Pith. sign in

REVIEW 10 cited by

A Survey of Multimodal Retrieval-Augmented Generation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2504.08748 v1 pith:ULEQCEAK submitted 2025-03-26 cs.IR cs.AIcs.CLcs.ETcs.LG

Lang Mei , Siyu Mo , Zhihan Yang , Chong Chen This is my paper · ORCID
classification cs.IRcs.AIcs.CLcs.ETcs.LG
keywords generationmultimodalmragretrievalretrieval-augmenteddataenhancesinformation
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Multimodal Retrieval-Augmented Generation (MRAG) enhances large language models (LLMs) by integrating multimodal data (text, images, videos) into retrieval and generation processes, overcoming the limitations of text-only Retrieval-Augmented Generation (RAG). While RAG improves response accuracy by incorporating external textual knowledge, MRAG extends this framework to include multimodal retrieval and generation, leveraging contextual information from diverse data types. This approach reduces hallucinations and enhances question-answering systems by grounding responses in factual, multimodal knowledge. Recent studies show MRAG outperforms traditional RAG, especially in scenarios requiring both visual and textual understanding. This survey reviews MRAG's essential components, datasets, evaluation methods, and limitations, providing insights into its construction and improvement. It also identifies challenges and future research directions, highlighting MRAG's potential to revolutionize multimodal information retrieval and generation. By offering a comprehensive perspective, this work encourages further exploration into this promising paradigm.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DualG-MRAG: Decoupling Macro-Reasoning and Micro-Matching for Multimodal Retrieval-Augmented Generation

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Decoupling macro topological routing from micro visual matching, plus query-driven GNN path decoding, improves multimodal multi-hop retrieval and QA over strong MM-RAG baselines.

  2. M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation

    cs.IR 2025-08 conditional novelty 6.0 of 10

    M2IO-R1 uses GRPO reinforcement learning to train a 3B model that selects and places retrieved images into generated text answers, improving MRAMG quality on several benchmarks.

  3. BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions

    cs.CL 2025-06 conditional novelty 6.0 of 10

    BioMol-MQA is a new multimodal QA dataset for polypharmacy in which LLMs perform poorly zero-shot but much better when given gold context.

  4. FinS-Pilot: A Benchmark for Online Financial RAG System

    cs.CL 2025-05 reject novelty 6.0 of 10

    FinS-Pilot is a small benchmark of real-world financial assistant queries with real-time API data and text corpus, used to compare Chinese LLMs on financial RAG tasks.

  5. mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation

    cs.AI 2025-05 conditional novelty 6.0 of 10

    A systematic empirical study finds that for multimodal RAG, EVA-CLIP retrieval, listwise LVLM reranking, and feeding only the top-ranked document works best, with a self-reflection agent adding further gains.

  6. Benchmarking Multimodal Knowledge Conflict for Large Multimodal Models

    cs.LG 2025-05 conditional novelty 6.0 of 10

    MMKC-Bench provides a human-verified benchmark of multimodal knowledge conflicts and shows that current LMMs prefer internal parametric knowledge over external evidence.

  7. Evidence-Grounded Multimodal Knowledge Graph Construction for Multi-Lecture Educational Reasoning

    cs.AI 2026-08 conditional novelty 5.0 of 10

    A pipeline that transcribes lecture videos, reads slides and diagrams, and builds an evidence-linked knowledge graph, tested on three neural-network lectures with a three-question sanity check.

  8. SearchArt: Training Long-Horizon Search Agent with Scalable Synthetic and Verified Task

    cs.IR 2026-07 conditional novelty 5.0 of 10

    SearchArt post-trains Qwen3.5-27B on verification-filtered synthetic search trajectories, scoring 74.39 on BrowseComp-ZH, 70.06 on BrowseComp, and 52.55 on DeepResearch-Bench, competitive with several 200B-700B agents.

  9. UniRank: End-to-End Domain-Specific Reranking of Hybrid Text-Image Candidates

    cs.IR 2026-02 conditional novelty 5.0 of 10

    UniRank natively scores hybrid text-image candidates with a VLM, then SFT plus hard-negative RLHF adapts it to a domain; it outperforms baselines on scientific literature and design patent retrieval.

  10. MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs

    cs.AI 2025-07 conditional novelty 5.0 of 10

    MMGraphRAG links scene-graph entities from images to text knowledge graph entities via SpecLink, and reports accuracy gains over naive RAG and GraphRAG on multimodal document QA.

Pith tools