Paper Citation Record · LEDGER

Convergence Analysis of Flow Matching in Latent Space with Transformers

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2404.02538.

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

pith.paper-citation-record.v1
2404.02538 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:42:55.989833Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:25:45.030097Z

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 ab415b98-5859-4e8c-81bb-7fe07fd25326 · inbound

Approximation Bounds for Transformer Networks with Application to Regression cites this paper.

Approximation Bounds for Transformer Networks with Application to Regression Convergence Analysis of Flow Matching in Latent Space with Transformers

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T12:42:55.989833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4db15cc-a43d-499f-b9c2-4c9983642b5e · inbound

Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective cites this paper.

Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective Convergence Analysis of Flow Matching in Latent Space with Transformers

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T12:19:03.914613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23104d43-01fd-4ddb-a99b-cbf64c94faf1 · inbound

Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation cites this paper.

Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation Convergence Analysis of Flow Matching in Latent Space with Transformers

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:30:44.863350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 600f8f6b-6533-4a00-ad05-f55174f38477 · inbound

Approximation Error Upper and Lower Bounds for H\"{o}lder Class with Transformers cites this paper.

Approximation Error Upper and Lower Bounds for H\"{o}lder Class with Transformers Convergence Analysis of Flow Matching in Latent Space with Transformers

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:00:54.958414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dcdf3f3b-a297-4f5f-af90-0c7f11000340 · inbound

Approximation Error Upper and Lower Bounds for H\"{o}lder Class with Transformers cites this paper.

Approximation Error Upper and Lower Bounds for H\"{o}lder Class with Transformers Convergence Analysis of Flow Matching in Latent Space with Transformers

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:25:45.032646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c8d6b379-85ed-4219-89cc-5a3f98b07303 · inbound

Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations cites this paper.

Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations Convergence Analysis of Flow Matching in Latent Space with Transformers

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T20:13:44.872119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.