Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2102.08921.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T20:53:46.194320Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T17:41:48.662478Z
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 77d565a7-7561-4d67-a6a9-a17c580e30a4 · inbound
A text-to-tabular approach to generate synthetic patient data using LLMs cites this paper.
A text-to-tabular approach to generate synthetic patient data using LLMs How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:53:46.194320Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f03a1993-6149-4a41-8c96-3fa94c904a25 · inbound
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data cites this paper.
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
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-10T17:41:48.667386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 990a61f9-7640-4a63-b235-8b459db4bcc4 · inbound
Diffusion models recover accurate mixture weights despite score function insensitivity cites this paper.
Diffusion models recover accurate mixture weights despite score function insensitivity How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T23:23:41.953772Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2272e4b-a07c-4801-aa0c-f40709221f19 · inbound
FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents cites this paper.
FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:50:33.465167Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 946c4a81-13e7-409a-89b2-7b16e588e523 · inbound
Tokenizer Generator Coupling in Medical Image Generation cites this paper.
Tokenizer Generator Coupling in Medical Image Generation How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:27:00.489452Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.