mlsanityData to 14 Oct 2026

A Graph-Retrieval-Augmented Generation Framework Enhances Decision-Making in the Circular Economy

arXiv 2506.04252 ↗First posted 1 Jun 2025cs.AI cs.CL cs.LGcited by 0

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

Abstract (arXiv)

Large language models (LLMs) hold promise for sustainable manufacturing, but often hallucinate industrial codes and emission factors, undermining regulatory and investment decisions. We introduce CircuGraphRAG, a retrieval-augmented generation (RAG) framework that grounds LLMs outputs in a domain-specific knowledge graph for the circular economy. This graph connects 117,380 industrial and waste entities with classification codes and GWP100 emission data, enabling structured multi-hop reasoning. Natural language queries are translated into SPARQL and verified subgraphs are retrieved to ensure accuracy and traceability. Compared with Standalone LLMs and Naive RAG, CircuGraphRAG achieves superior performance in single-hop and multi-hop question answering, with ROUGE-L F1 scores up to 1.0, while baseline scores below 0.08. It also improves efficiency, halving the response time and reducing token usage by 16% in representative tasks. CircuGraphRAG provides fact-checked, regulatory-ready support for circular economy planning, advancing reliable, low-carbon resource decision making.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 8 of its 34 references that are in the map. First posted on arXiv 1 Jun 2025.

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8 of 34 references.

References · 8

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  3. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  4. 2020Knowledge Graphscited by 621
  5. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
  6. 2024GRAG: Graph Retrieval-Augmented Generationcited by 78
  7. 2023Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generationcited by 54
  8. 2024Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processingcited by 1

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  1. 2026LCAi: Life Cycle Assessment with big data fusion and retrieval-augmented generation-assisted interpretation
  2. 2024Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation
  3. 2026Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industry
  4. 2026Evidence-Grounded Multi-Agent Planning Support for Urban Carbon Governance via RAG
  5. 2025Emission-GPT: A domain-specific language model agent for knowledge retrieval, emission inventory and data analysis
  6. 2025Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring
  7. 2025EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detection
  8. 2026WuYuEval: A Multi-Level Benchmark for Large Language Models in Solid Waste Management