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Paper Citation Record · LEDGER

Diffusion Models and Representation Learning: A Survey

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2407.00783.

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

pith.paper-citation-record.v1
2407.00783 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:59:44.085222Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:59:58.150347Z

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 4e1fef24-4527-46ad-be32-4c25a8f1763a · inbound

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think cites this paper.

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think Diffusion Models and Representation Learning: A Survey

Reference 134

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T15:09:37.111587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-12T15:09:36.982610Z digest=sha256:401652c947edbe5676f4d3938115d0281608c7e35f02881308da61208ef24659

Observation b3938c18-1b31-47c3-9d62-3a6247bca279 · inbound

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images cites this paper.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Diffusion Models and Representation Learning: A Survey

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-09T10:59:44.085222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:59:44.085222Z digest=sha256:c3e3df8577a2a0cdcdeee90d5375b19f2eaccfe09b7dabcfb86497c2e36938a9

Observation ade7fd2f-118a-4d8b-a295-b245f66e7678 · inbound

From Image to Video: An Empirical Study of Diffusion Representations cites this paper.

From Image to Video: An Empirical Study of Diffusion Representations Diffusion Models and Representation Learning: A Survey

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-08T14:10:55.211931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:10:55.211931Z digest=sha256:8d16ae5962e9873c7246b949f3b88802d9ca69a11b3a26994f189d18f8e5fb51

Observation 6fe28080-2dd0-4b65-a6fc-a286c5e56389 · inbound

SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation cites this paper.

SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation Diffusion Models and Representation Learning: A Survey

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T14:45:43.298029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:45:43.298029Z digest=sha256:ca4a569da61881b77d5ac2f109b0e864ed50ecef936b455d1a6adf0aa99b992b

Observation aa317539-df1e-43d1-b4f8-597efb453b8e · inbound

Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach cites this paper.

Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach Diffusion Models and Representation Learning: A Survey

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T14:16:32.688928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:32.688928Z digest=sha256:76e62f1d7bbd3966807bb78e8f114229af3991bfac8c101d12dd6921c2de0c7f

Observation 0d733709-b4b8-4a50-8da9-47e6f88a046f · inbound

Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment cites this paper.

Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment Diffusion Models and Representation Learning: A Survey

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T11:33:58.810974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:33:58.810974Z digest=sha256:c586d8fe9a04a9c52df3fa2866e05f2f72791c14fade9678b1674376cba24d2a

Observation 7ff2653b-fbb8-4951-af81-86fde7f88dc2 · inbound

Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models cites this paper.

Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models Diffusion Models and Representation Learning: A Survey

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T11:27:23.954699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:27:23.954699Z digest=sha256:f3f1800b5320006a4284c6e180749a3aa04479ab588ffcfa9aa70e2d9783e15b

Observation ba6e67ae-ce76-4950-b972-71d4fd87648f · inbound

DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation cites this paper.

DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation Diffusion Models and Representation Learning: A Survey

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T19:04:17.462093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:04:17.462093Z digest=sha256:1119517a11241004e726638feef0cce0d234b73fede4a2faca55fb767ceb65dc

Observation c0d2da50-f0c3-420f-bf79-b2d836a60206 · inbound

Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model cites this paper.

Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model Diffusion Models and Representation Learning: A Survey

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T18:12:43.664677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:12:43.664677Z digest=sha256:491a4b6041ccd0be7252b1b86334373ed36ce05b60d4b1b83aa4d74c8a892dbf

Observation 7e86f786-02d8-4d23-a983-12eaad4f8965 · inbound

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models cites this paper.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Diffusion Models and Representation Learning: A Survey

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T04:35:07.979603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T04:35:07.979603Z digest=sha256:c7b054e612625f7cee7427bcaf133167e415aa7e4025343b929a8a882bd06b1f

Observation 28b36a95-b780-4627-9f0a-2034b7d23a44 · inbound

Teacher-Feature Drifting: One-Step Diffusion Distillation with Pretrained Diffusion Representations cites this paper.

Teacher-Feature Drifting: One-Step Diffusion Distillation with Pretrained Diffusion Representations Diffusion Models and Representation Learning: A Survey

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T04:25:56.084976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-11T01:26:56.460904Z digest=sha256:a84ff149e157be23b65d8de6c0be20bda45d7080caf84c2084f03490231c1a3a

Observation d7195946-6401-4716-9e88-7c0b696914ca · inbound

Backbone-Equated Diffusion OOD via Sparse Internal Snapshots cites this paper.

Backbone-Equated Diffusion OOD via Sparse Internal Snapshots Diffusion Models and Representation Learning: A Survey

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:17:28.874599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-13T07:15:57.948000Z digest=sha256:ef8bdc610f906eea88b1152745b8cef8942cb659f211658a1490fe4cc80ba431

Observation 61a4eca8-82c2-4fd8-863f-f3f91e6633fa · inbound

Semantic Generative Tuning for Unified Multimodal Models cites this paper.

Semantic Generative Tuning for Unified Multimodal Models Diffusion Models and Representation Learning: A Survey

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-20T11:33:14.175205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-20T11:32:24.007847Z digest=sha256:0ad59e0814d21e79fb501a74ec0e51c63232ae3901c3c52ac309249b49487252

Observation 6be2cc4f-d226-4388-bd58-8cfb49e136e9 · inbound

Semantic Generative Tuning for Unified Multimodal Models cites this paper.

Semantic Generative Tuning for Unified Multimodal Models Diffusion Models and Representation Learning: A Survey

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-06-30T18:35:00.368536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T18:31:10.578558Z digest=sha256:c9b4a1e0706d35566dda5aa69852f15e186ccc6a981adcae19f1ea1fbf5da693

Observation 9c9197a5-a8e6-4e2b-b9ec-63438a72d716 · inbound

DiffusionBench: On Holistic Evaluation of Diffusion Transformers cites this paper.

DiffusionBench: On Holistic Evaluation of Diffusion Transformers Diffusion Models and Representation Learning: A Survey

Reference 94

Resolution
verified exact
arxiv_id, observed 2026-07-04T16:59:58.152642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-26T00:06:11.951205Z digest=sha256:758b5a6f54636ea1c557e8cdd8759985fa2cdc1b9663e457cc7c8774c92a5b88