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Paper Citation Record · LEDGER

SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 44 inbound Pith citation observations for arXiv:2401.08740.

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

pith.paper-citation-record.v1
2401.08740 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 44 of 44 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:02:59.653070Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3e9b1c09-c96d-42e8-98f6-df087a3955de · inbound

EventFlow: Forecasting Temporal Point Processes with Flow Matching cites this paper.

EventFlow: Forecasting Temporal Point Processes with Flow Matching SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 24

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arxiv_id, observed 2026-05-23T19:05:46.729873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-23T19:05:12.933364Z digest=sha256:2c8a14b963f68c558c6694b992368a137ba81e04787789a31a1630a727d048de

Observation 89eefafc-08b0-4b2e-8262-315ed8ac284a · inbound

Flow Matching Guide and Code cites this paper.

Flow Matching Guide and Code SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 52

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arxiv_id, observed 2026-05-12T10:28:14.058157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-12T10:28:14.014706Z digest=sha256:9ca94bb3be73aad0a71b710bb6e612dc29f73a5c0f643e03e299795ec816694a

Observation 9a20d62d-9009-4557-aff3-c80dee8b1c87 · inbound

Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps cites this paper.

Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 50

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arxiv_id, observed 2026-05-20T11:45:17.633903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 54812bf1-195d-4fae-a86f-16e537ac48b0 · inbound

Seedream 3.0 Technical Report cites this paper.

Seedream 3.0 Technical Report SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 13

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arxiv_id, observed 2026-05-13T07:55:38.782457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-13T07:55:38.690569Z digest=sha256:47a8b80505edb126d1d39ab4044444e37ec5b09a0954df635312cc6377d52e47

Observation 3a24c084-5581-42df-b4c4-88c3679f0997 · inbound

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis cites this paper.

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 46

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no resolver link, observed 2026-08-07T06:02:59.653070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:02:59.653070Z digest=sha256:dfc9575a7063f76709a380d79d986d1f0766f143de8c1843e435ae312981ee70

Observation 8b242ca2-ef67-4c57-a9a8-d9dda2a33e65 · inbound

UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation cites this paper.

UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 16

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no resolver link, observed 2026-08-06T20:26:37.885766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:26:37.885766Z digest=sha256:2b70a972d45966916a828acfb3324770b59993cb59b2b09a0865e1c0e9bb3ac0

Observation 504b9f5b-010f-495c-bb85-89dc6a8fe572 · inbound

La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching cites this paper.

La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 45

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no resolver link, observed 2026-08-06T18:00:53.132231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:00:53.132231Z digest=sha256:bd78d1bf082a6b30787dc76eef4d5e136a5ab94508d4cb70909b8a3e6bdd6d46

Observation 78920582-22b7-4b4f-b488-e3cb1af4ce87 · inbound

PixNerd: Pixel Neural Field Diffusion cites this paper.

PixNerd: Pixel Neural Field Diffusion SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 14

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no resolver link, observed 2026-08-06T10:59:53.556391Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:59:53.556391Z digest=sha256:c4f5de767d2a5ce2e4aaae42d6e5dc2173c07b889b4dec88d8da057866676899

Observation 842c7831-ed8c-4c9e-a6f8-455a40a3507f · inbound

FlowBack-Adjoint: Physics-Aware and Energy-Guided Conditional Flow-Matching for All-Atom Protein Backmapping cites this paper.

FlowBack-Adjoint: Physics-Aware and Energy-Guided Conditional Flow-Matching for All-Atom Protein Backmapping SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 17

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no resolver link, observed 2026-08-06T04:25:45.514699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:25:45.514699Z digest=sha256:36d29fc0b768419a610dc66bbd0022fa788ec7f33747e2e6f566c9fd37cedeed

Observation a71851bf-e54f-47f8-b1b3-bee9b0b9ca7e · inbound

Transition Models: Rethinking the Generative Learning Objective cites this paper.

Transition Models: Rethinking the Generative Learning Objective SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 49

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no resolver link, observed 2026-08-05T10:19:54.415886Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:19:54.415886Z digest=sha256:fdc28139d57ab7c5fa1fa0e0e98cf86e2be03a2e0b7f9e8ae0d0fcb6ad86e3c2

Observation 0d80c86a-da79-4959-84e7-75b3fbeef456 · inbound

Missing Fine Details in Images: Last Seen in High Frequencies cites this paper.

Missing Fine Details in Images: Last Seen in High Frequencies SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 32

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no resolver link, observed 2026-08-05T05:27:34.270506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T05:27:34.270506Z digest=sha256:1aa5ef1796fa1189f52592f0ab302702f7bd8bf9fc99fd0a4099aefb0d515b09

Observation e76ca54a-100b-43b2-ad76-195846a3b043 · inbound

Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT cites this paper.

Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 23

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verified exact
arxiv_id, observed 2026-05-18T16:31:37.309739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d8cc6f4e-e343-4de9-8dbb-618ed047e943 · inbound

Flow marching for a generative PDE foundation model cites this paper.

Flow marching for a generative PDE foundation model SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 45

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verified exact
arxiv_id, observed 2026-05-18T13:51:25.634942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-18T13:48:14.532529Z digest=sha256:45cab31fee5d4577a70febafac8617c5b4db740f65525baa1835dd2c62508e04

Observation 8cbfd164-1aaf-459e-8ecd-6fdb366bd89c · inbound

DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation cites this paper.

DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 39

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verified exact
arxiv_id, observed 2026-05-17T05:49:08.304126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-17T05:47:24.669763Z digest=sha256:6f7959db8529edbf6d558e3d9fb34c23c3a87c1375c80adec211326e950373be

Observation 775d61ef-fa8c-40ee-8185-c4fffa423b47 · inbound

Trainable Log-linear Sparse Attention for Efficient Diffusion Transformers cites this paper.

Trainable Log-linear Sparse Attention for Efficient Diffusion Transformers SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 23

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no resolver link, observed 2026-08-03T15:35:15.656310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:35:15.656310Z digest=sha256:39605c8baed7f21f0797e48b811d2a482e35405faa990e3edf1a8c45db8ba615

Observation 26443260-9a7e-468a-b8c5-2868fd8a3965 · inbound

Self-transcendence: Is External Feature Guidance Indispensable for Accelerating Diffusion Transformer Training? cites this paper.

Self-transcendence: Is External Feature Guidance Indispensable for Accelerating Diffusion Transformer Training? SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 26

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no resolver link, observed 2026-08-03T11:04:32.319958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 41cd81f8-34b6-4e08-be3f-f63e5f5484da · inbound

PixelGen: Improving Pixel Diffusion with Perceptual Supervision cites this paper.

PixelGen: Improving Pixel Diffusion with Perceptual Supervision SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 13

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verified exact
arxiv_id, observed 2026-05-16T07:57:33.188969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 0b5951ea-e949-44ab-ace6-c1a0022aea16 · inbound

Optimizing Few-Step Generation with Adaptive Matching Distillation cites this paper.

Optimizing Few-Step Generation with Adaptive Matching Distillation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 12

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no resolver link, observed 2026-08-03T03:42:45.300743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 398437ea-3233-4fa9-aa93-bab49c0a1f1f · inbound

Generative Modeling via Kernelized Stochastic Interpolants cites this paper.

Generative Modeling via Kernelized Stochastic Interpolants SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 9

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no resolver link, observed 2026-08-02T21:32:19.617512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T21:32:19.617512Z digest=sha256:634e79cd367c50e93679aac300ca640226375359818cfb299bfb01a5e5ca240b

Observation d46eeabf-11b0-46c5-8af6-8e35b6f939ba · inbound

Setting-Matched and Semantics-Scaled Benchmarking of One-Step Generative Models Against Multistep Diffusion and Flow Models cites this paper.

Setting-Matched and Semantics-Scaled Benchmarking of One-Step Generative Models Against Multistep Diffusion and Flow Models SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 16

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metadata mismatch
arxiv_id, observed 2026-05-15T12:20:00.819268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-15T12:15:38.186914Z digest=sha256:60f12c225cbac08c4d07f90e4196bf00bd4fb17fd2ca0b48e3d6397ae99cfb6b

Observation 33e5d802-3d2a-4651-b151-09629de6629c · inbound

GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow cites this paper.

GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 22

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arxiv_id, observed 2026-05-14T23:33:16.253198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-14T23:31:43.868878Z digest=sha256:c1d47d2aa69565f6e6c7401dc7ffccf7db78f521a6aa295eb1e12ac5d07e9f43

Observation b7afc9d8-6de0-4291-a0a7-e00ec0b2aff4 · inbound

Discrete Meanflow Training Curriculum cites this paper.

Discrete Meanflow Training Curriculum SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 12

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arxiv_id, observed 2026-05-11T05:10:55.570947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T18:15:51.900778Z digest=sha256:01a3f6652657e0d6ede83807e3edc4c3a962d1cac3610d8f2056817d599c3063

Observation 1fc9ff28-0fcf-4856-8cd0-b2152ba0335b · inbound

Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation cites this paper.

Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 41

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verified exact
arxiv_id, observed 2026-05-10T03:08:59.190290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T03:06:43.992858Z digest=sha256:17fcf38290ee7b4950ca44352c652251e11a1fa8079d5824ea01c0d0f695380b

Observation f2607530-f1ff-4258-8e21-ba5f7bd97434 · inbound

Posterior Augmented Flow Matching cites this paper.

Posterior Augmented Flow Matching SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 20

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arxiv_id, observed 2026-05-11T15:36:07.414161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-09T19:36:54.901681Z digest=sha256:092f87ab65f99bc6602d2db2a29eba90a263a93128234c19895f74c97ba5c34e

Observation bdaccb71-9422-49c6-8e5a-9f0ad8dd9300 · inbound

What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching cites this paper.

What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 21

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arxiv_id, observed 2026-05-12T08:36:24.812415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation f897c324-07b3-4bdc-9c55-35ffd910e7a6 · inbound

The two clocks and the innovation window: When and how generative models learn rules cites this paper.

The two clocks and the innovation window: When and how generative models learn rules SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 55

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arxiv_id, observed 2026-05-12T03:16:18.047678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-12T03:15:45.257213Z digest=sha256:e5175f067117f8a67955d2a08f9a289e83c5431579c367a52b414f464f751b92

Observation 76188e4d-3b26-48ae-a29d-7e21ba5d75f5 · inbound

FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion cites this paper.

FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 14

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arxiv_id, observed 2026-05-20T12:48:17.670588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 5ba13344-c8b5-4227-bb73-ab17a96bad6e · inbound

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation cites this paper.

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 27

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arxiv_id, observed 2026-05-20T12:03:15.281509Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-20T11:59:54.139888Z digest=sha256:301358d7110e8bdd80d8f2001c6e30fc43ff53aef4b6bff15188299d84a2389a

Observation b83df877-b91c-4ffe-97eb-43e75d7e0363 · inbound

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation cites this paper.

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 27

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arxiv_id, observed 2026-06-30T18:45:00.706079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T18:39:40.667006Z digest=sha256:6e3d454f6de1adeb8859b8ca767a5c1fe4d092529b7b93c9c54b3be75eee46db

Observation 01842dd2-230b-4768-8e71-0e08e07b1108 · inbound

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation cites this paper.

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 27

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no resolver link, observed 2026-08-02T13:49:19.939916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T13:49:19.939916Z digest=sha256:85580dfb3f5fb32bf04565f6e6cd7db12786b73db8aa83a3121e9390826cd243

Observation 25f0f05d-4073-4b49-b8fa-facd3c2bdfa6 · inbound

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters cites this paper.

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 91

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arxiv_id, observed 2026-07-02T06:06:41.246709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-28T07:41:38.548022Z digest=sha256:372bf84760f6072d037221d1b9bffcd5798190c36293870bd5911b362d16b23c

Observation d6a09457-3d7d-4417-85ac-fd034b97fb3c · inbound

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters cites this paper.

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 91

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unresolved
no resolver link, observed 2026-08-02T12:30:44.601602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:30:44.601602Z digest=sha256:57e07052a86e44abff62cd9fb9eb0d8d660cefc1e12c6920c404a9f2a7e82227

Observation 812658fe-1966-4e2d-8a31-4ad3bacc63b2 · inbound

DSA: Dynamic Step Allocation for Fast Autoregressive Video Generation cites this paper.

DSA: Dynamic Step Allocation for Fast Autoregressive Video Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 37

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arxiv_id, observed 2026-07-02T06:16:43.694176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-28T07:28:25.789458Z digest=sha256:dbcf53a5b4f9ead9039ba1ac1b75a5e1d5f064be9289327050de331a2ad8b3fe

Observation 387772a3-27e8-491b-a847-d27b6ea02328 · inbound

Balancing Image Compression and Generation with Bootstrapped Tokenization cites this paper.

Balancing Image Compression and Generation with Bootstrapped Tokenization SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-07-02T11:46:55.392724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-28T03:07:33.054518Z digest=sha256:ea77369b84eed9ed92e51a1178f062eec1746398fdc98938ee29ba1bef78f4a8

Observation 80798ca6-a46a-486a-895f-f7cd2c7c48be · inbound

IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder cites this paper.

IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-03T05:37:40.229595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T13:10:14.308216Z digest=sha256:5ceaf026a7c02e4d56d335e614d6094ade04a1587dfe21d363b37bac805ccae0

Observation cbf21d47-0c06-449f-aa2a-2023dbd965e1 · inbound

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation cites this paper.

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 26

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T20:50:11.391641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-25T19:34:02.046104Z digest=sha256:d90bbba46d8d7092b147b40d76f0be31beeaca877f4cfb22e69fa3490db5f221

Observation ba148561-f9e7-4894-9d16-79badf62f30f · inbound

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation cites this paper.

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 29

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T09:35:40.184655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-07-01T06:27:24.992386Z digest=sha256:fa50a268e3d2d33cc4be83f487f8f24c6fcc52e29af373ddb227f9515a81f23b

Observation 76c39b9f-c1e0-4ae6-999c-306b27e47838 · inbound

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation cites this paper.

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 29

Resolution
unresolved
no resolver link, observed 2026-07-12T12:07:02.175855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T12:07:02.175855Z digest=sha256:3e188940c954f377c706debcb902cbe5f9fbcf2b27fb0da25c43d7363f3d9bf9

Observation 84fff43a-18a0-479c-b380-66f530f52449 · inbound

PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion cites this paper.

PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 28

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T20:03:57.190864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T04:31:57.169935Z digest=sha256:1b983df92a6029862160b820c241c1417d307ffdb6bf57df09e7d84395b94bcd

Observation c095b750-6d29-4e92-9af0-4fce50520ab6 · inbound

Spatial Transport of Integration Error in Generative ODEs cites this paper.

Spatial Transport of Integration Error in Generative ODEs SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-01T22:04:48.902642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T22:04:48.902642Z digest=sha256:39d2c13db5302f4f93294b675b8c1f95089decf00ea10c3bf31d9362995c38ce

Observation 66cdf29a-1ba9-4865-828f-2cdd887890bf · inbound

HistoFID- Calibrating Frechet-distance evaluation across pathology foundation models cites this paper.

HistoFID- Calibrating Frechet-distance evaluation across pathology foundation models SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-01T10:56:35.830674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T10:56:35.830674Z digest=sha256:24e890496f54aee120d1492653b0d78ba10fcf3133e1be792d71ddf7e140dd11

Observation e1c920c2-64bc-4dcc-86f4-7451d29b31b4 · inbound

WaiT for the Signal: Simple Frequency-Aware Flow-Matching cites this paper.

WaiT for the Signal: Simple Frequency-Aware Flow-Matching SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-03T00:34:35.484981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T00:34:35.484981Z digest=sha256:c0846fed98570ccd3052b302a96bc0fe5f26e69f0a91d9ec445218fdc4fb2e5a

Observation 3b7fc349-f4bc-4e61-94bc-6bdff6404539 · inbound

MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation cites this paper.

MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-03T12:14:41.609978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T12:14:41.609978Z digest=sha256:bfdf3393c703fdae04499275755dc22b99b962a01eee36eb3aabbc0202a3930b

Observation 9a9afcdd-7c6f-4ffe-900f-e70d2b221952 · inbound

Beckmann Transport Models: From Autonomous Flows to One-Step Maps cites this paper.

Beckmann Transport Models: From Autonomous Flows to One-Step Maps SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:55.622484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:16:55.622484Z digest=sha256:b0df5ad8dce2b52748520525078482d1d9600616a67d369600e3ebc9d54e8bdc