Paper Citation Record · LEDGER

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations

As of 14 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2501.04675.

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

pith.paper-citation-record.v1
2501.04675 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:30:50.269302Z

measured 23 of 23 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-07T13:43:30.511187Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T08:51:23.968119Z

Reference resolution

22 of 22 outbound references displayed

verified exact1
verified fuzzy8
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cc8690f2-ac2b-4c1a-a9c9-54e16d2953be · outbound

This paper cites DePlot: One-shot visual language reasoning by plot-to-table translation.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations DePlot: One-shot visual language reasoning by plot-to-table translation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.137954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c065acc8-9347-4b1d-91de-a03a54e68b49 · outbound

This paper cites Language models are few-shot learners,.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Language models are few-shot learners,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.144427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cabc3fe-8bdc-4867-a1a7-fb08ecacc421 · outbound

This paper cites ChartQA: A benchmark for question answering about charts with visual and logical reasoning,.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations ChartQA: A benchmark for question answering about charts with visual and logical reasoning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.769783Z

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 ed2c8629-63cb-4eb8-99fa-719f34fd5a59 · outbound

This paper cites Chartocr: Data extraction from charts images via a deep hybrid framework,.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Chartocr: Data extraction from charts images via a deep hybrid framework,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.750520Z

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 1f04fdea-0a22-45d2-8f09-dc1056214568 · outbound

This paper cites Figureseer: Parsing result-figures in research papers,.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Figureseer: Parsing result-figures in research papers,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.732270Z

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 31dd110e-10a2-4816-8b41-b976d911a9cd · outbound

This paper cites MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.176774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a97f4161-3543-47fd-b634-f125db8ec85d · outbound

This paper cites Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.183976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e4e07ce-9a85-40cd-8800-549027aac684 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.190566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d72d906a-735f-404f-b5c8-511c5ec8be8a · outbound

This paper cites From Data Quality to Model Quality: an Exploratory Study on Deep Learning.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations From Data Quality to Model Quality: an Exploratory Study on Deep Learning

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:30:50.484052Z

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 1dd429fd-4f64-4059-8f7d-32a4f78c4977 · outbound

This paper cites The Effects of Data Quality on Machine Learning Performance on Tabular Data.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations The Effects of Data Quality on Machine Learning Performance on Tabular Data

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.203758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31036630-9623-4890-adf0-5163f1ba81eb · outbound

This paper cites Matplotlib: A 2d graphics environment,.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Matplotlib: A 2d graphics environment,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.210416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7769dce-25e2-40ba-a27e-7164297e5b0d · outbound

This paper cites seaborn: statistical data visualization,.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations seaborn: statistical data visualization,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.698321Z

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 3262c74d-2ae0-4cfc-901c-20b2cb7b9a05 · outbound

This paper cites Icdar 2019 competition on scene text visual question answering,.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Icdar 2019 competition on scene text visual question answering,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.679744Z

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 52233d0a-fc1d-4624-bafc-5c65cb234b5b · outbound

This paper cites Enhancing large vision language models with self-training on image comprehension,.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Enhancing large vision language models with self-training on image comprehension,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.662218Z

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 938b0371-4e26-4bdd-a144-bd1136ac483e · outbound

This paper cites Fine-tuning Smaller Language Models for Question Answering over Financial Documents.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Fine-tuning Smaller Language Models for Question Answering over Financial Documents

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.234543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f55ea03d-7b43-4398-9fc3-be2cc900ec57 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations LLaMA: Open and Efficient Foundation Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.240903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1856b9db-2a53-4c4f-bdc9-144f50168c51 · outbound

This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.246368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc435a35-b579-454f-ae40-23e05aca991c · outbound

This paper cites Pixtral 12B.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Pixtral 12B

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.252461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6196ccf5-b5b3-4cd2-8c06-24c9e8ffa41c · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.258087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2aef1462-7321-45d8-b103-96c755c070fe · outbound

This paper cites You are a helpful assistant. Help me with my math homework!.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations You are a helpful assistant. Help me with my math homework!

Reference 150

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.622858Z

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 aa605e70-1963-45d1-9af4-87e472b36bc0 · outbound

This paper cites The difference in value between Reserves and Cash is: -30 - (-20) = -10 Therefore, the difference in Value between Reserves and Cash is -10.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations The difference in value between Reserves and Cash is: -30 - (-20) = -10 Therefore, the difference in Value between Reserves and Cash is -10

Reference 800

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.642916Z

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 b2704af8-aea8-4c37-9f35-450423c5d384 · outbound

This paper cites Language Models are Few-Shot Learners.

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Language Models are Few-Shot Learners

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.150645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 44f8cb2f-8c35-4433-9921-7b254445a636 · inbound

A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflows cites this paper.

A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflows Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:51:23.970512Z

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.