MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation

Chi-Hsiang Hsiao; Chu-Song Chen; Tzung-Sheng Lin; Yi-Cheng Wang; Yi-Ren Yeh

arxiv: 2512.20626 · v2 · submitted 2025-11-26 · 💻 cs.AI · cs.CL· cs.CV· cs.IR

MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation

Chi-Hsiang Hsiao , Yi-Cheng Wang , Tzung-Sheng Lin , Yi-Ren Yeh , Chu-Song Chen This is my paper

Pith reviewed 2026-05-17 05:33 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.CVcs.IR

keywords Retrieval Augmented GenerationKnowledge GraphsMultimodalQuestion AnsweringVisual CuesCross-modal ReasoningLarge Language Models

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The pith

Incorporating visual cues into knowledge graphs enhances retrieval-augmented generation for better document comprehension.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a multimodal knowledge graph approach for retrieval-augmented generation that adds visual cues to traditional text structures. This targets the limits of large language models when handling long-form or domain-specific content such as full books, where context windows prevent deep reasoning. The method folds visual information into graph building, the retrieval step, and answer generation to support cross-modal reasoning. If the claim holds, systems could answer complex questions over mixed text-and-image materials more accurately than current text-only RAG pipelines.

Core claim

Our method incorporates visual cues into the construction of knowledge graphs, the retrieval phase, and the answer generation process. Experimental results across both global and fine-grained question answering tasks show that our approach consistently outperforms existing RAG-based approaches on both textual and multimodal corpora.

What carries the argument

The multimodal knowledge graph that structures entity-centric and hierarchical information by combining textual, visual, and spatial cues to support cross-modal reasoning during retrieval and generation.

Load-bearing premise

Adding visual cues supplies complementary insights that improve understanding without introducing noise or needing domain-specific adjustments.

What would settle it

Evaluating the multimodal method against a text-only knowledge-graph RAG baseline on a held-out multimodal QA benchmark and observing equal or lower accuracy would show the added cues do not help.

Figures

Figures reproduced from arXiv: 2512.20626 by Chi-Hsiang Hsiao, Chu-Song Chen, Tzung-Sheng Lin, Yi-Cheng Wang, Yi-Ren Yeh.

**Figure 2.** Figure 2: Prompt for extracting entities and relations during the initial construction of the MMKG. [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗

**Figure 3.** Figure 3: Prompt for MMKG refinement stage. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗

**Figure 4.** Figure 4: Prompts for MMKG-augmented answer generation. (a) Generates an intermediate answer from the [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗

**Figure 5.** Figure 5: (a) Prompt used for global question generation. (b) Example global questions. [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗

**Figure 6.** Figure 6: Overview of the global and local QA evaluation prompts. [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗

**Figure 7.** Figure 7: Example of enhanced multimodal relations. (a) A slide page from an environmental report. (b) Page-level [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗

**Figure 8.** Figure 8: Example of enhanced cross-page relations. (a) A slide page from an environmental report. (b) Page-level [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗

read the original abstract

Retrieval-augmented generation (RAG) enables large language models (LLMs) to dynamically access external information, which is powerful for answering questions over previously unseen documents. Nonetheless, they struggle with high-level conceptual understanding and holistic comprehension due to limited context windows, which constrain their ability to perform deep reasoning over long-form, domain-specific content such as full-length books. To solve this problem, knowledge graphs (KGs) have been leveraged to provide entity-centric structure and hierarchical summaries, offering more structured support for reasoning. However, existing KG-based RAG solutions remain restricted to text-only inputs and fail to leverage the complementary insights provided by other modalities such as vision. On the other hand, reasoning from visual documents requires textual, visual, and spatial cues into structured, hierarchical concepts. To address this issue, we introduce a multimodal knowledge graph-based RAG that enables cross-modal reasoning for better content understanding. Our method incorporates visual cues into the construction of knowledge graphs, the retrieval phase, and the answer generation process. Experimental results across both global and fine-grained question answering tasks show that our approach consistently outperforms existing RAG-based approaches on both textual and multimodal corpora.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MegaRAG adds visual cues across KG construction, retrieval, and generation but the outperformance claim on text-only inputs rests on an untested inference step with no visible ablations.

read the letter

This paper extends knowledge-graph RAG by folding visual cues into graph building, retrieval, and answer generation so the system can handle documents that mix text with images or diagrams. The pipeline description is clear enough on paper and targets a real limitation: standard RAG and text-only KG methods lose high-level structure on long content. That extension is the main concrete step forward from prior work cited in the abstract. It also tries to keep the entity-centric advantages of KGs while adding spatial and visual signals, which could matter for applications like technical manuals or illustrated books. The authors position the work against existing text-only KG-RAG solutions, and the claim that the same architecture works on both textual and multimodal corpora is at least a coherent research question. The soft spot is the missing evidence. The abstract states consistent outperformance on global and fine-grained QA tasks but supplies no numbers, baselines, or error bars. For purely textual inputs the method must run some upstream visual inference, yet nothing isolates whether that step adds signal, introduces noise, or needs domain-specific tuning. Without those controls the headline advantage of the multimodal KG itself is not yet established. The stress-test note about untested assumptions on textual corpora therefore lands. Readers working on practical RAG pipelines for mixed-media documents could still extract the integration pattern as a starting point. The work shows clear engagement with the literature and a practical goal, so it is worth sending to peer review once the full experiments and ablations are in place.

Referee Report

2 major / 2 minor

Summary. The paper introduces MegaRAG, a multimodal knowledge graph-based retrieval-augmented generation framework. It extends text-only KG-RAG by incorporating visual cues into knowledge graph construction, retrieval, and answer generation to support cross-modal reasoning over long-form content such as books. The central claim is that this approach consistently outperforms existing RAG methods on both global and fine-grained question-answering tasks across textual and multimodal corpora.

Significance. If the outperformance claims are substantiated with rigorous experiments, the work would represent a meaningful extension of structured RAG to multimodal settings, addressing limitations in context windows and holistic comprehension. The introduction of a multimodal KG is a clear conceptual contribution, though its practical value hinges on demonstrating that visual cues provide net-positive information without introducing noise or domain-specific tuning.

major comments (2)

[Abstract] Abstract: the assertion that 'experimental results across both global and fine-grained question answering tasks show that our approach consistently outperforms existing RAG-based approaches' is presented without any quantitative metrics, baselines, error bars, or dataset details. This absence directly undermines the central empirical claim and must be addressed with concrete results tables.
[Results section] Results section (or equivalent): no ablation or controlled experiment isolates the contribution of the visual-cue integration step, especially on purely textual corpora. The headline superiority of the multimodal KG-RAG architecture cannot be established until it is shown that the upstream visual inference (e.g., captioning or image generation) does not introduce artifacts or require per-domain hyper-parameter tuning.

minor comments (2)

The manuscript would benefit from an explicit diagram or formal definition of the multimodal knowledge graph nodes, edges, and how visual cues are encoded as attributes.
Ensure all baselines are clearly named and referenced; the current text refers to 'existing RAG-based approaches' without specific citations or implementation details.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment below and have updated the paper to strengthen the presentation of our empirical results.

read point-by-point responses

Referee: [Abstract] Abstract: the assertion that 'experimental results across both global and fine-grained question answering tasks show that our approach consistently outperforms existing RAG-based approaches' is presented without any quantitative metrics, baselines, error bars, or dataset details. This absence directly undermines the central empirical claim and must be addressed with concrete results tables.

Authors: We agree that the abstract would be strengthened by including concrete quantitative support. In the revised manuscript we will update the abstract to report key performance metrics (e.g., accuracy or F1 improvements), the specific baselines compared, dataset names, and error bars or standard deviations from our main experiments. revision: yes
Referee: [Results section] Results section (or equivalent): no ablation or controlled experiment isolates the contribution of the visual-cue integration step, especially on purely textual corpora. The headline superiority of the multimodal KG-RAG architecture cannot be established until it is shown that the upstream visual inference (e.g., captioning or image generation) does not introduce artifacts or require per-domain hyper-parameter tuning.

Authors: We acknowledge the importance of isolating the visual-cue integration component. Our existing experiments already evaluate the full system on both textual and multimodal corpora and show consistent gains; however, to directly address the referee’s concern we will add a dedicated ablation study in the results section. This study will compare the multimodal KG-RAG against a text-only KG-RAG variant on purely textual corpora, quantify any performance delta attributable to visual cues, and report analysis of potential artifacts or hyper-parameter sensitivity arising from the visual inference step. revision: yes

Circularity Check

0 steps flagged

No circularity: new multimodal KG-RAG architecture described without derivations or self-referential reductions

full rationale

The manuscript introduces MegaRAG as a new method that adds visual cues to knowledge graph construction, retrieval, and generation for cross-modal reasoning. No equations, parameter fittings, or derivation chains are present that reduce a claimed prediction or result back to the inputs by construction. The approach is presented as an architectural extension of existing RAG and KG techniques rather than a mathematical derivation; experimental outperformance is asserted via comparison to baselines without any self-citation load-bearing on uniqueness theorems or ansatz smuggling. The derivation chain is therefore self-contained as a descriptive system design.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on standard domain assumptions about the utility of structured graphs for reasoning and the value of multimodal cues, with no explicit free parameters or invented entities detailed in the abstract.

axioms (1)

domain assumption Knowledge graphs provide entity-centric structure and hierarchical summaries that support reasoning better than raw context windows.
Invoked in the problem statement and motivation for using KGs.

invented entities (1)

Multimodal knowledge graph no independent evidence
purpose: To incorporate visual cues for cross-modal reasoning in RAG.
New structure introduced to address the text-only limitation of prior KG-RAG methods.

pith-pipeline@v0.9.0 · 5524 in / 1142 out tokens · 33109 ms · 2026-05-17T05:33:20.228530+00:00 · methodology

discussion (0)

Forward citations

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Reference graph

Works this paper leans on

10 extracted references · 10 canonical work pages · cited by 2 Pith papers · 2 internal anchors

[1]

Colpali: Efficient document retrieval with vision language models. InICLR. Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu, and Bo Long. 2022. Graph-augmented learning to rank for querying large-scale knowledge graph. In AACL. Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan

work page 2022
[2]

Precise zero-shot dense retrieval without rele- vance labels. InACL. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. The llama 3 herd of models.arXiv preprint arXiv:2407.21783. Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and C...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[3]

Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge-guided retrieval augmented generation. InICLR. Xueguang Ma, Sheng-Chieh Lin, Minghan Li, Wenhu Chen, and Jimmy Lin. 2024. Unifying multimodal retrieval via document screenshot embedding. In EMNLP. Hatem Mousselly-Sergieh, Teresa Botschen, Iryna Gurevych, and Stefan Roth. 2...

work page arXiv 2024
[4]

MinerU: An Open-Source Solution for Precise Document Content Extraction

From louvain to leiden: guaranteeing well- connected communities.Scientific reports. Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, and 1 others. 2024a. Mineru: An open-source solution for precise document content extraction.arXiv preprint arXiv:2409.18839. Peng Wang, Shuai Bai, Sinan Tan, S...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[5]

The primary image document and its text content

Process the Input: a. The primary image document and its text content. b. Additional images from layout detection (if any), appended after the prompt

work page
[6]

Identify all entities from the text content and from any additional images that contain meaningful content. For each identified entity, extract the following information: - entity_name: Name of the entity, using the same language as the input text (capitalize the name if it is in English). - entity_type: One of the following types: [{entity_types}] - enti...

work page
[7]

relationship

From the entities identified in step 2, identify all pairs of (source_entity, target_entity) that are clearly related to each other. For each pair, extract the following information: - source_entity: Name of the source entity, as identified in step 2. - target_entity: Name of the target entity, as identified in step 2. - relationship_description: Explanat...

work page
[8]

content_keywords

Identify high-level keywords that summarize the main concepts, themes, or topics of the entire text and images. Format these as ("content_keywords" {tuple_delimiter}<high_level_keywords>)

work page
[9]

Use **{record_delimiter}** as the list delimiter

Return the output in {language} as a single list of all the entities and relationships identified in steps 2 and 3. Use **{record_delimiter}** as the list delimiter

work page
[10]

entity",

When finished, output {completion_delimiter} ############ -Examples- ############ {examples} ############ -Real Data- ############ Entity_types: {entity_types} Primary Image Document text content: {input_text} Additional Layout Detection Images: (The images are provided by appending them directly after this prompt, with the primary image document as the f...

work page 2031

[1] [1]

Colpali: Efficient document retrieval with vision language models. InICLR. Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu, and Bo Long. 2022. Graph-augmented learning to rank for querying large-scale knowledge graph. In AACL. Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan

work page 2022

[2] [2]

Precise zero-shot dense retrieval without rele- vance labels. InACL. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. The llama 3 herd of models.arXiv preprint arXiv:2407.21783. Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and C...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[3] [3]

Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge-guided retrieval augmented generation. InICLR. Xueguang Ma, Sheng-Chieh Lin, Minghan Li, Wenhu Chen, and Jimmy Lin. 2024. Unifying multimodal retrieval via document screenshot embedding. In EMNLP. Hatem Mousselly-Sergieh, Teresa Botschen, Iryna Gurevych, and Stefan Roth. 2...

work page arXiv 2024

[4] [4]

MinerU: An Open-Source Solution for Precise Document Content Extraction

From louvain to leiden: guaranteeing well- connected communities.Scientific reports. Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, and 1 others. 2024a. Mineru: An open-source solution for precise document content extraction.arXiv preprint arXiv:2409.18839. Peng Wang, Shuai Bai, Sinan Tan, S...

work page internal anchor Pith review Pith/arXiv arXiv 2025

[5] [5]

The primary image document and its text content

Process the Input: a. The primary image document and its text content. b. Additional images from layout detection (if any), appended after the prompt

work page

[6] [6]

Identify all entities from the text content and from any additional images that contain meaningful content. For each identified entity, extract the following information: - entity_name: Name of the entity, using the same language as the input text (capitalize the name if it is in English). - entity_type: One of the following types: [{entity_types}] - enti...

work page

[7] [7]

relationship

From the entities identified in step 2, identify all pairs of (source_entity, target_entity) that are clearly related to each other. For each pair, extract the following information: - source_entity: Name of the source entity, as identified in step 2. - target_entity: Name of the target entity, as identified in step 2. - relationship_description: Explanat...

work page

[8] [8]

content_keywords

Identify high-level keywords that summarize the main concepts, themes, or topics of the entire text and images. Format these as ("content_keywords" {tuple_delimiter}<high_level_keywords>)

work page

[9] [9]

Use **{record_delimiter}** as the list delimiter

Return the output in {language} as a single list of all the entities and relationships identified in steps 2 and 3. Use **{record_delimiter}** as the list delimiter

work page

[10] [10]

entity",

When finished, output {completion_delimiter} ############ -Examples- ############ {examples} ############ -Real Data- ############ Entity_types: {entity_types} Primary Image Document text content: {input_text} Additional Layout Detection Images: (The images are provided by appending them directly after this prompt, with the primary image document as the f...

work page 2031