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Paper Citation Record · LEDGER

How to Train Your Long-Context Visual Document Model

As of 12 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 3 inbound Pith citation observations for arXiv:2602.15257.

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

pith.paper-citation-record.v1
2602.15257 v3

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T22:58:04.553384Z

measured 9 of 9 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T15:50:49.083652Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T19:17:50.049016Z

Reference resolution

6 of 6 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved5
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

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

This paper cites SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images.

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.

source=pdf_text observed=2026-08-02T22:58:04.328430Z digest=sha256:616e67fb1361f6bf7fde0b4795951fbcac1dd718dca2a5d09b523a79f48525f5

Observation 93a4bbfd-a844-42da-b086-912a7694b133 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

How to Train Your Long-Context Visual Document Model Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-02T22:58:04.409237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:58:04.409237Z digest=sha256:e6d7065fd83d76e84990a09af2c5aaddd325de2554d3ca1e9c54eb14560da753

Observation ad54d47a-3bd4-46dc-a4c4-a49cb740db81 · outbound

This paper cites Editing Models with Task Arithmetic.

How to Train Your Long-Context Visual Document Model Editing Models with Task Arithmetic

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-02T22:58:04.249392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:58:04.249392Z digest=sha256:c5a0ece619ffca0698952b0a790f331acc828fb45e3b51330cf4ea4b60457963

Observation 18795420-2721-4ef8-8a1a-577569fae330 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

How to Train Your Long-Context Visual Document Model Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-02T22:58:04.107771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:58:04.107771Z digest=sha256:a036a309246edb9301758d9ffe543fdca24cb8394a2596796b96d3355cb25215

Observation 0f3ef289-c144-45d9-9ef6-38394afd707a · outbound

This paper cites LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization.

How to Train Your Long-Context Visual Document Model LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-02T22:58:04.044792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:58:04.044792Z digest=sha256:681505f2d7b03842d693e035740ecdc611e298c9e3ff6fbda81365b33d904583

Observation eca98806-d961-4cea-a643-2e197af8a78d · outbound

This paper cites least”→“lease.

How to Train Your Long-Context Visual Document Model least”→“lease

Reference 2026

Resolution
malformed identifier
no resolver link, observed 2026-08-02T22:58:04.553384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:58:04.553384Z digest=sha256:7cf8246e3bb074f4ce462e0d1e635eaf8addf382fa25301cd3827f005fc545bb

Pith citing papers

Observation 249e529e-a8c6-4f70-b6f1-b0e400f75a6d · inbound

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

Internalized Reasoning for Long-Context Visual Document Understanding How to Train Your Long-Context Visual Document Model

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-06-30T03:18:06.246234Z

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.

source=pdf_text observed=2026-05-13T23:53:19.148407Z digest=sha256:64c04157d1b06021b9dc4117a027a218e21919976ee892a5f57d536620332f6f

Observation adff9365-210b-49a2-b7f8-abc9ca3f284a · inbound

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

Internalized Reasoning for Long-Context Visual Document Understanding How to Train Your Long-Context Visual Document Model

Reference 45

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T15:50:49.083652Z digest=sha256:1615e0f6460cd0aacde2e2e26338eac91e7e3701ccf3cf41a15758c753a1b8d6

Observation 960230b5-809e-42c7-844b-275b9fd08b80 · inbound

Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context cites this paper.

Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context How to Train Your Long-Context Visual Document Model

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-06-30T03:18:06.246234Z

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.

source=pdf_text observed=2026-05-14T19:16:07.851098Z digest=sha256:7a66308cff24141365c2c4634c04950591e9da80ab04d8d81e976ae6735148ce