Paper Citation Record · LEDGER

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models

As of 14 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2502.10250.

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

pith.paper-citation-record.v1
2502.10250 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T18:51:10.479437Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

14 of 14 outbound references displayed

verified exact1
verified fuzzy2
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f54d4585-f05a-4c98-b478-db00235b1ff4 · outbound

This paper cites Mistral 7B.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models Mistral 7B

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.396754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37a019c0-ccec-4600-a883-838bbd380c5f · outbound

This paper cites AnglE-optimized Text Embeddings.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models AnglE-optimized Text Embeddings

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.422113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbe8465b-666e-4474-959d-90bd695456de · outbound

This paper cites Prismer: A Vision-Language Model with Multi-Task Experts.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models Prismer: A Vision-Language Model with Multi-Task Experts

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.428909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b04e03f2-cdb9-478c-abd7-829f9ec0989f · outbound

This paper cites FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T18:51:10.631920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5d72e60f-2d8a-4101-8f88-827bb158a8cc · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.445511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b7d06a7-4eb2-4aba-9c9f-11f48fbf400a · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.452158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b49e3b7c-8e44-478c-97c4-d55bc2c8562b · outbound

This paper cites OpenChat: Advancing Open-source Language Models with Mixed-Quality Data.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models OpenChat: Advancing Open-source Language Models with Mixed-Quality Data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.458835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d3b72f3-9ac3-47dc-971f-7913d16d98b5 · outbound

This paper cites Meta-Transformer: A Unified Framework for Multimodal Learning.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models Meta-Transformer: A Unified Framework for Multimodal Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.466981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b7debf4-1f14-4b51-964a-345353c0c1a1 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.473670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c7a2227-17d8-4acd-839b-273b0c428175 · outbound

This paper cites (a) Distribution of Number of Tokens in the Source Context.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models (a) Distribution of Number of Tokens in the Source Context

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:51:10.771807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f007f6f7-fc3f-4d62-8d85-6c88dc5bb315 · outbound

This paper cites What matters when building vision-language models?.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models What matters when building vision-language models?

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.414354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f21da683-72c6-467f-b430-15aeeb20059e · outbound

This paper cites ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.381594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1df0bbb-20d8-44a9-8860-606321539e65 · outbound

This paper cites A diagram is worth a dozen images.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models A diagram is worth a dozen images

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:51:10.791389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 087f2118-5123-4cfa-a18c-9d377f22aa4b · outbound

This paper cites ShareGPT4V: Improving Large Multi-Modal Models with Better Captions.

VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T18:51:10.388827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.