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Paper Citation Record · LEDGER

Generative Models: Principles, Architectures, and Applications

As of 19 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2608.08101.

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

pith.paper-citation-record.v1
2608.08101 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:31:34.739092Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

  • verified exact4
  • verified fuzzy8
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 9a4733de-bb04-4e28-99ba-22c838b2cc31 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Generative Models: Principles, Architectures, and Applications An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation dfe6ac2f-e6ac-40a9-a013-2da58ce8a15b · outbound

This paper cites Query-key normalization for transformers.

Generative Models: Principles, Architectures, and Applications Query-key normalization for transformers

Reference 10

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Observation ae2b678f-4e10-44fb-b324-f0ae1a50cc8e · outbound

This paper cites If you use this software, please cite it as below.

Generative Models: Principles, Architectures, and Applications If you use this software, please cite it as below

Reference 13

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source=pdf_text observed=2026-08-12T00:31:34.628796Z digest=sha256:55c562c2897038899b4d14b49d54352f52ab6eed70eac4bc815a4649d9417b4d

Observation 7f64df5a-3468-4443-8de5-275ab754fc80 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Generative Models: Principles, Architectures, and Applications Adam: A Method for Stochastic Optimization

Reference 14

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source=pdf_text observed=2026-08-12T00:31:34.632938Z digest=sha256:99b7edb76de257e5cadee6cac00960cda1d6779e015823780c28ad7912bfe1e7

Observation bd543dd1-84b5-4050-b892-2893c2503ee8 · outbound

This paper cites Auto-Encoding Variational Bayes.

Generative Models: Principles, Architectures, and Applications Auto-Encoding Variational Bayes

Reference 15

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source=pdf_text observed=2026-08-12T00:31:34.637003Z digest=sha256:f1105bcb6649f1a685899889118ea6f6f27fe9d9656aae93e93f8d1b8dee177a

Observation 030cb8de-29ce-43b5-84f3-3e8b5078fbcd · outbound

This paper cites FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space.

Generative Models: Principles, Architectures, and Applications FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space

Reference 16

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source=pdf_text observed=2026-08-12T00:31:34.641337Z digest=sha256:dc7e9adc23338e7693f1fa09c73b7aadbc48aa1a187091b195e290bbba6c35aa

Observation 2667061b-c427-491b-b485-d8ec5248f3ae · outbound

This paper cites Flow Matching for Generative Modeling.

Generative Models: Principles, Architectures, and Applications Flow Matching for Generative Modeling

Reference 17

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source=pdf_text observed=2026-08-12T00:31:34.645105Z digest=sha256:0b6643798088c4d8ec7886270e023d01517a89c1623c7a998c5531d60c9ef3e8

Observation adc096fc-da44-443c-b1f1-7ac604d8771d · outbound

This paper cites Flow Matching Guide and Code.

Generative Models: Principles, Architectures, and Applications Flow Matching Guide and Code

Reference 18

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source=pdf_text observed=2026-08-12T00:31:34.649030Z digest=sha256:ef5f78221e5675ceb5228cdd3dabec6b6ebf3f7c8b46bf725c46648c4652a587

Observation 0dd135db-ded1-4be3-b813-e93aaedac3c0 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Generative Models: Principles, Architectures, and Applications Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 19

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source=pdf_text observed=2026-08-12T00:31:34.653401Z digest=sha256:3c1ed0a1ab255bb7831dcb9f7843f21dc91941070c774f149b2fbf6743d40926

Observation f3fbfb5d-82bd-40e8-b7a2-01b08703a681 · outbound

This paper cites Practical Topics in Optimization.

Generative Models: Principles, Architectures, and Applications Practical Topics in Optimization

Reference 21

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T00:31:34.661094Z digest=sha256:af3ff3c595270fbb19d2c4a18f26362d8bc5942b7d15db06ba804acff95731c1

Observation cfd6822a-42cf-414e-bfc1-d1e6aa77d175 · outbound

This paper cites A first course in sparse optimization.arXiv preprint arXiv:2601.06173,.

Generative Models: Principles, Architectures, and Applications A first course in sparse optimization.arXiv preprint arXiv:2601.06173,

Reference 22

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T00:31:34.665173Z digest=sha256:dbc41d3c3a79c63872098db81147907878fc1e1a40cf8a22e7ceb6b3de856425

Observation 4a623fa7-7475-4040-b178-56eed226fb43 · outbound

This paper cites Autoencoding Conditional GAN for Portfolio Allocation Diversification.

Generative Models: Principles, Architectures, and Applications Autoencoding Conditional GAN for Portfolio Allocation Diversification

Reference 24

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source=pdf_text observed=2026-08-12T00:31:34.673295Z digest=sha256:d5d8125812f60e2f573d065b207912879132956f85dabe6ca6de7d8263c06063

Observation cadd86fc-4963-4417-98fd-3cadafbe5321 · outbound

This paper cites Understanding Diffusion Models: A Unified Perspective.

Generative Models: Principles, Architectures, and Applications Understanding Diffusion Models: A Unified Perspective

Reference 25

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source=pdf_text observed=2026-08-12T00:31:34.677488Z digest=sha256:c7970c7b767f482289aba409c7aeee794e6b87a65f7d6e4f4d7e779c161fe778

Observation a5841af1-f147-4fe1-addd-3a44e870c7b3 · outbound

This paper cites SDXL: Improving latent diffusion models for high- resolution image synthesis.

Generative Models: Principles, Architectures, and Applications SDXL: Improving latent diffusion models for high- resolution image synthesis

Reference 29

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T00:31:34.695003Z digest=sha256:fe92e6670700d20f7fe1b3df6220f6715b2f25e7e0b0c3152528e024de585d86

Observation 71eb78ae-b696-4537-bc9f-f5f135df0b31 · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

Generative Models: Principles, Architectures, and Applications Movie Gen: A Cast of Media Foundation Models

Reference 30

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source=pdf_text observed=2026-08-12T00:31:34.699210Z digest=sha256:7ec75cb12bb66f19338510a13ddfcf5edcd71f7e0c348d4d0dcd0b648b693d3d

Observation 9749341d-1cf4-45a2-b901-6866d3d286c8 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Generative Models: Principles, Architectures, and Applications Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 31

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source=pdf_text observed=2026-08-12T00:31:34.703943Z digest=sha256:122a9868743bdcf97c6291d5e3e8caaac17a1192e8227cc2ecf038afddbaeebd

Observation b798070e-51e5-4655-8b18-9b26ed25b8c2 · outbound

This paper cites Kandinsky: an improved text-to-image synthesis with image prior and latent diffusion.

Generative Models: Principles, Architectures, and Applications Kandinsky: an improved text-to-image synthesis with image prior and latent diffusion

Reference 32

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T00:31:34.708526Z digest=sha256:33d0f8e35761796e7d0183660648faa28d67cc6d03383f4ad535f4166e5b4aec

Observation 67dd6935-2e2c-4f23-bf17-49cf0003c335 · outbound

This paper cites Denoising Diffusion Implicit Models.

Generative Models: Principles, Architectures, and Applications Denoising Diffusion Implicit Models

Reference 33

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source=pdf_text observed=2026-08-12T00:31:34.712811Z digest=sha256:2b4b90c6bbfeb5797b121ac7be20198db5d11ee26020badbaa894c66421a3bc2

Observation 5245c4f7-8e54-4fe0-ac9f-9dec98ba3ebd · outbound

This paper cites NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale.

Generative Models: Principles, Architectures, and Applications NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale

Reference 35

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source=pdf_text observed=2026-08-12T00:31:34.721694Z digest=sha256:004d04bd942f17648009099e6ea10c786b872a1b2323933c3b8617437d698dc0

Observation f313538b-4eba-4c5c-8cd5-48548f041d4b · outbound

This paper cites SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers.

Generative Models: Principles, Architectures, and Applications SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 38

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source=pdf_text observed=2026-08-12T00:31:34.734515Z digest=sha256:18bf9684db74e5606d316b203ddd4e45781facd50ddea7cf6c510b1a659c25f5

Observation 65a9adba-d75f-46df-92c6-de1e3e98ca99 · outbound

This paper cites Fast Sampling of Diffusion Models with Exponential Integrator.

Generative Models: Principles, Architectures, and Applications Fast Sampling of Diffusion Models with Exponential Integrator

Reference 39

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source=pdf_text observed=2026-08-12T00:31:34.739092Z digest=sha256:8ab0003cf94a211f811503c5ea86bad28e82e88d13cba57909776af14c36e246

Observation 379a04a1-38d4-4605-90be-fe4523866d61 · outbound

This paper cites Automated Variational Inference in Probabilistic Programming.

Generative Models: Principles, Architectures, and Applications Automated Variational Inference in Probabilistic Programming

Reference 1992

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source=pdf_text observed=2026-08-12T00:31:34.730235Z digest=sha256:f598959adee2768a8dc610e6963274f03ab68fe92d276e5673b4f52a57672d45

Observation 6c38e0eb-12e2-43e7-9b20-b1f5c24fa12f · outbound

This paper cites Classifier-Free Diffusion Guidance.

Generative Models: Principles, Architectures, and Applications Classifier-Free Diffusion Guidance

Reference 1995

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source=pdf_text observed=2026-08-12T00:31:34.620929Z digest=sha256:eb6ea483e42061a4822a85827c1009039c443b3d1682a208ef62bcdc71ccf457

Observation 303b6b90-cf94-4b3a-8d3c-55a2c89aba77 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Generative Models: Principles, Architectures, and Applications GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 1998

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source=pdf_text observed=2026-08-12T00:31:34.685877Z digest=sha256:7c34b8a62674fc674ae4e804ddce4dcf0fdc4f91b75da851599792991b919a50

Observation de01bbaa-1149-4f77-aaa5-972d91193929 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Generative Models: Principles, Architectures, and Applications Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 2003

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source=pdf_text observed=2026-08-12T00:31:34.579346Z digest=sha256:2fa2f033ace99c36e5f9e920bebc2751196a5b2674f41ead0b44f3272b8b318d

Observation 5315e706-6b73-42ac-b1e9-b5fa951c3295 · outbound

This paper cites Learning to encode text as human-readable summaries using generative adversarial networks.

Generative Models: Principles, Architectures, and Applications Learning to encode text as human-readable summaries using generative adversarial networks

Reference 2008

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T00:31:34.725998Z digest=sha256:b93fd3a2ec4cdd276f156183e01e6ded2eb6484fe82825951e4f0af57ecbf70f

Observation 83aa4799-8e42-4f17-9d49-5c8316e39aa4 · outbound

This paper cites eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers.

Generative Models: Principles, Architectures, and Applications eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Reference 2009

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source=pdf_text observed=2026-08-12T00:31:34.574483Z digest=sha256:ec3756a5fe342cbd1a78fbc5fcc8df6353e9e11d1f6b9837e8f39548654329e4

Observation 67e2c0e8-2c47-4c5b-a356-f9c1d4a4b1f2 · outbound

This paper cites W¨ urstchen: An efficient architecture for large-scale text-to-image diffusion models.

Generative Models: Principles, Architectures, and Applications W¨ urstchen: An efficient architecture for large-scale text-to-image diffusion models

Reference 2013

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T00:31:34.690054Z digest=sha256:557cf5123ae8e9b95fe1b16f4f25fa92cee00e53f92cfdbed77e7c0d8e2a4814

Observation 6d92c647-4aa0-4c93-9855-c1f937ef80ac · outbound

This paper cites Score-Based Generative Modeling with Critically-Damped Langevin Diffusion.

Generative Models: Principles, Architectures, and Applications Score-Based Generative Modeling with Critically-Damped Langevin Diffusion

Reference 2014

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source=pdf_text observed=2026-08-12T00:31:34.602948Z digest=sha256:22e970ae63c1c0cea498916f29d55f26aaa4a3ec8d8b1d3f91b90629b7beda58

Observation a0379a78-1baa-46b9-9552-fac60e3657c4 · outbound

This paper cites An introduction to flow matching and diffusion models.

Generative Models: Principles, Architectures, and Applications An introduction to flow matching and diffusion models

Reference 2016

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source=pdf_text observed=2026-08-12T00:31:34.624949Z digest=sha256:4ec0e2f10bc4d319397b51a88996c3d7f2995f47985b5a40e39b4be033d3650d

Observation f3c24fad-a31a-4e17-86c7-37b203554db7 · outbound

This paper cites Conditional Generative Adversarial Nets.

Generative Models: Principles, Architectures, and Applications Conditional Generative Adversarial Nets

Reference 2018

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source=pdf_text observed=2026-08-12T00:31:34.681598Z digest=sha256:3a98db78d19f74d7bda29f764fa6cb3e58a217b6d68042a87534a8eb093ce025

Observation dc47d0ec-9922-4154-8ef8-67a658ce109c · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Generative Models: Principles, Architectures, and Applications Score-Based Generative Modeling through Stochastic Differential Equations

Reference 2019

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source=pdf_text observed=2026-08-12T00:31:34.717171Z digest=sha256:76dd65c9a68cdb58bfd6ea3bb356661e347023835e050a45ff9363143f8438e7

Observation d3938833-9f53-498b-8773-c24bb30199c7 · outbound

This paper cites FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models.

Generative Models: Principles, Architectures, and Applications FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Reference 2020

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source=pdf_text observed=2026-08-12T00:31:34.611899Z digest=sha256:fad9bd2e4f53c5e37c00db96c4391266b3c85a3fe99462c38210d3dfd15ff44d

Observation 1d14b1ef-3384-4c02-ade2-ec1457cf809a · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Generative Models: Principles, Architectures, and Applications NICE: Non-linear Independent Components Estimation

Reference 2021

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source=pdf_text observed=2026-08-12T00:31:34.598382Z digest=sha256:cdf70dcf9b2a5c192bff738ebf2f2680cd10660f4d3d055835805da6bf276b15

Observation a775cd98-47f6-4a50-b8c2-b7f9cb86492a · outbound

This paper cites PixArt-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis.

Generative Models: Principles, Architectures, and Applications PixArt-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis

Reference 2022

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raw_fallback, observed 2026-08-12T00:31:35.513295Z

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.

source=pdf_text observed=2026-08-12T00:31:34.584891Z digest=sha256:928b6a5e7108a34ef9115766b9dcbf8aabd24f56b1876d35a416c2f0ae105cdf

Observation f58ebc40-e542-4e3d-9ab2-f9129a809738 · outbound

This paper cites Unsupervised speech representation learning using wavenet autoencoders.IEEE/ACM transactions on audio, speech, and language processing, 27(12):2041–2053,.

Generative Models: Principles, Architectures, and Applications Unsupervised speech representation learning using wavenet autoencoders.IEEE/ACM transactions on audio, speech, and language processing, 27(12):2041–2053,

Reference 2023

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raw_fallback, observed 2026-08-12T00:31:35.499639Z

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.

source=pdf_text observed=2026-08-12T00:31:34.594061Z digest=sha256:ffb9e02a326ba47e2278fe7e86b3a3c1c7a1aa03e6254f5a0a762ad9666df313

Observation 582d15f6-e725-4ff4-8415-9b19c5466655 · outbound

This paper cites WaveGrad: Estimating Gradients for Waveform Generation.

Generative Models: Principles, Architectures, and Applications WaveGrad: Estimating Gradients for Waveform Generation

Reference 2024

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:31:34.589272Z digest=sha256:0ee6216242498b05a92a959bffc0579b0e3150bfcf9b79f023df7bd5bc668559

Observation 1884bc17-5f2f-4136-bdf3-01d8201bcb17 · outbound

This paper cites Numerical Matrix Decomposition.

Generative Models: Principles, Architectures, and Applications Numerical Matrix Decomposition

Reference 2025

Resolution
verified exact
local_arxiv, observed 2026-08-12T00:31:35.118201Z

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.

source=pdf_text observed=2026-08-12T00:31:34.657414Z digest=sha256:8dc1b637d5d9ed9238d1879c6685475739aeb60592b9bc8a7f7e83bbaa6140d7

Observation c3552327-f44a-4c85-8e89-e5acd11504d5 · outbound

This paper cites A hybrid approach on conditional GAN for portfolio analysis.

Generative Models: Principles, Architectures, and Applications A hybrid approach on conditional GAN for portfolio analysis

Reference 2026

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:31:35.468944Z

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.

source=pdf_text observed=2026-08-12T00:31:34.669308Z digest=sha256:413a660c69dcfe604bd26ea129c1391a1fcd2ba534d6915bed55a2d0608d3572

Pith citing papers

No inbound Pith citation observations are available.