mlsanityData to 7 Oct 2026

Overcoming the "Impracticality" of RAG: Proposing a Real-World Benchmark and Multi-Dimensional Diagnostic Framework

arXiv 2604.02640 ↗First posted 3 Apr 2026cs.CLcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Performance evaluation of Retrieval-Augmented Generation (RAG) systems within enterprise environments is governed by multi-dimensional and composite factors extending far beyond simple final accuracy checks. These factors include reasoning complexity, retrieval difficulty, the diverse structure of documents, and stringent requirements for operational explainability. Existing academic benchmarks fail to systematically diagnose these interlocking challenges, resulting in a critical gap where models achieving high performance scores fail to meet the expected reliability in practical deployment. To bridge this discrepancy, this research proposes a multi-dimensional diagnostic framework by defining a four-axis difficulty taxonomy and integrating it into an enterprise RAG benchmark to diagnose potential system weaknesses.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 12 references that are in the map. First posted on arXiv 3 Apr 2026.

Builds on

6 of 12 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries27 Jan

Other references · 5

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2024A Survey on LLM-as-a-Judgecited by 1,384
  3. 2024M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understandingcited by 110
  4. 2023RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledgecited by 51
  5. 2025TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoningcited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026LayerRAG-Bench: A Cross-Layer Reliability Benchmark for Agentic Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
  2. 2025Engineering the RAG Stack: A Comprehensive Review of the Architecture and Trust Frameworks for Retrieval-Augmented Generation Systems
  3. 2024RAG Does Not Work for Enterprises
  4. 2024A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation
  5. 2024Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective
  6. 2026RAGe: A Retrieval-Augmented Generation Evaluation Framework
  7. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation System
  8. 2025RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation