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Research#llm🔬 ResearchAnalyzed: Dec 25, 2025 02:34

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

Published:Dec 24, 2025 05:00
1 min read
ArXiv NLP

Analysis

This paper introduces M$^3$KG-RAG, a novel approach to Retrieval-Augmented Generation (RAG) that leverages multi-hop multimodal knowledge graphs (MMKGs) to enhance the reasoning and grounding capabilities of multimodal large language models (MLLMs). The key innovations include a multi-agent pipeline for constructing multi-hop MMKGs and a GRASP (Grounded Retrieval And Selective Pruning) mechanism for precise entity grounding and redundant context pruning. The paper addresses limitations in existing multimodal RAG systems, particularly in modality coverage, multi-hop connectivity, and the filtering of irrelevant knowledge. The experimental results demonstrate significant improvements in MLLMs' performance across various multimodal benchmarks, suggesting the effectiveness of the proposed approach in enhancing multimodal reasoning and grounding.
Reference

“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.”

PermalinkArXiv NLP
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:56

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

Published:Dec 23, 2025 07:54
1 min read
ArXiv

Analysis

The article introduces M$^3$KG-RAG, a system that combines multi-hop reasoning, multimodal data, and knowledge graphs to improve retrieval-augmented generation (RAG) for language models. The focus is on enhancing the accuracy and relevance of generated text by leveraging structured knowledge and diverse data types. The use of multi-hop reasoning suggests an attempt to address complex queries that require multiple steps of inference. The integration of multimodal data (likely images, audio, etc.) indicates a move towards more comprehensive and contextually rich information retrieval. The paper likely details the architecture, training methodology, and evaluation metrics of the system.
Reference

“The paper likely details the architecture, training methodology, and evaluation metrics of the system.”

PermalinkArXiv