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Paper Citation Record · LEDGER

How to Train Your Long-Context Visual Document Model

As of 21 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 2026-08-20T06:33:59.587034+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=sha256:95218524a9d330fe9c9e4f22b4ac3f8108d3c021ec2cda301674f6a7ae657b76

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=sha256:3dffdcfe307d5ce0c0c20d1aeac178865fd7c35b599d27834a64a570e15fa7a4

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=sha256:133219c04c0bed6e6459c25d8b234299d84e6652cff181654a728bd1e68c873e

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=sha256:7b99a29becbda066c25206641f956ff479b1c5dbc3d574b907756621800f526f

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:a408c2d3bea3ff146a93b79b808119ce8a18189e4183d256fdb7460f14f946ef

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:f2ce202895e21171a72badadd268bf7b4f2bf4521e6b0287a2fbb86e243c3c42

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-13T23:53:19.148407Z digest=sha256:ea97382bc7449ece6598447f76d2fdd6be6e7734464cb56cd4e10e2c29f0aa45

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=sha256:3aab21f078db4323db1197d57c9d93b0b81f0c96d6b07c5e1ca4ee633c1303ef

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-14T19:16:07.851098Z digest=sha256:425fe790c4f5ed4ac8eb2f588ab7649f026cd330c1303ddbab797fefba56bea6