mlsanityData to 14 Oct 2026

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries

arXiv 2607.10151 ↗First posted 11 Jul 2026cs.IRcited by 2

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems are widely adopted in question answering, yet they often fail to satisfy complex multi-constraint queries, leading to constraint violations, factual inconsistencies, or hallucinations. We present Structure-Driven RAG System for Multi-Constraint Queries(MC-RAG), a structure-driven RAG system that reformulates retrieval as a subgraph matching problem over a knowledge graph. By integrating semantic and structural embeddings with path-level indexing, MC-RAG performs interpretable, structure-aware, and constraint-consistent retrieval and generation. During the demonstration, participants can input medical or encyclopedic multi-constraint queries, visualize how the system parses constraints, performs structural matching, and generates answers, thereby experiencing an end-to-end, interactive, and explainable RAG pipeline. A demo video is available at https://youtu.be/J8kahzmAnu0.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 13 of its 20 references that are in the map. First posted on arXiv 11 Jul 2026.

Builds on

13 of 20 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Structure Guided Retrieval-Augmented Generation for Factual Queries21 Apr
  2. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation8 Oct
  3. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation10 Sep
  4. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  5. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering12 Feb

Other references · 8

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  3. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  4. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  5. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  6. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  7. 2024Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generationcited by 19
  8. 2023Efficient Exact Subgraph Matching via GNN-based Path Dominance Embedding (Technical Report)cited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026No=esis: Deterministic-First Retrieval with Two-Tier Context Hydration for Factuality-Critical Queries on Small Local Modelscited by 0
  2. 2026ShanLiangRen: A Nutrition Agent for Personalized Daily Meal Planningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval
  2. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  3. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
  4. 2026HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs
  5. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
  6. 2026SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering
  7. 2025RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking
  8. 2025Query-Centric Graph Retrieval Augmented Generation