M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2512.20136v2
• Date: Wed, 24 Dec 2025 01:35:59 GMT
• Title: M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation
• Authors: Hyeongcheol Park, Jiyoung Seo, Jaewon Mun, Hogun Park, Wonmin Byeon, Sung June Kim, Hyeonsoo Im, JeungSub Lee, Sangpil Kim,
• Abstract summary: M$3$KG-RAG is a Multi-hop Multimodal Knowledge Graph-enhanced RAG.<n>It retrieves query-aligned audio-visual knowledge from MMKGs.<n>It improves reasoning depth and answer faithfulness in MLLMs.
• Score: 20.170643730917963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) has recently been extended to multimodal settings, connecting multimodal large language models (MLLMs) with vast corpora of external knowledge such as multimodal knowledge graphs (MMKGs). Despite their recent success, multimodal RAG in the audio-visual domain remains challenging due to 1) limited modality coverage and multi-hop connectivity of existing MMKGs, and 2) retrieval based solely on similarity in a shared multimodal embedding space, which fails to filter out off-topic or redundant knowledge. To address these limitations, we propose M$^3$KG-RAG, a Multi-hop Multimodal Knowledge Graph-enhanced RAG that retrieves query-aligned audio-visual knowledge from MMKGs, improving reasoning depth and answer faithfulness in MLLMs. Specifically, we devise a lightweight multi-agent pipeline to construct multi-hop MMKG (M$^3$KG), which contains context-enriched triplets of multimodal entities, enabling modality-wise retrieval based on input queries. Furthermore, we introduce GRASP (Grounded Retrieval And Selective Pruning), which ensures precise entity grounding to the query, evaluates answer-supporting relevance, and prunes redundant context to retain only knowledge essential for response generation. Extensive experiments across diverse multimodal benchmarks demonstrate that M$^3$KG-RAG significantly enhances MLLMs' multimodal reasoning and grounding over existing approaches.

Related papers

• MMhops-R1: Multimodal Multi-hop Reasoning [89.68086555694084]
  We introduce MMhops, a novel benchmark designed to evaluate and foster multi-modal multi-hop reasoning.<n> MMhops dataset comprises two challenging task formats, Bridging and Comparison.<n>We propose MMhops-R1, a novel multi-modal Retrieval-Augmented Generation framework for dynamic reasoning.
  arXiv  Detail & Related papers  (2025-12-15T17:29:02Z)
• mKG-RAG: Multimodal Knowledge Graph-Enhanced RAG for Visual Question Answering [29.5761347590239]
  Retrieval-Augmented Generation (RAG) has been proposed to expand internal knowledge of Multimodal Large Language Models (MLLMs)<n>In this paper, we propose a novel multimodal knowledge-augmented generation framework (mKG-RAG) based on multimodal KGs for knowledge-intensive VQA tasks.
  arXiv  Detail & Related papers  (2025-08-07T12:22:50Z)
• VAT-KG: Knowledge-Intensive Multimodal Knowledge Graph Dataset for Retrieval-Augmented Generation [16.248703946640735]
  Multimodal Knowledge Graphs (MMKGs) represent explicit knowledge across multiple modalities.<n>Visual-Audio-Text Knowledge Graph (VAT-KG) is first concept-centric and knowledge-intensive multimodal knowledge graph.
  arXiv  Detail & Related papers  (2025-06-11T07:22:57Z)
• CLaMR: Contextualized Late-Interaction for Multimodal Content Retrieval [70.9990850395981]
  We introduce CLaMR, a multimodal, late-interaction retriever that jointly indexes 4 modalities: video frames, transcribed speech, on-screen text, and metadata.<n>CLaMR is trained to enhance dynamic modality selection via two key innovations.
  arXiv  Detail & Related papers  (2025-06-06T15:02:30Z)
• Benchmarking Retrieval-Augmented Multimodal Generation for Document Question Answering [60.062194349648195]
  Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents.<n>Current document retrieval-augmented generation (DocRAG) methods remain limited by their text-centric approaches.<n>We introduce MMDocRAG, a comprehensive benchmark featuring 4,055 expert-annotated QA pairs with multi-page, cross-modal evidence chains.
  arXiv  Detail & Related papers  (2025-05-22T09:52:57Z)
• Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts [56.7225771305861]
  This paper introduces Multi-Modal Retrieval-Augmented Generation (M$2$RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models.<n>The benchmark comprises four tasks: image captioning, multi-modal question answering, multi-modal fact verification, and image reranking.<n>To enhance the context utilization capabilities of MLLMs, we also introduce Multi-Modal Retrieval-Augmented Instruction Tuning (MM-RAIT)
  arXiv  Detail & Related papers  (2025-02-24T16:25:25Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Needle In A Multimodal Haystack [79.81804334634408]
  We present the first benchmark specifically designed to evaluate the capability of existing MLLMs to comprehend long multimodal documents. Our benchmark includes three types of evaluation tasks: multimodal retrieval, counting, and reasoning. We observe that existing models still have significant room for improvement on these tasks, especially on vision-centric evaluation.
  arXiv  Detail & Related papers  (2024-06-11T13:09:16Z)
• Multimodal Reasoning with Multimodal Knowledge Graph [19.899398342533722]
  Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge. We propose the Multimodal Reasoning with Multimodal Knowledge Graph (MR-MKG) method to learn rich and semantic knowledge across modalities.
  arXiv  Detail & Related papers  (2024-06-04T07:13:23Z)
• MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries [22.4349439498591]
  Retrieval-augmented generation (RAG) augments large language models (LLM) by retrieving relevant knowledge. Existing RAG systems are inadequate in answering multi-hop queries, which require retrieving and reasoning over multiple pieces of supporting evidence. We develop a novel dataset, MultiHop-RAG, which consists of a knowledge base, a large collection of multi-hop queries, their ground-truth answers, and the associated supporting evidence.
  arXiv  Detail & Related papers  (2024-01-27T11:41:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.