{"ID":6536353,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-14T06:08:37.952498173Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10151","arxiv_id":"2607.10151","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","abstract":"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.","short_abstract":"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-d...","url_abs":"https://arxiv.org/abs/2607.10151","url_pdf":"https://arxiv.org/pdf/2607.10151v1","authors":"[\"Xiao Zhang\",\"Yang Wan\",\"Yi Li\",\"Miao Xie\",\"Chunli Lv\"]","published":"2026-07-11T06:20:38Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"RAG\"]","has_code":false}