mlsanityData to 7 Oct 2026

EviReform: Evidence-Guided Query Reformulation for Multi-Hop Graph Retrieval

arXiv 2608.13006 ↗First posted 13 Aug 2026cs.CL cs.IRcited by 0

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Abstract (arXiv)

Multi-hop retrieval must recover passages that provide sufficient evidence together. An initial passage often resolves an entity or relation implicit in the question, making the missing evidence easier to describe only after retrieval begins. Graph retrieval improves access to related evidence through stored corpus structure, but its retrieval signal is commonly derived from the original question. Complementary evidence must then be reached through stored relations even when an observed passage provides a more direct semantic cue. We introduce EviReform, which separates revising the retrieval request from aggregating evidence in the graph. Retrieved source passages formulate residual queries for the unresolved information need. The original and residual retrieval signals are normalized separately, combined, and propagated between propositions that share entities. On 2WikiMultiHopQA, HotpotQA, and MuSiQue, EviReform exceeds the strongest baseline by up to 5.59 Recall@5 points and 4.50 F1 points. These results show that observed evidence can guide graph retrieval toward the part of a supporting chain left underspecified by the original question. Code is available at https://github.com/XrazyMee/EviReform.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 69 references that are in the map. First posted on arXiv 13 Aug 2026.

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35 of 69 references.

References · 35

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  4. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  5. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  6. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  7. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
  8. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  9. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  10. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
  11. 2023Active Retrieval Augmented Generationcited by 506
  12. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 466
23 more
  1. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 356
  2. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  3. 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 310
  4. 2024Generative Representational Instruction Tuningcited by 227
  5. 2025From RAG to Memory: Non-Parametric Continual Learning for Large Language Modelscited by 223
  6. 2020Answering Complex Open-Domain Questions with Multi-Hop Dense Retrievalcited by 160
  7. 2023Dense X Retrieval: What Retrieval Granularity Should We Use?cited by 88
  8. 2021Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrievalcited by 63
  9. 2019Answering Complex Open-domain Questions Through Iterative Query Generationcited by 60
  10. 2025Knowledge Graph-Guided Retrieval Augmented Generationcited by 57
  11. 2025Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learningcited by 50
  12. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generationcited by 46
  13. 2021Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedbackcited by 45
  14. 2025PropRAG: Guiding Retrieval with Beam Search over Proposition Pathscited by 22
  15. 2024LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generationcited by 17
  16. 2024GeAR: Graph-enhanced Agent for Retrieval-augmented Generationcited by 17
  17. 2025Shifting from Ranking to Set Selection for Retrieval Augmented Generationcited by 13
  18. 2025GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learningcited by 12
  19. 2025Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrievalcited by 10
  20. 2025G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledgecited by 9
  21. 2026S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QAcited by 9
  22. 2026Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guaranteescited by 7
  23. 2026Breaking the Static Graph: Context-Aware Traversal for Robust Retrieval-Augmented Generationcited by 4

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