Paper Citation Record · LEDGER

SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2301.04883.

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

pith.paper-citation-record.v1
2301.04883 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:17:27.994465Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:36:29.833518Z

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 cdb31871-084f-40e8-b7a7-650e2162565d · inbound

Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends cites this paper.

Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-10T22:17:27.994465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1caa3165-a8c6-42da-ab86-2a846ff7461f · inbound

How to Train Your Long-Context Visual Document Model cites this paper.

How to Train Your Long-Context Visual Document Model SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T22:58:04.328430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06747e7e-9dd2-4488-bf85-c1275c9a2381 · inbound

Internalized Reasoning for Long-Context Visual Document Understanding cites this paper.

Internalized Reasoning for Long-Context Visual Document Understanding SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-13T23:53:27.812936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c4ec3d05-0e5d-4afe-aac8-5c68daba142a · inbound

Internalized Reasoning for Long-Context Visual Document Understanding cites this paper.

Internalized Reasoning for Long-Context Visual Document Understanding SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-13T15:50:49.083652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12292e19-047f-4aa3-a6d0-23c720587d1e · inbound

SMMBench: A Benchmark for Source-Distributed Multimodal Agent Memory cites this paper.

SMMBench: A Benchmark for Source-Distributed Multimodal Agent Memory SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:13:40.538259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8cfc62a8-debc-473e-ba9a-dfed0f0b9486 · inbound

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A cites this paper.

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:36:29.834747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.