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Paper Citation Record · LEDGER

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility

As of 10 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2505.18521.

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

pith.paper-citation-record.v1
2505.18521 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:35:19.869576Z

measured 38 of 38 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T01:44:04.922621Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T01:46:13.859391Z

Reference resolution

37 of 37 outbound references displayed

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  • unresolved21
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2081ea35-4c2b-43ca-ba8c-506ad70b7b5f · outbound

This paper cites Image embedding for denoising generative models.Artificial Intelligence Review, 56(12):14511–14533, 2023.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Image embedding for denoising generative models.Artificial Intelligence Review, 56(12):14511–14533, 2023

Reference 1

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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.

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Observation a1d02260-1225-4088-8ea0-c10ae71449dc · outbound

This paper cites Conditional wasser- stein distances with applications in bayesian ot flow matching, 2024.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Conditional wasser- stein distances with applications in bayesian ot flow matching, 2024

Reference 2

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Observation 8d9b636b-0d7a-4fcc-8f9f-d33f7a172b11 · outbound

This paper cites Diffusion policy: Visuomotor policy learning via action diffusion.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Diffusion policy: Visuomotor policy learning via action diffusion

Reference 3

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Observation 3f94372f-110b-4363-92d3-f5b696fdc807 · outbound

This paper cites On implementing 2d rectangular assignment algorithms.IEEE Transactions on Aerospace and Electronic Systems, 52(4):1679–1696, 2016.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility On implementing 2d rectangular assignment algorithms.IEEE Transactions on Aerospace and Electronic Systems, 52(4):1679–1696, 2016

Reference 4

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

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Observation 9cb9382d-9267-4cdf-88ce-26cd74960fe1 · outbound

This paper cites an unresolved cited work.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Unresolved cited work

Reference 5

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Observation 40681f8a-2c22-4b11-9098-94634103d02f · outbound

This paper cites an unresolved cited work.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Unresolved cited work

Reference 6

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Source-reported events for the cited work

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Observation d72e5ca0-bdfc-4d5a-9367-2566c6c16c93 · outbound

This paper cites Clipscore: A reference-free evaluation metric for image captioning.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Clipscore: A reference-free evaluation metric for image captioning

Reference 7

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Observation 15c79646-a3bb-45c3-b5ef-32be32018838 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2018.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2018

Reference 8

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Observation e7cd5209-851a-4280-9f00-d9582a38d152 · outbound

This paper cites Blue noise for diffusion models.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Blue noise for diffusion models

Reference 9

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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.

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Observation c59fac77-0b87-4966-83d9-77a5430ed32c · outbound

This paper cites Improving consistency models with generator-induced coupling, 2024.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Improving consistency models with generator-induced coupling, 2024

Reference 10

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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.

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Observation c0ea3938-f79e-410d-9b86-af0d1fcaa813 · outbound

This paper cites Analyzing and improving the training dynamics of diffusion models.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Analyzing and improving the training dynamics of diffusion models

Reference 11

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Observation 07d425d7-a9f6-4ce4-9a67-8fb6ef5d0b7e · outbound

This paper cites Understanding DDPM latent codes through optimal transport.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Understanding DDPM latent codes through optimal transport

Reference 12

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Source-reported events for the cited work

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Observation 60c7b2c3-075b-4cfc-9ccd-339d49d2af78 · outbound

This paper cites Auto-encoding variational bayes, 2022.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Auto-encoding variational bayes, 2022

Reference 13

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Observation f2ce1ac9-c097-48a2-a17b-6db7232e57f4 · outbound

This paper cites Learning multiple layers of features from tiny images.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Learning multiple layers of features from tiny images

Reference 14

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Observation ef78fb8c-ac58-4336-9637-40a58e17f8b0 · outbound

This paper cites Score-based generative modeling secretly minimizes the wasserstein distance.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Score-based generative modeling secretly minimizes the wasserstein distance

Reference 15

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Source-reported events for the cited work

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Observation 47205a05-ff3d-4bf6-bfe2-e87ecd5607d2 · outbound

This paper cites Minimizing trajectory curvature of ODE-based generative models.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Minimizing trajectory curvature of ODE-based generative models

Reference 16

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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.

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Observation cc7556ae-1cd6-4aed-a6b3-0f940cf00207 · outbound

This paper cites Immiscible diffusion: Accelerating diffusion training with noise assignment.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Immiscible diffusion: Accelerating diffusion training with noise assignment

Reference 17

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

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Observation c1884b41-0dd4-4e6c-bf97-4ca61a4135a5 · outbound

This paper cites Microsoft coco: Common objects in context.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Microsoft coco: Common objects in context

Reference 18

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Observation 638ebe06-f26a-4f8b-b716-837f3d1d414f · outbound

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Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Unresolved cited work

Reference 19

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Observation ca4e24d5-33d3-458d-9d69-94161beccc1a · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 20

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Source-reported events for the cited work

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Observation 0d497223-ba09-48b4-afc6-92d706c9e172 · outbound

This paper cites Instaflow: One step is enough for high-quality diffusion-based text-to-image generation.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Instaflow: One step is enough for high-quality diffusion-based text-to-image generation

Reference 21

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Observation 8b858cfe-f4f5-4df4-8b8a-68b702c4c593 · outbound

This paper cites Towards a Mechanistic Explanation of Diffusion Model Generalization.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Towards a Mechanistic Explanation of Diffusion Model Generalization

Reference 22

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Observation 3720c17c-f1a0-4e6d-a847-bb33ed038ad9 · outbound

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Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Unresolved cited work

Reference 23

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Source-reported events for the cited work

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Observation 61868b2e-7fee-4f3b-90c0-f0525a9dce44 · outbound

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

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility High- resolution image synthesis with latent diffusion models

Reference 24

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Observation dab53375-1911-4703-9159-9d5abe30c129 · outbound

This paper cites High-resolution image synthesis with latent diffusion models, 2022.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility High-resolution image synthesis with latent diffusion models, 2022

Reference 25

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Observation a161498d-171c-4549-8904-872c1950effc · outbound

This paper cites Laion- 5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Laion- 5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022

Reference 26

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Observation ca60182a-7a93-4297-b148-dfe4b5016d02 · outbound

This paper cites Denoising diffusion implicit models, 2022.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Denoising diffusion implicit models, 2022

Reference 27

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Observation 8c98eb2b-7eb2-41b9-ac42-5d1ec8fa8a54 · outbound

This paper cites Improved techniques for training consistency models.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Improved techniques for training consistency models

Reference 28

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Observation d072f5f7-4aee-4dfc-9bbb-0b7fbf3f1cd3 · outbound

This paper cites Consistency models.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Consistency models

Reference 29

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Source-reported events for the cited work

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Observation 23028d1c-7a39-438b-b701-309a8876cec7 · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport, 2024.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Improving and generalizing flow-based generative models with minibatch optimal transport, 2024

Reference 30

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Observation e35654e6-a92c-4dfe-9315-07892d907c1c · outbound

This paper cites A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training

Reference 31

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Source-reported events for the cited work

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Observation ab259c75-54dd-412b-ac58-0d3f3f70ff10 · outbound

This paper cites Patch diffusion: Faster and more data-efficient training of diffusion models.Advances in neural information processing systems, 36:72137–72154, 2023.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Patch diffusion: Faster and more data-efficient training of diffusion models.Advances in neural information processing systems, 36:72137–72154, 2023

Reference 32

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Observation 1a6042b6-47a2-4a4f-97cf-6e001d2e3d37 · outbound

This paper cites Exploring straighter trajecto- ries of flow matching with diffusion guidance, 2023.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Exploring straighter trajecto- ries of flow matching with diffusion guidance, 2023

Reference 33

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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.

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Observation 59e52e97-bf65-4538-aced-7e8c6bad0865 · outbound

This paper cites The emergence of reproducibility and consistency in diffusion models.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility The emergence of reproducibility and consistency in diffusion models

Reference 34

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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.

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Observation cefc30d6-c037-49be-9727-06ba8aa01081 · outbound

This paper cites Formulating discrete probability flow through optimal transport.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Formulating discrete probability flow through optimal transport

Reference 35

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raw_fallback, observed 2026-08-07T14:35:19.948412Z

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.

source=pdf_text observed=2026-08-07T14:35:19.862268Z digest=sha256:0327b63f98b1848973ae62dc5760d07dcd45e0084fe4897ec3d67787c520cce2

Observation 54765b26-637a-449b-bb84-69ebe1e5f7c7 · outbound

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

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility Non-uniform timestep sampling: Towards faster diffusion model training

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:35:19.937767Z

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.

source=pdf_text observed=2026-08-07T14:35:19.865899Z digest=sha256:53f5e147e28d32df0ea3e4b1853fb661745873a16fbd31ca38db903e06617e98

Observation 01b0a997-93ba-423b-aa81-af97d991b776 · outbound

This paper cites There and back again: On the relation between noise and image inversions in diffusion models, 2025.

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility There and back again: On the relation between noise and image inversions in diffusion models, 2025

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:35:19.926782Z

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.

source=pdf_text observed=2026-08-07T14:35:19.869576Z digest=sha256:0052e71db9a28ce1b7c4f6c1eb87d143ba5743c2383456a93f6a40ccb4eb8355

Pith citing papers

Observation 8a3fb5c1-c619-419d-af16-4c674058c9cf · inbound

NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training cites this paper.

NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-07-01T02:16:54.609590Z

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.

source=pdf_text observed=2026-05-12T01:44:04.922621Z digest=sha256:b5a7c0a1ef2de11786a85bddc77f179a07ffcc89c5486bc6de044ee430e9fc0d