Paper Citation Record · LEDGER

Flow-based generative models as iterative algorithms in probability space

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2502.13394.

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

pith.paper-citation-record.v1
2502.13394 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:54:50.447194Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T14:15:47.194426Z

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 1a7ef1cc-85ca-4b79-986c-18f8d88bb609 · inbound

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach cites this paper.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Flow-based generative models as iterative algorithms in probability space

Reference 226

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:50.447194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 439d6922-5e2e-4467-abfe-1129a68d8577 · inbound

Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models cites this paper.

Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models Flow-based generative models as iterative algorithms in probability space

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:49.371726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f17c4be7-eccb-4f54-847b-83296e6c6f70 · inbound

One-Step Generative Modeling via Wasserstein Gradient Flows cites this paper.

One-Step Generative Modeling via Wasserstein Gradient Flows Flow-based generative models as iterative algorithms in probability space

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:47:33.112764Z

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 f7d0f421-5237-4878-8395-97465fe54c26 · inbound

One-Step Generative Modeling via Wasserstein Gradient Flows cites this paper.

One-Step Generative Modeling via Wasserstein Gradient Flows Flow-based generative models as iterative algorithms in probability space

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:15:47.195907Z

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.