mlsanityData to 14 Oct 2026

Beyond Vector Search: Comparing Classical RAG with Hybrid GraphRAG for Climate Science Q&A

arXiv 2608.28766 ↗First posted 28 Aug 2026cs.SEcited by 0

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

Abstract (arXiv)

Traditional Retrieval-Augmented Generation (RAG) systems treat documents in isolation, failing to capture hierarchical relationships between concepts in complex scientific corpora. This limitation compromises answer quality in specialized domains such as climatology, where conceptual dependencies frequently traverse multiple articles. We propose a hybrid architecture that integrates vector search with GraphRAG, Leiden community detection, and cross-encoder re-ranking, achieving gains of 160% in contextual relevance and 177% in contextual recall compared to classical RAG. These results demonstrate that unifying local and global retrieval significantly outperforms text-span isolation, paving the way for more effective question-answering systems over dispersed scientific literature.

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 7 of its 12 references that are in the map. First posted on arXiv 28 Aug 2026.

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7 of 12 references.

References · 7

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  3. 2024Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systemscited by 87
  4. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insightscited by 53
  5. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documentscited by 49
  6. 2023A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databasescited by 25
  7. 2025The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Modelscited by 24

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  1. 2025HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights
  2. 2026SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG
  3. 2025CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs
  4. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation
  5. 2025SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generated LLMs
  6. 2026Re-ranking and Late Interaction Drive Retrieval Quality: A Controlled Comparison of RAG Strategies for Scientific Question Answering
  7. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  8. 2026DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generation