generation with Implicit Queries [34.72864597562907]
  ImpRAG is a query-free RAG system that integrates retrieval and generation into a unified model.<n>We show that ImpRAG achieves 3.6-11.5 improvements in exact match scores on unseen tasks with diverse formats.
  arXiv  Detail & Related papers  (2025-06-02T21:38:21Z)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.