Paper Citation Record · LEDGER

REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2302.02041.

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

pith.paper-citation-record.v1
2302.02041 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:11.625165Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:58:57.964977Z

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 72a45e4e-a941-4cf3-b96e-460944881f3a · inbound

LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion cites this paper.

LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:07:19.859271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7fa95727-d3b7-4aa5-84a1-0c7fbae56949 · inbound

The Prompt is Mightier than the Example cites this paper.

The Prompt is Mightier than the Example REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:11.625165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 911218f8-03a2-4b09-ba3b-1be020b30dc7 · inbound

IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples) cites this paper.

IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples) REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:44.943130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 206bc91a-e23d-40df-af02-88c23437ebc8 · inbound

Making Logic a First-Class Citizen in Generative ML for Networking cites this paper.

Making Logic a First-Class Citizen in Generative ML for Networking REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T07:27:09.084770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88ebb6d4-aebf-4675-8190-e7e3cdbfe54a · inbound

Taming Data Challenges in ML-based Security Tasks Using Generative AI cites this paper.

Taming Data Challenges in ML-based Security Tasks Using Generative AI REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T19:16:28.677741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaa13ae3-bc3f-48d4-8204-25c1169af31c · inbound

Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering cites this paper.

Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T17:43:53.876143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad9b4e5e-7876-43ee-a905-c53c66167383 · inbound

ASPEN: An Additional Sampling Penalty Method for Finite-Sum Optimization Problems with Nonlinear Equality Constraints cites this paper.

ASPEN: An Additional Sampling Penalty Method for Finite-Sum Optimization Problems with Nonlinear Equality Constraints REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T05:04:11.097178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 285b9980-f71b-4d74-b9f3-a07b0a3ba362 · inbound

Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN cites this paper.

Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T22:42:45.291442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4497750-2e09-4e48-8f99-48bd10e91772 · inbound

Cross-Flow Correlations Survive Synthesis: Measuring Source-Level Privacy Leakage in Synthetic Network Traces cites this paper.

Cross-Flow Correlations Survive Synthesis: Measuring Source-Level Privacy Leakage in Synthetic Network Traces REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:26:53.222410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 586aa1b6-13ea-41e5-8816-e862f34d5c5e · inbound

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data cites this paper.

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 202

Resolution: unresolved; no resolver link, observed 2026-08-03T08:15:31.311397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e3fec6c-2861-469c-8e9f-70a3725f90b4 · inbound

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting cites this paper.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:30:41.457030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 46bdeebd-95e7-4c3c-8046-7097d19ab1a9 · inbound

Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration cites this paper.

Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T07:01:49.535482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 898f57a3-a19b-4264-8b37-b6cd5f10c324 · inbound

The Power of Order: Fooling LLMs with Adversarial Table Permutations cites this paper.

The Power of Order: Fooling LLMs with Adversarial Table Permutations REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:36:06.416286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 31b40da4-718e-4659-a2e7-a64310508b96 · inbound

The Power of Order: Fooling LLMs with Adversarial Table Permutations cites this paper.

The Power of Order: Fooling LLMs with Adversarial Table Permutations REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:26:16.635394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9ec5afdc-97d4-4574-98c4-5b8ab95c504c · inbound

Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.

Concordia: Self-Improving Synthetic Tables for Federated LLMs REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:16:27.655800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 92a49cec-a51e-4e6b-a8c3-50288f287f8f · inbound

Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.

Concordia: Self-Improving Synthetic Tables for Federated LLMs REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:23:47.871200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2a4d543f-d39c-4561-b5ed-3cc58fb03974 · inbound

HH-SAE: Discovering and Steering Hierarchical Knowledge of Complex Manifolds cites this paper.

HH-SAE: Discovering and Steering Hierarchical Knowledge of Complex Manifolds REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:19.148931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 990e4c51-ff6d-45da-97bb-9b5c0daff371 · inbound

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data cites this paper.

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T10:07:56.190679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d0f88e06-5722-4472-997f-7846a3dc2bfe · inbound

PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization cites this paper.

PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:58:57.966447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2ff8e18f-cff3-4bfc-b0a6-f738ba2720c5 · inbound

Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval cites this paper.

Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:14:18.979246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7bb2d918-8527-4bb5-b01c-27c99f4ee5c9 · inbound

Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss cites this paper.

Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-01T06:35:29.328395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4c4e0344-17ca-4305-bffd-f3a2f35509ed · inbound

TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data cites this paper.

TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-11T22:59:07.646605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36289d85-d75c-4cc3-9ca9-a3a3902b0672 · inbound

Do Generative Models Keep Time? A Time-Aware Evaluation of Synthetic Sequential Tabular Data cites this paper.

Do Generative Models Keep Time? A Time-Aware Evaluation of Synthetic Sequential Tabular Data REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T22:52:40.995209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5052276a-9d74-4103-9d40-e170bdba5b0b · inbound

TopoFE: topology-aware LLM-guided Automated Feature Engineering cites this paper.

TopoFE: topology-aware LLM-guided Automated Feature Engineering REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 90

Resolution: unresolved; no resolver link, observed 2026-07-31T23:58:35.470651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92701c0a-02a5-4b08-aa09-44a6f9989047 · inbound

LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation cites this paper.

LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T19:14:36.375103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.