Paper Citation Record · LEDGER

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2508.14413.

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

pith.paper-citation-record.v1
2508.14413 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:41:34.754868Z

measured 41 of 41 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 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

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0ae17fa4-26af-4391-a9dc-dc720b0743b0 · outbound

This paper cites write newline.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states write newline

Reference 1

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Source-reported events for the cited work

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Observation eb7e1a1c-5734-40e6-be3c-6219bf1c564e · outbound

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

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T18:41:30.780764Z

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Observation c079a585-7f96-4999-8b37-1d03157ce5ef · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Reproducible scaling laws for contrastive language-image learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T18:41:30.870551Z

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Observation d4634697-d0d4-47f3-9a24-2ca528a34b07 · outbound

This paper cites Perception prioritized training of diffusion models.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Perception prioritized training of diffusion models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:40.528486Z

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Observation 69f30784-740d-41b6-a3c7-2a6a5f6f6fcd · outbound

This paper cites NVIDIA NVLink Interconnect.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states NVIDIA NVLink Interconnect

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:40.349868Z

Source-reported events for the cited work

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Observation 3e12ed55-8763-42bc-8a68-84235bf67533 · outbound

This paper cites Socher, Li Fei-Fei, Wei Dong, Kai Li, and Li-Jia Li.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Socher, Li Fei-Fei, Wei Dong, Kai Li, and Li-Jia Li

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:40.166488Z

Source-reported events for the cited work

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Observation dbc011d6-0c6d-44f2-8019-7536a739ebe7 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Diffusion models beat gans on image synthesis

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:39.952723Z

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Observation db253de0-a011-423e-9227-8cd8c650c53b · outbound

This paper cites Taming transformers for high-resolution image synthesis, 2020.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Taming transformers for high-resolution image synthesis, 2020

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:39.798098Z

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Observation 6ddeb9be-560e-49ea-a7e9-ebf78fbad704 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 9

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Observation ff5bc1fe-aa69-4d80-baa8-2724ec3b1423 · outbound

This paper cites an unresolved cited work.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-05T18:41:39.606833Z

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Observation 93cf2aff-4aad-4790-ac4e-d1d31b7fe3d5 · outbound

This paper cites Ernie-vilg 2.0: Improving text-to-image diffusion model with knowledge-enhanced mixture-of-denoising-experts.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Ernie-vilg 2.0: Improving text-to-image diffusion model with knowledge-enhanced mixture-of-denoising-experts

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:39.423399Z

Source-reported events for the cited work

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Observation 0ae8259d-2c74-4407-8f58-b19b2ac0e8db · outbound

This paper cites Masked diffusion transformer is a strong image synthesizer.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Masked diffusion transformer is a strong image synthesizer

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:39.233878Z

Source-reported events for the cited work

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Observation 1a736b86-7634-449d-84a0-5c9965b50d57 · outbound

This paper cites Efficient diffusion training via min-snr weighting strategy.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Efficient diffusion training via min-snr weighting strategy

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:39.017938Z

Source-reported events for the cited work

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Observation df17b9b9-2712-4632-912c-5abd57134065 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:38.881245Z

Source-reported events for the cited work

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Observation 9c185907-9644-49d9-bea7-68ba8c54a12f · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Denoising Diffusion Probabilistic Models

Reference 15

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Observation 67850597-ba20-4148-bcdb-b7fd79c80a67 · outbound

This paper cites TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering

Reference 16

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Source-reported events for the cited work

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Observation 8e5f4b5f-8790-47ae-8117-b74ff21949bc · outbound

This paper cites Rethinking fid: Towards a better evaluation metric for image generation.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Rethinking fid: Towards a better evaluation metric for image generation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:38.694182Z

Source-reported events for the cited work

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Observation 933f9f57-db00-413c-aead-516596ef0ea5 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Elucidating the design space of diffusion-based generative models

Reference 18

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Observation 38903f92-ea07-45f8-99be-f39edbecd4bf · outbound

This paper cites Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, and Stefano Soatto.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, and Stefano Soatto

Reference 19

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Observation d309d9fb-88a0-446b-8881-693d2e913f25 · outbound

This paper cites an unresolved cited work.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Observation ee9c400f-3fc2-479a-b0db-317e7377e1c2 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Microsoft COCO: Common Objects in Context

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T18:41:32.502601Z

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Observation af0e0a4d-9382-4beb-b945-1c0effd0d1ee · outbound

This paper cites Learning in Implicit Generative Models.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Learning in Implicit Generative Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T18:41:32.558276Z

Source-reported events for the cited work

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Observation 5798c82b-4c11-4b40-9507-5da305c541e2 · outbound

This paper cites Switch diffusion transformer: Synergizing denoising tasks with sparse mixture-of-experts.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Switch diffusion transformer: Synergizing denoising tasks with sparse mixture-of-experts

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:38.286543Z

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 3fea2850-98e4-4cd8-b7e2-66aa5537ed9c · outbound

This paper cites Scalable diffusion models with transformers.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Scalable diffusion models with transformers

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:38.071097Z

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 91048bdb-1d52-4db4-a474-ee5a52ab63f2 · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T18:41:32.745301Z

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Observation bdb920d1-8a70-47be-a674-db3e2e11eaf5 · outbound

This paper cites Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever

Reference 26

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Source-reported events for the cited work

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Observation 367d0071-4928-4d18-9698-1848f661c83a · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states High-resolution image synthesis with latent diffusion models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:37.657282Z

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 16dd0c91-5601-4007-8ed6-dbb33eb6c9cd · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states U-net: Convolutional networks for biomedical image segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:37.435637Z

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 5aeb89d9-8168-4d61-a5cd-4a2278cae89c · outbound

This paper cites Pyramidal Denoising Diffusion Probabilistic Models.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Pyramidal Denoising Diffusion Probabilistic Models

Reference 29

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Source-reported events for the cited work

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Observation 835563df-539c-4a9a-8dfa-c8a195dea1f8 · outbound

This paper cites Improved techniques for training gans.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Improved techniques for training gans

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:37.261229Z

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 69cce2a8-071a-4bb9-a192-e5c355a7b199 · outbound

This paper cites LAION -5b: An open large-scale dataset for training next generation image-text models.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states LAION -5b: An open large-scale dataset for training next generation image-text models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:37.036168Z

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 c7ff3992-3e59-4121-84c9-406d0f64477d · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Deep unsupervised learning using nonequilibrium thermodynamics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:36.818979Z

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 e4e2ee54-a65d-491e-a239-c1ac9cf05748 · outbound

This paper cites Denoising Diffusion Implicit Models.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Denoising Diffusion Implicit Models

Reference 33

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Source-reported events for the cited work

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Observation c5e11f39-a942-4ee3-9d06-71bb74e8e3e0 · outbound

This paper cites A closer look at time steps is worthy of triple speed-up for diffusion model training, 2024.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states A closer look at time steps is worthy of triple speed-up for diffusion model training, 2024

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:36.616892Z

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 47868f70-f618-4b5b-92a9-c33e83a1e810 · outbound

This paper cites Patch diffusion: Faster and more data-efficient training of diffusion models.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Patch diffusion: Faster and more data-efficient training of diffusion models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:36.438655Z

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 9097299c-78ad-4c3d-83dc-9e884659528a · outbound

This paper cites Tackling the generative learning trilemma with denoising diffusion gans.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Tackling the generative learning trilemma with denoising diffusion gans

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:36.207727Z

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 07ebcd71-a7df-4ab8-b74e-2e2b5cc3d0a2 · outbound

This paper cites Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T18:41:34.084031Z

Source-reported events for the cited work

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Observation a5a8ddef-1273-4313-8027-41a6574c5eb4 · outbound

This paper cites Truncated diffusion probabilistic models and diffusion-based adversarial auto-encoders.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Truncated diffusion probabilistic models and diffusion-based adversarial auto-encoders

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:35.984344Z

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 4a7686e0-a3ce-461b-af2a-a3e1fb0fc1aa · outbound

This paper cites Fast training of diffusion models with masked transformers.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Fast training of diffusion models with masked transformers

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:35.783178Z

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 8ac850b2-bfef-4f6c-8da9-d591a5d527a3 · outbound

This paper cites Non-uniform timestep sampling: Towards faster diffusion model training.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Non-uniform timestep sampling: Towards faster diffusion model training

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:35.595610Z

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 a1c7ed96-67cb-4151-916c-1779aba6bff2 · outbound

This paper cites Beta-tuned timestep diffusion model.

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states Beta-tuned timestep diffusion model

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:41:35.408503Z

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.

Pith citing papers

No inbound Pith citation observations are available.