mlsanityData to 14 Oct 2026

Agentic RAG with Knowledge Graphs for Complex Multi-Hop Reasoning in Real-World Applications

arXiv 2507.16507 ↗First posted 22 Jul 2025cs.AI cs.IRcited by 3

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

also in 2 more
Abstract (arXiv)

Conventional Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) but often fall short on complex queries, delivering limited, extractive answers and struggling with multiple targeted retrievals or navigating intricate entity relationships. This is a critical gap in knowledge-intensive domains. We introduce INRAExplorer, an agentic RAG system for exploring the scientific data of INRAE (France's National Research Institute for Agriculture, Food and Environment). INRAExplorer employs an LLM-based agent with a multi-tool architecture to dynamically engage a rich knowledge base, through a comprehensive knowledge graph derived from open access INRAE publications. This design empowers INRAExplorer to conduct iterative, targeted queries, retrieve exhaustive datasets (e.g., all publications by an author), perform multi-hop reasoning, and deliver structured, comprehensive answers. INRAExplorer serves as a concrete illustration of enhancing knowledge interaction in specialized fields.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 10 of its 13 references that are in the map. First posted on arXiv 22 Jul 2025.

Builds on

10 of 13 references.

References · 10

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  6. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 249
  7. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 223
  8. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 88
  9. 2025Knowledge Graph-Guided Retrieval Augmented Generationcited by 58
  10. 2023Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Modelscited by 35

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learningcited by 1
  2. 2026Knowledge Capsules: Structured Nonparametric Memory Units for LLMscited by 1
  3. 2026Towards Trustworthy and Cost-Efficient Data Integration: From Na"ive RAG to Agentic RAGcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
  2. 2026Agent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval
  3. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
  4. 2025Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges
  5. 2025SIRAG: Towards Stable and Interpretable RAG with A Process-Supervised Multi-Agent Framework
  6. 2025Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Framework
  7. 2025Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning
  8. 2025DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router