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Paper Citation Record · LEDGER

Integration Flow Models

As of 17 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2504.20179.

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

pith.paper-citation-record.v1
2504.20179 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:43:04.410440Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:09:21.954009Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T17:09:22.343165Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved22
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation c63dba93-3bf2-4396-bff6-9603c325e6aa · outbound

This paper cites Building Normalizing Flows with Stochastic Interpolants.

Integration Flow Models Building Normalizing Flows with Stochastic Interpolants

Reference 1

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Observation 9c70adc5-bdc7-4458-8e94-d5fb0d0e32ee · outbound

This paper cites Multistep Consistency Models.

Integration Flow Models Multistep Consistency Models

Reference 8

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source=pdf_text observed=2026-08-16T05:43:04.325071Z digest=sha256:5a0d5c9899f6c6cf4b76f3de078d74b490ed1200cc55f24c9a8ccbd397fdacb6

Observation fef5c92e-6965-4c1f-b103-8c16ef864d9e · outbound

This paper cites Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models.

Integration Flow Models Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

Reference 10

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Observation 5ee157bc-422c-40fd-b0c8-6525fe32d800 · outbound

This paper cites 9 Submission for ICML 2025 Krizhevsky, A., Hinton, G., et al.

Integration Flow Models 9 Submission for ICML 2025 Krizhevsky, A., Hinton, G., et al

Reference 11

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source=pdf_text observed=2026-08-16T05:43:04.340113Z digest=sha256:a1293aa20b27ae57403236ea9470aab69dc12ede09294c08109708e851c67436

Observation f4179777-c48d-46ad-a331-6ddf69145506 · outbound

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

Integration Flow Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 14

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source=pdf_text observed=2026-08-16T05:43:04.355279Z digest=sha256:71b2c9f18d5b24a785e926d26fe0a470da235cb8928fbe66e20fd8bca717179c

Observation 7fed9762-1d58-4315-a9c7-4d0c315568f5 · outbound

This paper cites Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed.

Integration Flow Models Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 15

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source=pdf_text observed=2026-08-16T05:43:04.360109Z digest=sha256:3e616abda4ee8c0bdd7e1d30233e5aa2c632ef0049a0b380201c07c1f846f136

Observation ca0974d9-0d24-4c48-9243-fa6b71aa4ad1 · outbound

This paper cites Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models.

Integration Flow Models Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models

Reference 16

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source=pdf_text observed=2026-08-16T05:43:04.365038Z digest=sha256:ddafe10e11f8307766e5e2d3e332c4330f425ea6a2d7398dcfba227d41806d81

Observation 7bbde87d-320a-40c1-8ab8-3d29845e1dd2 · outbound

This paper cites StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis.

Integration Flow Models StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

Reference 17

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Observation 68b815d7-93c9-4a7b-925e-b390ab8a64cb · outbound

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

Integration Flow Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 18

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Observation 81a6b7d0-4763-4cb9-a1c9-d8c6e5bf3682 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

Integration Flow Models Dropout: a simple way to prevent neural networks from overfitting

Reference 19

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source=pdf_text observed=2026-08-16T05:43:04.379984Z digest=sha256:3e186ed55888b1a813e4f2f4553293e3864879465c204141cbc220c9850cd28f

Observation 085d0c06-fd81-45e8-b58a-5cb462ffa997 · outbound

This paper cites Consistency Flow Matching: Defining Straight Flows with Velocity Consistency.

Integration Flow Models Consistency Flow Matching: Defining Straight Flows with Velocity Consistency

Reference 20

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source=pdf_text observed=2026-08-16T05:43:04.385460Z digest=sha256:9596219c8dc72cd72672945211f30f18477431241addca8f155479b1e12bc618

Observation 9b6e4b00-315e-44d6-a1e3-ef78f110dd08 · outbound

This paper cites Directly Denoising Diffusion Models.

Integration Flow Models Directly Denoising Diffusion Models

Reference 21

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T05:43:04.390456Z digest=sha256:7cbaffeaad98f03bd895b57118dcbb3b475e55b7392062cb29759ac087dd00f7

Observation d2acf03c-d793-419c-aea0-a9d6ea69a32e · outbound

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

Integration Flow Models Fast Sampling of Diffusion Models with Exponential Integrator

Reference 22

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source=pdf_text observed=2026-08-16T05:43:04.395839Z digest=sha256:092a6edd40c38775ce54326e6afffbe575f2bea0b3bd75ca6eab9e67c4858e7b

Observation 67541521-c775-4e24-a85a-218d5f562774 · outbound

This paper cites UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models.

Integration Flow Models UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models

Reference 23

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Observation 96b7767c-26bf-4178-a1ca-1ab903330f5d · outbound

This paper cites Derivation of Integration Flow Algorithms A.1.

Integration Flow Models Derivation of Integration Flow Algorithms A.1

Reference 24

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation e731f158-3e17-4eb3-be44-1362baa87678 · outbound

This paper cites an unresolved cited work.

Integration Flow Models Unresolved cited work

Reference 25

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Observation c6d1efab-753c-48f6-9e9e-9dd927ebc0f6 · outbound

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Integration Flow Models Unresolved cited work

Reference 1997

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source=pdf_text observed=2026-08-16T05:43:04.304492Z digest=sha256:e461b850e31bc0573db48ebbf17d8b830e08f29277f2e9d2caf56738be43a5a3

Observation e83e42d2-a6a5-476d-ab3f-7065b4bbb548 · outbound

This paper cites Improving the Training of Rectified Flows.

Integration Flow Models Improving the Training of Rectified Flows

Reference 2009

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source=pdf_text observed=2026-08-16T05:43:04.345333Z digest=sha256:38d0249bf6146eba6c8224ed134dac52b9235056e94f3dffbb0a85aff55a5ece

Observation eddba640-be49-4741-a275-fc951613b10c · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Integration Flow Models Imagenet: A large-scale hierarchical image database

Reference 2018

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Observation 0b178d0e-a326-4a76-9ec7-7b8491d18fcf · outbound

This paper cites BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping.

Integration Flow Models BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping

Reference 2019

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Observation da350a72-bf2b-4f32-8c08-1573d99b8418 · outbound

This paper cites Scalable Adaptive Computation for Iterative Generation.

Integration Flow Models Scalable Adaptive Computation for Iterative Generation

Reference 2020

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Observation 46435179-ea3b-4f9b-8dfe-d6a818733701 · outbound

This paper cites ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs.

Integration Flow Models ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs

Reference 2021

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source=pdf_text observed=2026-08-16T05:43:04.314690Z digest=sha256:5e54fe808bad5379c375d62c41875f29aaa2c594f558300507beb18c521092c9

Observation 8a22df99-79ab-4dc9-a7fe-a0d5a75d7846 · outbound

This paper cites TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation.

Integration Flow Models TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation

Reference 2022

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Observation 7a4ecda2-31ac-472b-9921-28e20f110d7b · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Integration Flow Models Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 2023

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Observation 2a6d31f7-300f-44c0-8a96-ed522b8a8216 · outbound

This paper cites Flow Matching for Generative Modeling.

Integration Flow Models Flow Matching for Generative Modeling

Reference 2024

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Pith citing papers

Observation d3a77e1f-9b7a-4eb8-96ef-18ac6392ddb3 · inbound

Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modeling cites this paper.

Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modeling Integration Flow Models

Reference 38

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local_arxiv, observed 2026-08-15T17:09:22.357817Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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