Paper Citation Record · LEDGER

Machine Learning for Synthetic Data Generation: A Review

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2302.04062.

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

pith.paper-citation-record.v1
2302.04062 v10

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:12:59.438835Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T21:10:09.196610Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ec388cf4-cbfd-4fe1-b7c7-49b5612013a6 · inbound

Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms cites this paper.

Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms Machine Learning for Synthetic Data Generation: A Review

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:39.006264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dce145b7-97b0-4f72-aa4d-191cc7b96529 · inbound

HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions cites this paper.

HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions Machine Learning for Synthetic Data Generation: A Review

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:44:55.181185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ee7205d6-58fd-441b-a8e8-f358d29fc4f0 · inbound

Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques cites this paper.

Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques Machine Learning for Synthetic Data Generation: A Review

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:59.438835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3853221-a222-40f5-9d59-2c3695e6a23d · inbound

Stochastic dynamics learning with state-space systems cites this paper.

Stochastic dynamics learning with state-space systems Machine Learning for Synthetic Data Generation: A Review

Reference 58

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T00:01:56.003520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f725b00f-2176-4e01-91fc-fc1934f3e1a8 · inbound

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data cites this paper.

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data Machine Learning for Synthetic Data Generation: A Review

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T18:08:41.580224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 985f82e5-d619-4f65-ac88-92be84a6c744 · inbound

Synthetic CVs To Build and Test Fairness-Aware Hiring Tools cites this paper.

Synthetic CVs To Build and Test Fairness-Aware Hiring Tools Machine Learning for Synthetic Data Generation: A Review

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T14:35:15.858186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2d4e1bf-8f84-4f67-9072-44d4d2eaa492 · inbound

FUTURE: Flexible Unlearning for Tree Ensemble cites this paper.

FUTURE: Flexible Unlearning for Tree Ensemble Machine Learning for Synthetic Data Generation: A Review

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T14:35:42.717782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c663f409-9c75-40a6-9ebe-d81dc7d90472 · inbound

Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records cites this paper.

Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records Machine Learning for Synthetic Data Generation: A Review

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T18:10:01.541823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7b0a065-c66e-4a18-b282-f353859eb73d · inbound

Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training cites this paper.

Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training Machine Learning for Synthetic Data Generation: A Review

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:03.821535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 589cc310-66c6-4859-98df-b0640ed5d05c · inbound

Generative AI-Based Monte Carlo Simulation for Method Evaluation Using Synthetic Multilevel Data cites this paper.

Generative AI-Based Monte Carlo Simulation for Method Evaluation Using Synthetic Multilevel Data Machine Learning for Synthetic Data Generation: A Review

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:46:10.767225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be5f8cac-5ae8-4920-a4bd-cf821ab149be · inbound

Fundamental Trade-Offs in Multi-Bit Watermarking of Stochastic Processes cites this paper.

Fundamental Trade-Offs in Multi-Bit Watermarking of Stochastic Processes Machine Learning for Synthetic Data Generation: A Review

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:56:25.662949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8d130a63-fc49-477e-bb43-63e1a562f1c0 · inbound

Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs cites this paper.

Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs Machine Learning for Synthetic Data Generation: A Review

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T22:32:49.697954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3fbdd2b-f549-4444-ba8c-bd52fa68adc5 · inbound

CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution cites this paper.

CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution Machine Learning for Synthetic Data Generation: A Review

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:08:15.420893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3fe146d1-6ebf-4d5e-9378-0ca66263b84a · inbound

What Makes Synthetic Data Effective in Image Segmentation cites this paper.

What Makes Synthetic Data Effective in Image Segmentation Machine Learning for Synthetic Data Generation: A Review

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T07:13:06.690633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d7e5412-f147-4f4e-b1c2-8a513ac108ad · inbound

When Does Model Collapse Occur in Structured Interactive Learning? cites this paper.

When Does Model Collapse Occur in Structured Interactive Learning? Machine Learning for Synthetic Data Generation: A Review

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T06:33:05.626109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6ac15c3c-05fb-4445-80ac-8d526b8cdabd · inbound

SADGE: Structure and Appearance Domain Gap Estimation of Synthetic and Real Data cites this paper.

SADGE: Structure and Appearance Domain Gap Estimation of Synthetic and Real Data Machine Learning for Synthetic Data Generation: A Review

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T06:54:42.090413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a5db162c-ad28-4944-ad72-fc2dba3b0cbe · inbound

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? cites this paper.

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? Machine Learning for Synthetic Data Generation: A Review

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T21:10:09.198645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.