mRAG: Multimodal Retrieval-Augmented Generation

Updated 14 August 2025

mRAG is a multimodal framework that retrieves and fuses evidence from text, images, audio, and video to reduce hallucinations and improve factual grounding.
It employs modality-specific parsing, shared embedding spaces, and adaptive query planning to dynamically integrate heterogeneous data sources.
By interleaving evidence and leveraging agent-based reasoning, mRAG sets new benchmarks in performance, reliability, and real-world application.

Multimodal Retrieval-Augmented Generation (mRAG) is a paradigm that integrates retrieval of multimodal evidence—encompassing text, images, audio, video, and structured data—into the generation processes of large language and vision-LLMs. This approach enables models to ground responses in up-to-date, external, and heterogeneous sources, reducing hallucination and enhancing factual accuracy, especially in tasks that span multiple data modalities. Recent mRAG advances cover theoretical design, practical architectures, benchmark development, evaluation metrics, privacy/security, and agent-based retrieval planning, reflecting its breadth and centrality in the next generation of AI systems.

1. Foundations and Core Architecture

mRAG extends traditional (text-only) RAG by introducing modules tailored for diverse modalities and their integration. The canonical mRAG pipeline comprises multiple highly-interacting stages:

Document Parsing and Indexing: Raw, unstructured multimodal corpora (e.g., web pages, scientific papers, video/audio archives) are parsed using modality-specific extraction tools. For example, OCR and layout detectors generate structured representations for parsed documents; images and videos are described both as visual features and with derived captions or scene graphs; audio is transcribed with ASR models.
Multimodal Embedding and Index Construction: All content types are encoded into a shared, or at least comparable, embedding space using encoders such as CLIP, BLIP, or modality-adaptive networks. This enables nearest neighbour search across text, images, and other modalities using metrics like cosine similarity.
Query Planning & Adaptive Retrieval: Instead of static query execution, advanced mRAG pipelines employ planning modules that classify the retrieval need (text, image, audio, or composite), decompose multi-hop queries, and dynamically re-route queries (e.g., R1-Router, CogPlanner) (Peng et al., 28 May 2025, Yu et al., 26 Jan 2025).
Multimodal Retrieval and Re-ranking: Candidate evidence spans multiple modalities. Score-fusion (combining visual and textual similarity), feature-fusion (processing multimodal inputs jointly), and cross-modal re-ranking (e.g., with LVLMs or listwise models mitigating positional biases) are essential (Hu et al., 29 May 2025).
Context Integration and Generation: Retrieved content is integrated into the context window of large language, vision-language, or multimodal generative models. Evidence localization (e.g., mR²AG’s “Relevance-Reflection” (Zhang et al., 22 Nov 2024)) and output interleaving (placing images, referencing video segments) are employed for precise, context-rich responses.
Self-Reflection / Agentic Reasoning: Recent frameworks introduce agent-based or reflective loops, iteratively verifying and selecting evidence and even planning next retrieval actions (Hu et al., 29 May 2025, Yu et al., 26 Jan 2025).

2. Retrieval Strategies, Fusion, and Query Planning

mRAG retrieval strategies depend critically on modality alignment and the ability to access semantically coherent evidence:

Strategy	Functionality	Notables
Score Fusion	Weighted sum of individual modal similarities (e.g., S = φ_vis(I) + φ_txt(τ))	CLIP_SF, EVA-CLIP_SF
Feature Fusion	Joint representation across modalities at encoder level	BLIP_FF
Agent-based Planning	Iterative, decision-based retrieval and query reformulation	R1-Router, CogPlanner

Score fusion retrieves top candidates based on combined similarity, often using FAISS or similar tools for efficient search over large corpora (Hu et al., 29 May 2025). Feature fusion enables more expressive joint-matchings, and LVLM-based retrievers leverage foundation models for zero-shot open-domain cross-modal retrieval.

Advanced pipelines introduce retrieval and query planning modules that dynamically determine, at each reasoning step, whether and from where to retrieve. For example, R1-Router uses a reinforcement learning approach to maximize step-wise reward on routing queries among heterogeneous knowledge bases, conditionally reducing unnecessary retrievals and improving flexibility (Peng et al., 28 May 2025). CogPlanner operates with parallel or sequential planning policies, iteratively refining queries and retrieval actions (Yu et al., 26 Jan 2025). In essence, these approaches tightly couple evidence acquisition with ongoing reasoning.

3. Multimodal Generation, Output Structuring, and Evidence Interleaving

The generation stage integrates retrieved evidence into output responses, with increasing emphasis on multimodal answers:

Plaintext Only: Early or baseline mRAG systems simply concatenate retrieved passages, often leading to noisy or verbose outputs.
Multimodal Output Interleaving: Recent frameworks enforce explicit interleaving of images within text, as in MRAMG, M²RAG, and M2IO-R1 (Yu et al., 6 Feb 2025, Ma et al., 25 Nov 2024, Xiao et al., 8 Aug 2025). Inserter modules (e.g., RL-based Inserter-R1-3B) sequentially decide image selection and placement at the sentence level, trained via reward signals that balance recall and positional fidelity.
Evidence Localization and Self-Reflection: Models such as mR²AG (Zhang et al., 22 Nov 2024) introduce “Reflection” steps, where an MLLM adaptively selects whether retrieval is necessary (“Retrieval-Reflection”) and then, for each candidate passage, judges its relevance (“Relevance-Reflection”), grounding the generation on explicit, verifiable evidence.
Graph-based Reasoning and Path Traversal: MMGraphRAG constructs structural multimodal knowledge graphs (MMKG), linking entities extracted from text and scene-graph representations of images, allowing reasoning along explicit logical chains between modalities (Wan et al., 28 Jul 2025).

A consistent empirical finding is that positional biases (the “lost-in-the-middle” effect in LVLMs) necessitate careful evidence ordering. Listwise re-ranking paired with agentic generation boosts performance up to 5% even without additional model fine-tuning (Hu et al., 29 May 2025).

4. Datasets, Benchmarks, and Evaluation Methodologies

The mRAG research ecosystem has produced a diversity of benchmarks targeting different aspects of the pipeline:

Benchmark/Resource	Modalities/Tested	Tasks/Highlights
MRAG-Bench (Hu et al., 10 Oct 2024)	Text, Image	Vision-centric, augments QA with external images; human–model gap highlighted
M²RAG (Liu et al., 24 Feb 2025)	Text, Image	Captioning, QA, Fact Verification, Image Reranking; MM-RAIT for instruction tuning
MRAMG-Bench (Yu et al., 6 Feb 2025)	Text, Image	Multi-image integration, complex ordering in answers, LLM-based and statistical evaluation
M²RAG Benchmark (Ma et al., 25 Nov 2024)	Text, Image	Joint and separate generation, domain analyses, performance by topic
mmRAG (Xu et al., 16 May 2025)	Text, Tables, KGs	Modular evaluation (retrieval, routing, generation), annotated by relevance
Chart-MRAG Bench (Yang et al., 20 Feb 2025)	Text, Charts	Chart-intensive QA, emphasizes text-over-visual bias in MLLMs
MMGraphRAG Testbeds (Wan et al., 28 Jul 2025)	Text, Images	Multimodal knowledge graph reasoning, explicit path-based retrieval

Evaluation metrics range from exact match, ROUGE/BLEU/CIDEr/BERTScore for text, statistical and LLM-based image position/recall/order for images, and modular IR metrics (NDCG, MAP, Hits) for granular component evaluation (Xu et al., 16 May 2025). Agentic and adaptive planning steps are evaluated with token-level F1, claim-level recall, and advantage estimation aligned with reinforcement learning frameworks (Yu et al., 26 Jan 2025, Peng et al., 28 May 2025).

5. Limitations, Open Problems, and Security

Key unresolved challenges in mRAG include:

Modality Bias and Alignment: MLLMs favor text, even when visual evidence contains more precise information (text-over-visual bias), especially in chart/structured visual reasoning (Yang et al., 20 Feb 2025).
Retrieval Limitations: Unified embedding methods sometimes fail in dense visual formats (e.g., charts, complex documents). Coverage and correctness scores plateau below 60–75% even with perfect retrieval (Yang et al., 20 Feb 2025).
Privacy and Security Risks: mRAG systems face severe privacy vulnerabilities since external multimodal knowledge bases can inadvertently expose sensitive images, audio, or texts. Compositional structured prompt attacks can trigger both direct and indirect (descriptive) leakage, as shown by robust attacks in both vision–language and speech–language settings (Zhang et al., 20 May 2025). Knowledge poisoning attacks, such as Poisoned-MRAG, can inject as few as five image–text pairs to manipulate system output with a 98% attack success rate, while existing defenses (paraphrasing, duplication removal, purification) only partially mitigate these threats (Liu et al., 8 Mar 2025).
Computational and Latency Costs: Adaptive/interleaved planning architectures improve efficiency, but agentic and RL-driven reinforcement approaches must balance increased decision complexity with minimal inference latency (Xiao et al., 8 Aug 2025, Peng et al., 28 May 2025).
Handling Structured Data and Knowledge Graphs: Parsing, representation, and reasoning over tables, charts, and KGs require dedicated modules (e.g., multi-granularity retrieval (Xu et al., 1 May 2025), MMGraphRAG (Wan et al., 28 Jul 2025)) to achieve cross-modal logical chaining and maintain interpretability.
Evaluation Standardization: While benchmark coverage has expanded, standardizing multimodal hallucination and “hallucination in visual outputs” metrics remains ongoing (Mei et al., 26 Mar 2025).

6. Applications, Impacts, and Future Directions

mRAG systems are increasingly deployed and studied in:

Complex, Multi-evidence Reasoning: Legal, accident, and scientific document synthesis, where multi-aspect queries are prevalent (Besta et al., 7 Jun 2024).
Video and Real-time Perceptual Understanding: Human–robot interaction, situational command, video QA, and educational technology harness adaptive multimodal memory (Mao et al., 29 May 2025).
Education, Instruction, and Journalism: Step-by-step guides in recipes or manuals, travel and product recommendations, and journalistic storytelling with verifiable multimodal facts (Xiao et al., 8 Aug 2025).
Medical, Security, and Privacy Sensitive Domains: Systemic risks necessitate advances in privacy-preserving mRAG and robust, interpretable decision-making (Zhang et al., 20 May 2025).
Agent-Based Interactive Assistants: Dynamic, multi-turn dialogues, agentic information acquisition, and mixed-modality decision-making (Yu et al., 26 Jan 2025, Hu et al., 29 May 2025).

Directions for further research include improved document parsing (with layout and OCR retention), unified cross-modal representation, fully agentic and collaborative planning with reinforcement learning, and robust, privacy-preserving systems for real-world deployment. The drive toward interpretability, compositional reasoning (especially over structured and graph forms), and scalable multi-agent frameworks for dynamic retrieval is evident in the progression of recent literature (Mei et al., 26 Mar 2025, Wan et al., 28 Jul 2025).

7. Summary Table: Major Milestones and Themes in mRAG

Theme/Feature	Representative Papers	Key Advancement
Adaptive Planning/Agents	CogPlanner, R1-Router (Yu et al., 26 Jan 2025, Peng et al., 28 May 2025)	Dynamic multi-step, modality-aware retrieval
Multi-Aspect Retrieval	Multi-Head RAG (Besta et al., 7 Jun 2024)	Attention-head embeddings for multi-aspect queries
Multimodal Output	MRAMG, M²RAG, M2IO-R1 (Yu et al., 6 Feb 2025, Ma et al., 25 Nov 2024, Xiao et al., 8 Aug 2025)	Integrated text+image generation, RL-based insertion
Graph-Based Reasoning	MMGraphRAG (Wan et al., 28 Jul 2025)	Scene/text KG construction, path-based retrieval
Privacy/Security	Poisoned-MRAG, Beyond Text (Liu et al., 8 Mar 2025, Zhang et al., 20 May 2025)	Poisoning attacks, structured prompt leakage

These developments collectively position mRAG as a critical and rapidly evolving paradigm for AI models operating on dynamic, heterogeneous, and evidence-intensive tasks across research and application domains.