Paper Citation Record · LEDGER

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

As of 17 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 7 inbound Pith citation observations for arXiv:2501.12012.

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

pith.paper-citation-record.v1
2501.12012 v2

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:41:47.750518Z

measured 78 of 78 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:49:59.127349Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T18:16:27.091186Z

Reference resolution

71 of 71 outbound references displayed

verified exact2
verified fuzzy23
unresolved43
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57976818-1898-4c8b-b302-89433538e4d9 · outbound

This paper cites write newline.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data write newline

Reference 1

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Observation 1c9045ab-2f56-44ff-a715-c7e900bfb808 · outbound

This paper cites B., Mironov, I., Talwar, K., and Zhang, L.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data B., Mironov, I., Talwar, K., and Zhang, L

Reference 2

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Observation 01ecc190-ed32-4c78-bce6-d858db38c024 · outbound

This paper cites A., Li, J., Ayd \"o re, S., and Leahy, R.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data A., Li, J., Ayd \"o re, S., and Leahy, R

Reference 3

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Observation f03a1993-6149-4a41-8c96-3fa94c904a25 · outbound

This paper cites How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

Reference 4

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Observation 154c8dec-cdc1-4758-8d82-2e13336b8e93 · outbound

This paper cites and Panda, A.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data and Panda, A

Reference 5

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Source-reported events for the cited work

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Observation e75c979f-c127-49cd-ab91-39ab2821950a · outbound

This paper cites Pkdd'99 financial dataset, 1999.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Pkdd'99 financial dataset, 1999

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:49.113947Z

Source-reported events for the cited work

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Observation 4d44e84a-dde1-40ed-822d-362cb9e9043f · outbound

This paper cites Language models are realistic tabular data generators.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Language models are realistic tabular data generators

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:49.098818Z

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Observation f595b006-3254-46de-8c93-816aad268351 · outbound

This paper cites Language Models are Few-Shot Learners.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Language Models are Few-Shot Learners

Reference 8

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Observation a4f09d17-112e-4a53-b93b-39fc302c7671 · outbound

This paper cites DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation

Reference 9

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Observation 66167e09-28c9-455e-9b2c-e4575a27b6cc · outbound

This paper cites TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Reference 10

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Observation 5cf1ee7c-36b3-4a03-b734-feb4f895322f · outbound

This paper cites Retiring adult: New datasets for fair machine learning.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Retiring adult: New datasets for fair machine learning

Reference 11

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Observation 0f3b1fc1-eb08-41b2-91b7-42637576f8ba · outbound

This paper cites Synthetic datasets for statistical disclosure control: theory and implementation, volume 201.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Synthetic datasets for statistical disclosure control: theory and implementation, volume 201

Reference 12

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Observation 734b6f91-debc-4bea-924d-397d4dde0869 · outbound

This paper cites and Graff, C.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data and Graff, C

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:49.059712Z

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Observation c1405c67-06b6-44e5-944e-9798a0f324f2 · outbound

This paper cites The algorithmic foundations of differential privacy.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data The algorithmic foundations of differential privacy

Reference 14

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Observation 16559829-92a9-4c15-9684-c72d249c99cb · outbound

This paper cites Understanding and mitigating memorization in diffusion models for tabular data, 2024.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Understanding and mitigating memorization in diffusion models for tabular data, 2024

Reference 15

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Observation 549f09c1-af37-4e6d-8086-07c9ee31c526 · outbound

This paper cites an unresolved cited work.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Unresolved cited work

Reference 16

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Source-reported events for the cited work

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Observation 390f15db-23b5-4482-9a3f-320f8bf77de0 · outbound

This paper cites Anonymization: The imperfect science of using data while preserving privacy.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Anonymization: The imperfect science of using data while preserving privacy

Reference 17

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Observation 37195cdb-ae38-4304-81be-74daa1d6f589 · outbound

This paper cites Generative adversarial nets.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Generative adversarial nets

Reference 18

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Observation 3df1d838-5f84-4baf-8b03-8bbcb89faddc · outbound

This paper cites Why do tree-based models still outperform deep learning on typical tabular data? In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Why do tree-based models still outperform deep learning on typical tabular data? In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A

Reference 19

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Observation 420bed60-4206-4319-a82c-dda64e5ef0eb · outbound

This paper cites Row conditional-tgan for generating synthetic relational databases.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Row conditional-tgan for generating synthetic relational databases

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.995566Z

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Observation 0161727a-881a-47a7-8277-78c97f38ffb7 · outbound

This paper cites and Roysdon, P.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data and Roysdon, P

Reference 21

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Source-reported events for the cited work

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Observation 61e5838d-e271-4b11-b2f8-d3e1ec8598fb · outbound

This paper cites Tabllm: Few-shot classification of tabular data with large language models.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Tabllm: Few-shot classification of tabular data with large language models

Reference 22

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Observation 34dcb1a1-f8c8-4ab6-a556-3daa8c725c31 · outbound

This paper cites and Schmidhuber, J.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data and Schmidhuber, J

Reference 23

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Observation a25ac0ef-f96b-4864-8858-9bd839426d23 · outbound

This paper cites Multipurpose synthetic population for policy applications.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Multipurpose synthetic population for policy applications

Reference 24

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Observation d4138dc9-2a0a-4730-be92-dc82e9a50e34 · outbound

This paper cites SoK: Privacy-Preserving Data Synthesis.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data SoK: Privacy-Preserving Data Synthesis

Reference 25

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Generating and imputing tabular data via diffusion and flow-based gradient-boosted trees

Reference 26

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Observation f1a91216-c28b-4dba-a5fb-00c2313f54e0 · outbound

This paper cites Synthetic Data -- what, why and how?.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Synthetic Data -- what, why and how?

Reference 27

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Observation e3ee9613-971c-443e-936f-d12a9871ae2c · outbound

This paper cites SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers

Reference 28

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Stasy: Score-based tabular data synthesis

Reference 29

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Observation 82e84337-634f-4fad-88b0-e61ab23b0baa · outbound

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Auto-Encoding Variational Bayes

Reference 30

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Tabddpm: Modelling tabular data with diffusion models

Reference 31

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Observation 8ab5e0ae-92f3-400b-a62d-5b985e83e7b6 · outbound

This paper cites Strong statistical parity through fair synthetic data.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Strong statistical parity through fair synthetic data

Reference 32

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Observation 502b1ccd-1aba-4784-a14e-75bd890e7f5d · outbound

This paper cites Sean lahman baseball database, 2023.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Sean lahman baseball database, 2023

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.905791Z

Source-reported events for the cited work

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Observation 720806bb-67ae-4cd9-8e02-9fb459ae3bc5 · outbound

This paper cites Composable Generative Models.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Composable Generative Models

Reference 34

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This paper cites Codi: Co-evolving contrastive diffusion models for mixed-type tabular synthesis.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Codi: Co-evolving contrastive diffusion models for mixed-type tabular synthesis

Reference 35

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data J., Li, J., and Zhu, T

Reference 36

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Observation 76cd149d-aa38-4843-b5aa-17888493d08b · outbound

This paper cites Using gans for sharing networked time series data: Challenges, initial promise, and open questions.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Using gans for sharing networked time series data: Challenges, initial promise, and open questions

Reference 37

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Goggle: Generative modelling for tabular data by learning relational structure

Reference 38

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Observation f38cbc97-6587-405f-ab86-4ba7006e0344 · outbound

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Unmasking Trees for Tabular Data

Reference 39

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data and Sariyar, M

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.855735Z

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Observation 568a685b-0c48-4634-bde1-2d7eee6df446 · outbound

This paper cites MOSTLY AI - Quality Assurance , 2024.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data MOSTLY AI - Quality Assurance , 2024

Reference 41

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data GPT-4 Technical Report

Reference 42

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Unresolved cited work

Reference 43

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This paper cites ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models

Reference 44

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Data synthesis based on generative adversarial networks, 2018

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.825054Z

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Observation 9bf18579-3f0e-4b29-baf2-fb8249f6a7d3 · outbound

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data The synthetic data vault

Reference 46

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data and Reutterer, T

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.809569Z

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Observation d53eee85-d744-455a-a768-bf7e91d05f9a · outbound

This paper cites Synthcity: facilitating innovative use cases of synthetic data in different data modalities, 2023, 2023.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Synthcity: facilitating innovative use cases of synthetic data in different data modalities, 2023, 2023

Reference 48

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Unresolved cited work

Reference 49

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TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Training and Inference on Any-Order Autoregressive Models the Right Way

Reference 50

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This paper cites V., Crowley, K., and Agarwal, R.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data V., Crowley, K., and Agarwal, R

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.795289Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2feec093-365c-4313-9c05-e1fb8612aba1 · outbound

This paper cites REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.662784Z

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Observation 59824f58-a2f6-44d2-accb-d4c64aae82a5 · outbound

This paper cites Timeautodiff: Combining autoencoder and diffusion model for time series tabular data synthesizing.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Timeautodiff: Combining autoencoder and diffusion model for time series tabular data synthesizing

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.667337Z

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Observation 2a181005-d12f-4d4d-8621-9eba721e6f11 · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.671523Z

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Observation b77bf050-2abb-404e-9945-979adc048d63 · outbound

This paper cites Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.675826Z

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Unavailable: canonical work link unavailable.

Observation 283b0c25-d951-43c0-8cd8-dd80c6dd22a7 · outbound

This paper cites Synthetic Data for Official Statistics.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Synthetic Data for Official Statistics

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.680097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aeff8c3-7a9a-435e-9b91-091fb40a4472 · outbound

This paper cites Neural autoregressive distribution estimation.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Neural autoregressive distribution estimation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.780956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 97eb774e-4800-4daf-ab0d-ae6508015043 · outbound

This paper cites Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.688985Z

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Unavailable: canonical work link unavailable.

Observation 13fb04d5-6c8d-4438-bd82-57efcc8cba59 · outbound

This paper cites Decaf: Generating fair synthetic data using causally-aware generative networks.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Decaf: Generating fair synthetic data using causally-aware generative networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.767241Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1114ef7-6f71-409a-8382-99481ffe6ca2 · outbound

This paper cites Diffusion Models for Tabular Data Imputation and Synthetic Data Generation.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Diffusion Models for Tabular Data Imputation and Synthetic Data Generation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.698954Z

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Unavailable: canonical work link unavailable.

Observation 77c42b18-2879-43cb-9bd1-a4682a19b30e · outbound

This paper cites Modeling tabular data using conditional gan.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Modeling tabular data using conditional gan

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.703737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ab3eeb2-6836-43b1-9438-448bca7a6116 · outbound

This paper cites XLNet: Generalized Autoregressive Pretraining for Language Understanding.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data XLNet: Generalized Autoregressive Pretraining for Language Understanding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.708134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1849b12d-d899-42ea-bd79-5e9cabb0a24e · outbound

This paper cites Default of credit card clients, 2009.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Default of credit card clients, 2009

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.713079Z

Source-reported events for the cited work

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Observation eeeace99-2868-481f-b208-d798461a2a76 · outbound

This paper cites Time-series generative adversarial networks.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Time-series generative adversarial networks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.717749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e334afae-3f00-45db-a1e8-5dc5a5530e93 · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.722281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ee685bc-4a79-439a-b5ef-96a844f75575 · outbound

This paper cites Diffusion-TS: Interpretable Diffusion for General Time Series Generation.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Diffusion-TS: Interpretable Diffusion for General Time Series Generation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.727154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e8708c5-faca-4a87-935a-bb07180a4477 · outbound

This paper cites Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T17:41:47.731968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 071ecb47-6413-4726-8a41-f320ca072ec6 · outbound

This paper cites Mixed-type tabular data synthesis with score-based diffusion in latent space.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Mixed-type tabular data synthesis with score-based diffusion in latent space

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.732900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b6d2caec-0cd6-4340-9692-903a7b94031a · outbound

This paper cites an unresolved cited work.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:41:48.716947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 547096ff-d9b1-4056-8b17-9febcd284ed4 · outbound

This paper cites an unresolved cited work.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:41:48.699870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 296abd76-ecaa-4186-8071-b4115d365b71 · outbound

This paper cites Sdformer: Similarity-driven discrete transformer for time series generation.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Sdformer: Similarity-driven discrete transformer for time series generation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:41:48.683002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 1eefbf9d-3334-4613-ac36-effbfca6d6f9 · inbound

A Note on Statistically Accurate Tabular Data Generation Using Large Language Models cites this paper.

A Note on Statistically Accurate Tabular Data Generation Using Large Language Models TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T00:49:59.127349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06b81809-a40e-439a-9566-76960512a871 · inbound

Disjoint Generation of Synthetic Data cites this paper.

Disjoint Generation of Synthetic Data TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T14:12:19.137268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01504750-bc1c-4e4a-a96e-6f1e6a4f71e9 · inbound

Achieving Hilbert-Schmidt Independence Under R\'enyi Differential Privacy for Fair and Private Data Generation cites this paper.

Achieving Hilbert-Schmidt Independence Under R\'enyi Differential Privacy for Fair and Private Data Generation TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f86f70b-54d0-4822-84a5-b73e62e6e381 · inbound

Autoregressive Synthesis of Sparse and Semi-Structured Mixed-Type Data cites this paper.

Autoregressive Synthesis of Sparse and Semi-Structured Mixed-Type Data TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29730827-d6c6-4cfb-b60c-9cb42ab77e34 · inbound

Tabular Foundation Model for Generative Modelling cites this paper.

Tabular Foundation Model for Generative Modelling TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:41:43.368388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0d3ef960-b658-4fd4-856d-52a79f0d9875 · inbound

Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data cites this paper.

Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T22:52:41.099615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73012fc3-f6c4-4b08-abcd-bce2654f00ea · inbound

NAE: Normalizing AutoEncoder cites this paper.

NAE: Normalizing AutoEncoder TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

Reference 143

Resolution: unresolved; no resolver link, observed 2026-08-16T00:22:07.836022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.