Multimodal Rag Survey

Name: Multimodal Rag Survey
Author: Llm Lab Org

by Llm Lab Org llm-lab-org/multimodal-rag-survey

Free2AITools Nexus Index

45.2

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 57

P: Popularity 63

R: Recency 79

Q: Quality 70

Tech Context

Vital Performance —

Source →

- Lang

499 Stars

Alpha Reliability

Tool Information Summary
Entity Passport
Registry ID	llm-lab-org/multimodal-rag-survey
Provider	github

📜

Cite this tool

Academic & Research Attribution

BibTeX

@misc{gh_tool_llm_lab_org_multimodal_rag_survey,
  author = {Llm Lab Org},
  title = {Multimodal Rag Survey Tool},
  year = {2026},
  howpublished = {\url{https://github.com/llm-lab-org/Multimodal-RAG-Survey}},
  note = {Accessed via Free2AITools.}
}

APA Style

Llm Lab Org. (2026). Multimodal Rag Survey [Tool]. Free2AITools. https://github.com/llm-lab-org/Multimodal-RAG-Survey

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🐙 GitHub Clone

git clone https://github.com/llm-lab-org/Multimodal-RAG-Survey

🐙 Git Clone

git clone https://github.com/llm-lab-org/Multimodal-RAG-Survey

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 57

Popularity (P) 63

Recency (R) 79

Quality (Q) 70

💬 Index Insight

FNI V2.0 for Multimodal Rag Survey: Authority (A:57), Popularity (P:63), Recency (R:79), Quality (Q:70). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📋 Specs

Language: —
License: —
Version: —

Technical Documentation

Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation

This repository is designed to collect and categorize papers related to Multimodal Retrieval-Augmented Generation (RAG) according to our survey paper: Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation. Given the rapid growth in this field, we will continuously update both the paper and this repository to serve as a resource for researchers working on future projects.

📢 News

January 9, 2026: Happy New Year! We’ve added some new papers in the field.
September 19, 2025: We've just added new papers to our repository.
August 20, 2025: The poster and slide for this survey paper have been added to the repository for readers.
August 1, 2025: We've just added new papers to our repository; a major update!
June 2, 2025: A new enhanced version of our paper is out now on arXiv! This update also includes new related papers and covers new topics such as agentic interaction and audio-centric retrieval.
May 15, 2025: This paper has been accepted for publication in the ACL 2025 Findings.
April 18, 2025: Our website for this topic is up now.
February 17, 2025: We release the first survey for Multimodal Retrieval-Augmented Generation. Feel free to cite, contribute, or open a pull request to add recent related papers!