mlsanityData to 7 Oct 2026

RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation

arXiv 2605.17072 ↗First posted 16 May 2026cs.AI cs.CLcited by 0

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

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Abstract (arXiv)

Existing LLM-driven knowledge graph (KG) construction methods predominantly employ stateless batch processing pipelines, exhibiting structural deficiencies in cross-chunk semantic relation capture, entity disambiguation, and construction process interpretability. These limitations undermine KG quality, retrieval precision, and deployment trust in high-stakes domains. We propose RAGA (Reading And Graph-building Agent), an LLM-based autonomous KG construction and retrieval fusion framework. RAGA provides an atomic toolset supporting full KG lifecycle CRUD operations and embeds a Read-Search-Verify-Construct cognitive constraint into a ReAct tool loop. A KG-vector synchronization mechanism enables hybrid symbolic-vector retrieval, while evidence-anchored verification links every knowledge entry to its source text for auditable provenance. Preliminary experiments on a subset of the QASPER scientific QA dataset indicate that RAGA's fusion retrieval outperforms zero-shot baselines, with KG integration providing measurable gains in both answer and evidence quality. The framework design and experimental baseline serve as a reference for agent-driven autonomous KG construction.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 22 of its 32 references that are in the map. First posted on arXiv 16 May 2026.

Builds on

22 of 32 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning29 Jul
  2. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation8 Oct
  3. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation10 Sep
  4. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering12 Feb
  5. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval31 Jan

Other references · 17

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  4. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  5. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  6. 2023MemoryBank: Enhancing Large Language Models with Long-Term Memorycited by 539
  7. 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 475
  8. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  9. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  10. 2023LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunitiescited by 97
  11. 2024KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graphcited by 90
  12. 2024Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Constructioncited by 70
5 more
  1. 2024StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurizationcited by 70
  2. 2024HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Basescited by 26
  3. 2025PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generationcited by 18
  4. 2024UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Constructioncited by 15
  5. 2024iText2KG: Incremental Knowledge Graphs Construction Using Large Language Modelscited by 11

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  1. 2026Agent-Enhanced Heterogeneous Graph RAG for Academic Question Answering
  2. 2025Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning
  3. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativity
  4. 2026SCOUT-RAG: Scalable and Cost-Efficient Unifying Traversal for Agentic Graph-RAG over Distributed Domains
  5. 2025DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
  6. 2026AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs
  7. 2025Leveraging Spreading Activation for Improved Document Retrieval in Knowledge-Graph-Based RAG Systems
  8. 2025Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning