mlsanityData to 7 Oct 2026

STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation

arXiv 2604.22282 ↗First posted 24 Apr 2026cs.CLcited by 0

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

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Abstract (arXiv)

Knowledge Graph-based Question Answering (KGQA) plays a pivotal role in complex reasoning tasks but remains constrained by two persistent challenges: the structural heterogeneity of Knowledge Graphs(KGs) often leads to semantic mismatch during retrieval, while existing reasoning path retrieval methods lack a global structural perspective. To address these issues, we propose Structure-Tracing Evidence Mining (STEM), a novel framework that reframes multi-hop reasoning as a schema-guided graph search task. First, we design a Semantic-to-Structural Projection pipeline that leverages KG structural priors to decompose queries into atomic relational assertions and construct an adaptive query schema graph. Subsequently, we execute globally-aware node anchoring and subgraph retrieval to obtain the final evidence reasoning graph from KG. To more effectively integrate global structural information during the graph construction process, we design a Triple-Dependent GNN (Triple-GNN) to generate a Global Guidance Subgraph (Guidance Graph) that guides the construction. STEM significantly improves both the accuracy and evidence completeness of multi-hop reasoning graph retrieval, and achieves State-of-the-Art performance on multiple multi-hop benchmarks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 33 of its 48 references that are in the map. First posted on arXiv 24 Apr 2026.

Builds on

33 of 48 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025PathMind: A Retrieve-Prioritize-Reason Framework for Knowledge Graph Reasoning with Large Language Models18 Nov
  2. 2025LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph4 Apr
  3. 2025Reasoning of Large Language Models over Knowledge Graphs with Super-Relations28 Mar
  4. 2024Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs31 Oct
  5. 2024FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering22 May
  6. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning2 Oct
  7. 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph15 Jul
  8. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions20 Dec

Other references · 25

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  6. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  7. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  8. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  9. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  10. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  11. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  12. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
13 more
  1. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  2. 2018The Web as a Knowledge-base for Answering Complex Questionscited by 409
  3. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
  4. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  5. 2019A Study of BFLOAT16 for Deep Learning Trainingcited by 227
  6. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
  7. 2023Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answeringcited by 119
  8. 2021Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signalscited by 100
  9. 2023Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answeringcited by 97
  10. 2022DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Basescited by 83
  11. 2024GRAG: Graph Retrieval-Augmented Generationcited by 77
  12. 2020SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Basescited by 47
  13. 2024SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generationcited by 6

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  4. 2023Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database
  5. 2026HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering
  6. 2026Theseus in the Graph: Towards Traceable Multi-Hop Graph Navigation
  7. 2026EvLink: Source-Grounded Evidence Linking for Graph RAG
  8. 2026BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs