Paper Citation Record · LEDGER

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions

As of 10 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2502.04389.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.04389 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T04:09:13.435547Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact3
verified fuzzy6
unresolved23
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69974e3e-79c9-463f-91b3-905e1340c4a1 · outbound

This paper cites write newline.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:12.794091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 259ff82d-5221-4dc5-95be-99ba4072062f · outbound

This paper cites an unresolved cited work.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-09T04:09:14.283426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f04d3b80-dc59-46f7-b6fa-5b3e6379b48d · outbound

This paper cites PaLM 2 Technical Report.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions PaLM 2 Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:12.850665Z

Source-reported events for the cited work

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Observation 01f8e264-b9a8-46d0-8995-dd93f79f24a2 · outbound

This paper cites Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-09T04:09:13.909720Z

Source-reported events for the cited work

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Observation 373299ba-c648-48fb-b2d2-8624e27acd24 · outbound

This paper cites How far are we to GPT - 4V ? Closing the gap to commercial multimodal models with open-source suites.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions How far are we to GPT - 4V ? Closing the gap to commercial multimodal models with open-source suites

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:12.859116Z

Source-reported events for the cited work

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Observation a539b88b-8b20-47aa-b76c-801293357724 · outbound

This paper cites RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:12.862671Z

Source-reported events for the cited work

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Observation 54c39ac3-b6c8-47a8-8297-8a833e4bf144 · outbound

This paper cites VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:12.871790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bcc6c06-589e-4811-b439-600c4ccc9304 · outbound

This paper cites Do Vision-Language Models Really Understand Visual Language?.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Do Vision-Language Models Really Understand Visual Language?

Reference 8

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Source-reported events for the cited work

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Observation bd4695d3-f338-45ad-9755-b8c3ef070c0c · outbound

This paper cites Introducing Gemini 2.0: our new AI model for the agentic era, December 2024.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Introducing Gemini 2.0: our new AI model for the agentic era, December 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:09:14.229040Z

Source-reported events for the cited work

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Observation 576538dc-7b39-4c28-867a-49080da2e95a · outbound

This paper cites Layoutlmv3: Pre-training for document ai with unified text and image masking.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Layoutlmv3: Pre-training for document ai with unified text and image masking

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:09:14.218568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8a2effa-a012-434d-9ae5-c944cac9172d · outbound

This paper cites ISO / IEC 29500-1:2016, 2016.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions ISO / IEC 29500-1:2016, 2016

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:09:14.208441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 99452ea1-9ee4-4289-9077-fe367dc07d56 · outbound

This paper cites VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:12.982888Z

Source-reported events for the cited work

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Observation 8f7b4706-7df6-47ec-8b9a-afe353b5659c · outbound

This paper cites ( Security ) Assertions by Large Language Models.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions ( Security ) Assertions by Large Language Models

Reference 13

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Source-reported events for the cited work

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Observation d15e50ef-e078-4125-a7c7-98b07b7d8955 · outbound

This paper cites Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.017075Z

Source-reported events for the cited work

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Observation d002fb36-271c-4e91-b220-b6bdc1313401 · outbound

This paper cites Ferret-ui 2: Mastering universal user interface understanding across platforms, 2024.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Ferret-ui 2: Mastering universal user interface understanding across platforms, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:09:14.100640Z

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Observation 5343c688-3fed-4cae-a254-edb358d51da1 · outbound

This paper cites an unresolved cited work.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.141322Z

Source-reported events for the cited work

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Observation c46a944f-5cdb-4f8a-b1f6-0d92fa01b048 · outbound

This paper cites MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.144212Z

Source-reported events for the cited work

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Observation 04d38526-95cd-4a30-8825-f859a2f9c39e · outbound

This paper cites Unraveling the Truth : Do VLMs really Understand Charts ? A Deep Dive into Consistency and Robustness.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Unraveling the Truth : Do VLMs really Understand Charts ? A Deep Dive into Consistency and Robustness

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.148319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e9a610d-cec7-4780-a594-25a95d3e5b22 · outbound

This paper cites GPT-4 Technical Report.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions GPT-4 Technical Report

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.152055Z

Source-reported events for the cited work

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Observation 708da264-625b-477a-b10e-33fe10f5a56d · outbound

This paper cites GPT-4o System Card.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions GPT-4o System Card

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.155143Z

Source-reported events for the cited work

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Observation 0d61a7b8-b479-4174-a363-d73d9d86409d · outbound

This paper cites FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.158068Z

Source-reported events for the cited work

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Observation 85364f4f-4887-461d-a6f9-59f4f65d3781 · outbound

This paper cites Vision language models are blind: Failing to translate detailed visual features into words.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Vision language models are blind: Failing to translate detailed visual features into words

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.161806Z

Source-reported events for the cited work

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Observation 18df3a35-225f-4cf7-8ccb-bd1af4aa9f08 · outbound

This paper cites FlowVQA : Mapping Multimodal Logic in Visual Question Answering with Flowcharts.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions FlowVQA : Mapping Multimodal Logic in Visual Question Answering with Flowcharts

Reference 23

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Source-reported events for the cited work

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Observation eca1d1fa-eee1-425b-afd0-a23070db800e · outbound

This paper cites LLM4VV: Exploring LLM-as-a-Judge for Validation and Verification Testsuites.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions LLM4VV: Exploring LLM-as-a-Judge for Validation and Verification Testsuites

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-09T04:09:13.549466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 631d5d0f-34fc-46de-8529-24b46f3513f4 · outbound

This paper cites HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.286082Z

Source-reported events for the cited work

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Observation e2f8e72e-f6ad-474f-87c8-239ff489b7a6 · outbound

This paper cites Feighelstein, Jasmina Bogojeska, Joseph Shtok, Assaf Arbelle, Peter W.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Feighelstein, Jasmina Bogojeska, Joseph Shtok, Assaf Arbelle, Peter W

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:09:14.064080Z

Source-reported events for the cited work

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Observation 7f6c368e-81c1-41ef-b40a-3f78581f2390 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions LaMDA: Language Models for Dialog Applications

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.292812Z

Source-reported events for the cited work

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Observation b14e2e0e-8f85-40ef-a7cf-0fef0c888f50 · outbound

This paper cites LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.295917Z

Source-reported events for the cited work

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Observation 7206a5bf-b538-4c66-aee6-8b6a5350f007 · outbound

This paper cites Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding

Reference 29

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Observation 0b8e8ba3-4849-42b3-817a-bc022ce0aa3b · outbound

This paper cites Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.386647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f27af81-5143-40f1-bea6-3a9c2504b3b9 · outbound

This paper cites MMMU : A Massive Multi -discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions MMMU : A Massive Multi -discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:09:14.054889Z

Source-reported events for the cited work

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Observation bfe125d8-adb9-4275-87c1-bfc3a8d37400 · outbound

This paper cites MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?.

Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T04:09:13.435547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.