Paper Citation Record · LEDGER

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts

As of 17 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 5 inbound Pith citation observations for arXiv:2412.03590.

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

pith.paper-citation-record.v1
2412.03590 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:57:21.675143Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:55:55.384566Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T23:49:32.709426Z

Reference resolution

21 of 21 outbound references displayed

verified exact5
verified fuzzy12
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1478f6e7-22b5-4aab-a740-3ccb5b8178f6 · outbound

This paper cites Evaluate generalisation & robustness of visual features from images to video,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Evaluate generalisation & robustness of visual features from images to video,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:22.054767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dbdde3aa-c134-4370-a71b-db61772f08de · outbound

This paper cites Synthetic document generation pipeline for training artificial intelligence models,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Synthetic document generation pipeline for training artificial intelligence models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:22.043889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 03d4846f-da9c-4857-9b83-55c5f6612190 · outbound

This paper cites Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:21.602753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4386b89-767e-4f4d-b045-e728b281d2cf · outbound

This paper cites A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:22.031694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c20a9f74-3770-490f-9442-cd4ed87c09f6 · outbound

This paper cites Neural Message Passing for Quantum Chemistry.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Neural Message Passing for Quantum Chemistry

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:21.612816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8f0303c-c361-46a8-ad1b-7deb10894331 · outbound

This paper cites Continuous Spiking Graph Neural Networks.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Continuous Spiking Graph Neural Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:21.617273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 921bc651-1dfd-4b67-b3b7-e7920df0b1b0 · outbound

This paper cites Document layout analysis with variational autoencoders: An industrial application,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Document layout analysis with variational autoencoders: An industrial application,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:22.020040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4ad9a4ab-4b57-4b0d-a435-ad7f218df3b6 · outbound

This paper cites Using gans for domain adaptive high resolution synthetic document generation,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Using gans for domain adaptive high resolution synthetic document generation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:22.008223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b7a3238c-a9ea-42c9-a37e-87ed9d79e6ff · outbound

This paper cites Techniques for graph data structure augmentation,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Techniques for graph data structure augmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:21.996868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6330ea58-f82a-4a68-952f-1b33dc674e71 · outbound

This paper cites A comprehensive survey on graph neural networks,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts A comprehensive survey on graph neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:21.983862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 39bb3e53-5637-4e90-8296-479d64e802ed · outbound

This paper cites Pseudo labelling for key-value extraction from documents,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Pseudo labelling for key-value extraction from documents,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:21.970297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76741bee-1767-4c5a-80c0-5fec12f080d9 · outbound

This paper cites An Augmentation Strategy for Visually Rich Documents.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts An Augmentation Strategy for Visually Rich Documents

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:57:21.870533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 283f84d3-28b5-4520-ad41-4389df1c8579 · outbound

This paper cites Graph-based Deep Generative Modelling for Document Layout Generation.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Graph-based Deep Generative Modelling for Document Layout Generation

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:57:21.854022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b3979439-d379-48b1-b576-c735ac4dc322 · outbound

This paper cites Domain adapting graph networks for visually rich documents,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Domain adapting graph networks for visually rich documents,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:21.957687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 08f0a6f4-12a5-4db0-bc4e-79316cb97f8d · outbound

This paper cites Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:57:21.837497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fe8c99ad-ca90-4e76-bfc9-fb99f7248e8a · outbound

This paper cites Evalu ation of deep convolutional nets for document image classification and retrieval,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Evalu ation of deep convolutional nets for document image classification and retrieval,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:21.944637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 339f422f-6562-4b86-8aa7-3cc6bdc8255e · outbound

This paper cites Funsd: A dataset for form understanding in noisy scanned documents,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Funsd: A dataset for form understanding in noisy scanned documents,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:21.932134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6d89a0c0-ff86-4d1b-9302-19ae13e9bf32 · outbound

This paper cites Icdar2019 competition on scanned receipt ocr and information extraction,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Icdar2019 competition on scanned receipt ocr and information extraction,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:21.663900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae63ff43-e0c7-44bc-9245-27c8cbc623bf · outbound

This paper cites Contrastive document representation learning with graph attention networks,.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Contrastive document representation learning with graph attention networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:21.920055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 53885586-a7ef-4dd7-82cd-5a3729258ca7 · outbound

This paper cites Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:57:21.731194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7422b9bd-b054-4c26-b22d-cc7799059e2c · outbound

This paper cites Graph Domain Adaptation: A Generative View.

Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts Graph Domain Adaptation: A Generative View

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:57:21.714524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation a087b0a7-3ec9-417c-b6b4-960f7bc59772 · inbound

Tokenization Matters: Improving Zero-Shot NER for Indic Languages cites this paper.

Tokenization Matters: Improving Zero-Shot NER for Indic Languages Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:55:55.384566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ee05890-3ee7-49f1-ac40-767856edd6ea · inbound

SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use cites this paper.

SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:27.217122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32327ea2-5002-46f5-af25-c767c4c7a7e1 · inbound

Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems cites this paper.

Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:09.809386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90c43477-57c2-4a16-a763-f7e47e465691 · inbound

Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation cites this paper.

Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:15.145388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dee9b8c4-157d-4e70-894b-3fc0c29b6624 · inbound

MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS cites this paper.

MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-04T23:49:32.788744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.