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Paper Citation Record · LEDGER

PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2404.12720.

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

pith.paper-citation-record.v1
2404.12720 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:40:11.059123Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:13:15.042714Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2ada8776-87d8-45c9-8cb3-af5a019af1f4 · inbound

Enhancing Document Key Information Localization Through Data Augmentation cites this paper.

Enhancing Document Key Information Localization Through Data Augmentation PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-08T16:40:11.059123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:40:11.059123Z digest=sha256:320fe7f263516af2dec734c12b549413ba14296e66912e1760e585671c69243f

Observation 11227b6e-6f49-4fa6-9d1c-ed6ce3a5b060 · inbound

VRD-IU: Lessons from Visually Rich Document Intelligence and Understanding cites this paper.

VRD-IU: Lessons from Visually Rich Document Intelligence and Understanding PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T11:49:08.346682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:49:08.346682Z digest=sha256:0f7e35afdbc874cd90696876185df2249682671de96f4925599baa1fef487cd6

Observation 0f233e32-b037-4401-a55e-61e8655d5b7d · inbound

Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning cites this paper.

Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-04T20:28:56.764648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T20:28:56.764648Z digest=sha256:098b92de29327d4dcce3269c7d34e5543abe3a1ed06dffe1d2baaa5d0ea7ea3e

Observation 8ea5a4ee-9f61-46ee-ad81-fdafa8b38b60 · inbound

DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding cites this paper.

DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:05:58.491401Z

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.

source=pdf_text observed=2026-05-10T16:16:58.889065Z digest=sha256:b5b4408ef7391c9660b17eea0a1e9051ee4bdd569435c651fb370502316f3db8

Observation 283ec2f1-5f71-4a8a-9021-a12dce7a11ae · inbound

DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding cites this paper.

DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:26:24.407871Z

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.

source=pdf_text observed=2026-05-12T04:17:55.318813Z digest=sha256:e17c6c39f4bafb549c1100921822bfda5aaaf0dfecd6ec571738a91fe34eda84

Observation 078c664f-980d-4fb2-9d3d-39ca10e0cd31 · inbound

Lightweight and Production-Ready PDF Visual Element Parsing cites this paper.

Lightweight and Production-Ready PDF Visual Element Parsing PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:36:09.148986Z

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.

source=pdf_text observed=2026-05-08T08:31:44.430067Z digest=sha256:d648e2572b31707531a36a84f1ca81d398402c4614a236ced6398f6ac4473776

Observation 3339602c-1ccd-484f-8fed-3b001e845583 · inbound

DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark cites this paper.

DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-06-29T08:13:15.044494Z

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.

source=pdf_text observed=2026-06-29T08:09:41.068000Z digest=sha256:6eb0eccc15a1fe3823419963782c71c683bfd35fb7e325628922093a2bb5c103

Observation 7a4c4363-aebd-46d1-9fa1-a2571c95fa34 · inbound

DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth cites this paper.

DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering

Reference 121

Resolution
unresolved
no resolver link, observed 2026-08-02T14:55:34.545662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T14:55:34.545662Z digest=sha256:4e4fda9ac0a780985da1199786ee20e9597dfecc179e6a00f1d8e4277eff58f6