Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2501.12012.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:12:19.137268Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T18:16:27.091186Z
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 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
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:59:45.876567Z
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
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T18:16:27.095233Z
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 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 2026-08-08T06:32:00.761636+00:00.
Observation 0d3ef960-b658-4fd4-856d-52a79f0d9875 · 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 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.