IndexRAG: Bridging Facts for Cross-Document Reasoning at Index Time

• URL: http://arxiv.org/abs/2603.16415v1
• Date: Tue, 17 Mar 2026 11:51:31 GMT
• Title: IndexRAG: Bridging Facts for Cross-Document Reasoning at Index Time
• Abstract summary: We present IndexRAG, a novel approach that shifts cross-document reasoning from online inference to offline indexing.<n>IndexRAG identifies bridge entities shared across documents and generates bridging facts as independently retrievable units.
• Score: 3.674756352891229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-hop question answering (QA) requires reasoning across multiple documents, yet existing retrieval-augmented generation (RAG) approaches address this either through graph-based methods requiring additional online processing or iterative multi-step reasoning. We present IndexRAG, a novel approach that shifts cross-document reasoning from online inference to offline indexing. IndexRAG identifies bridge entities shared across documents and generates bridging facts as independently retrievable units, requiring no additional training or fine-tuning. Experiments on three widely-used multi-hop QA benchmarks (HotpotQA, 2WikiMultiHopQA, MuSiQue) show that IndexRAG improves F1 over Naive RAG by 4.6 points on average, while requiring only single-pass retrieval and a single LLM call at inference time. When combined with IRCoT, IndexRAG outperforms all graph-based baselines on average, including HippoRAG and FastGraphRAG, while relying solely on flat retrieval. Our code will be released upon acceptance.

Related papers

• PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation [13.8041276969708]
  Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be read.<n>We propose PAGE-RAG, a Provenance-Aware Graph Evidence promotion method that scores candidate paths with relevance.<n>As a plug-in, PAGE-RAG further improves all reported RAG backends, including reasoning-oriented, compression-based, graph-based, and document/chunk-level systems.
  arXiv  Detail & Related papers  (2026-08-30T12:33:58Z)
• 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)
• Hierarchical Abstract Tree for Cross-Document Retrieval-Augmented Generation [3.338186852016847]
  $$-RAG is a tree-RAG framework with two key components.<n>It supports diverse tasks from token-level question answering to document-level summarization.<n>It outperforms RAPTOR by 25.9% and HippoRAG 2 by 7.4% in average F1 score.
  arXiv  Detail & Related papers  (2026-05-01T09:06:23Z)
• 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)
• 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)
• $G^2$-Reader: Dual Evolving Graphs for Multimodal Document QA [53.491241153213565]
  $G2$-Reader is a dual-graph system for multimodal question answering.<n>$G2$-Reader with Qwen3-VL-32B-Instruct reaches 66.21% average accuracy, outperforming strong baselines and a standalone GPT-5 (53.08%)<n>On VisDoMBench across five multimodal domains, $G2$-Reader with Qwen3-VL-32B-Instruct reaches 66.21% average accuracy, outperforming strong baselines and a standalone GPT-5 (53.08%)
  arXiv  Detail & Related papers  (2026-01-29T17:52:54Z)
• N2N-GQA: Noise-to-Narrative for Graph-Based Table-Text Question Answering Using LLMs [0.0]
  Multi-hop question answering over hybrid table-text data requires retrieving and reasoning across multiple evidence pieces from large corpora.<n>Standard Retrieval-Augmented Generation (RAG) pipelines process documents as flat ranked lists, causing retrieval noise to obscure reasoning chains.<n>N2N-GQA is the first zeroshot framework for open-domain hybrid table-text QA that constructs dynamic evidence graphs from noisy retrieval outputs.
  arXiv  Detail & Related papers  (2026-01-10T15:55:15Z)
• 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)
• FrugalRAG: Learning to retrieve and reason for multi-hop QA [10.193015391271535]
  Large-scale fine-tuning is not needed to improve RAG metrics.<n>Supervised and RL-based fine-tuning can help RAG from the perspective of frugality.
  arXiv  Detail & Related papers  (2025-07-10T11:02:13Z)
• ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge [50.93758649363798]
  Impliret is a benchmark that shifts the reasoning challenge to document-side processing.<n>We evaluate a range of sparse and dense retrievers, all of which struggle in this setting.
  arXiv  Detail & Related papers  (2025-06-17T11:08:29Z)
• Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval [22.33550491040999]
  RAG grounds large language models in external evidence, yet it still falters when answers must be pieced together across semantically distant documents.<n>We build two plug-and-play retrievers: StatementGraphRAG and TopicGraphRAG.<n>Our methods outperform naive chunk-based RAG achieving an average relative improvement of 23.1% in retrieval recall and correctness.
  arXiv  Detail & Related papers  (2025-06-09T17:58:35Z)
• 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)
• Answering Any-hop Open-domain Questions with Iterative Document Reranking [62.76025579681472]
  We propose a unified QA framework to answer any-hop open-domain questions. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets.
  arXiv  Detail & Related papers  (2020-09-16T04:31:38Z)

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.