HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2504.12330v1
• Date: Sun, 13 Apr 2025 06:55:33 GMT
• Title: HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation
• Authors: Pei Liu, Xin Liu, Ruoyu Yao, Junming Liu, Siyuan Meng, Ding Wang, Jun Ma,
• Abstract summary: HM-RAG is a novel Hierarchical Multi-agent Multimodal RAG framework.<n>It pioneers collaborative intelligence for dynamic knowledge synthesis across structured, unstructured, and graph-based data.
• Score: 11.53083922927901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge, conventional single-agent RAG remains fundamentally limited in resolving complex queries demanding coordinated reasoning across heterogeneous data ecosystems. We present HM-RAG, a novel Hierarchical Multi-agent Multimodal RAG framework that pioneers collaborative intelligence for dynamic knowledge synthesis across structured, unstructured, and graph-based data. The framework is composed of three-tiered architecture with specialized agents: a Decomposition Agent that dissects complex queries into contextually coherent sub-tasks via semantic-aware query rewriting and schema-guided context augmentation; Multi-source Retrieval Agents that carry out parallel, modality-specific retrieval using plug-and-play modules designed for vector, graph, and web-based databases; and a Decision Agent that uses consistency voting to integrate multi-source answers and resolve discrepancies in retrieval results through Expert Model Refinement. This architecture attains comprehensive query understanding by combining textual, graph-relational, and web-derived evidence, resulting in a remarkable 12.95% improvement in answer accuracy and a 3.56% boost in question classification accuracy over baseline RAG systems on the ScienceQA and CrisisMMD benchmarks. Notably, HM-RAG establishes state-of-the-art results in zero-shot settings on both datasets. Its modular architecture ensures seamless integration of new data modalities while maintaining strict data governance, marking a significant advancement in addressing the critical challenges of multimodal reasoning and knowledge synthesis in RAG systems. Code is available at https://github.com/ocean-luna/HMRAG.

Related papers

• UniversalRAG: Retrieval-Augmented Generation over Multiple Corpora with Diverse Modalities and Granularities [53.76854299076118]
  UniversalRAG is a novel RAG framework designed to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. We propose a modality-aware routing mechanism that dynamically identifies the most appropriate modality-specific corpus and performs targeted retrieval within it. We validate UniversalRAG on 8 benchmarks spanning multiple modalities, showing its superiority over modality-specific and unified baselines.
  arXiv  Detail & Related papers  (2025-04-29T13:18:58Z)
• Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases [0.0]
  This paper presents a Small Language Model(SLM)-driven system that synergizes advancements in lightweight Retrieval-Augmented Generation (RAG) and semantic-aware data structuring.<n>By integrating MiniRAG's semantic-aware heterogeneous graph indexing and topology-enhanced retrieval with SLM-powered structured data extraction, our system addresses the limitations of traditional methods.<n> Experimental results demonstrate superior performance in accuracy and efficiency, while the introduction of semantic entropy as an unsupervised evaluation metric provides robust insights into model uncertainty.
  arXiv  Detail & Related papers  (2025-04-08T03:28:03Z)
• Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning [51.54046200512198]
  Retrieval-augmented generation (RAG) is extensively utilized to incorporate external, current knowledge into large language models.<n>A standard RAG pipeline may comprise several components, such as query rewriting, document retrieval, document filtering, and answer generation.<n>To overcome these challenges, we propose treating the RAG pipeline as a multi-agent cooperative task, with each component regarded as an RL agent.
  arXiv  Detail & Related papers  (2025-01-25T14:24:50Z)
• Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG [0.8463972278020965]
  Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human like text generation and natural language understanding.<n>Retrieval Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real time data retrieval to provide contextually relevant responses.<n>Agentic Retrieval-Augmented Generation (RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline.
  arXiv  Detail & Related papers  (2025-01-15T20:40:25Z)
• QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance [1.433758865948252]
  This work presents a novel architecture for building Retrieval-Augmented Generation (RAG) systems.<n>RAG architecture is constructed to generate responses from the target document.<n>We introduce QuIM-RAG, a novel approach for the retrieval mechanism in our system.
  arXiv  Detail & Related papers  (2025-01-06T01:07:59Z)
• Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models [0.0]
  We propose a methodology that combines several advanced techniques in Large Language Model (LLM) retrieval to support the development of robust, multi-source question-answer systems.<n>This methodology is designed to integrate information from diverse data sources, through a coordinated multi-agent orchestration and dynamic retrieval approach.<n>Our results indicate that this approach enhances response accuracy and relevance, offering a versatile and scalable framework for developing question-answer systems.
  arXiv  Detail & Related papers  (2024-12-23T20:28:20Z)
• ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation [26.4086456393314]
  Long-form text generation requires coherent, comprehensive responses that address complex queries with both breadth and depth. Existing iterative retrieval-augmented generation approaches often struggle to delve deeply into each facet of complex queries. This paper introduces ConTReGen, a novel framework that employs a context-driven, tree-structured retrieval approach.
  arXiv  Detail & Related papers  (2024-10-20T21:17:05Z)
• BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data [61.936320820180875]
  Large language models (LLMs) have become increasingly pivotal across various domains. BabelBench is an innovative benchmark framework that evaluates the proficiency of LLMs in managing multimodal multistructured data with code execution. Our experimental findings on BabelBench indicate that even cutting-edge models like ChatGPT 4 exhibit substantial room for improvement.
  arXiv  Detail & Related papers  (2024-10-01T15:11:24Z)
• FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research [70.6584488911715]
  retrieval-augmented generation (RAG) has attracted considerable research attention.<n>Existing RAG toolkits are often heavy and inflexibly, failing to meet the customization needs of researchers.<n>Our toolkit has implemented 16 advanced RAG methods and gathered and organized 38 benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-22T12:12:40Z)
• STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases [93.96463520716759]
  We develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Knowledge Bases. Our benchmark covers three domains: product search, academic paper search, and queries in precision medicine. We design a novel pipeline to synthesize realistic user queries that integrate diverse relational information and complex textual properties.
  arXiv  Detail & Related papers  (2024-04-19T22:54:54Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition [49.463864096615254]
  We analyze the influence of learning complexity, including the input complexity and model complexity. We propose a recurrent convolutional network (RCN) to explore the shallower-architecture and lower-resolution input data. We develop three parameter-free modules to integrate with RCN without increasing any learnable parameters.
  arXiv  Detail & Related papers  (2020-06-17T06:19:24Z)

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.