Paper Citation Record · LEDGER

CCDM: Continuous Conditional Diffusion Models for Image Generation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2405.03546.

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

pith.paper-citation-record.v1
2405.03546 v4

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 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:14:31.500636Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:51:05.902827Z

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 b124b0c6-8e3c-4585-b71a-173d96a826ff · inbound

UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control cites this paper.

UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control CCDM: Continuous Conditional Diffusion Models for Image Generation

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-08T18:14:31.500636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a74a8337-5e17-43ed-bccf-c748be8ea237 · inbound

Using Diffusion Models to do Data Assimilation cites this paper.

Using Diffusion Models to do Data Assimilation CCDM: Continuous Conditional Diffusion Models for Image Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.520770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d56971d8-022e-414e-bb84-c3dcee853f1f · inbound

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization cites this paper.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization CCDM: Continuous Conditional Diffusion Models for Image Generation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T05:33:44.592818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c42e8c4-a079-42a8-97c2-809d209d995b · inbound

From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data cites this paper.

From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data CCDM: Continuous Conditional Diffusion Models for Image Generation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:51:05.905399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.