Paper Citation Record · LEDGER

Normalizing Flows are Capable Generative Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2412.06329.

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

pith.paper-citation-record.v1
2412.06329 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 26 of 26 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 26 of 26 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:50:03.733713Z

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

verified exact0
verified fuzzy0
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External citation measurements

4
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 442afa5c-a670-49cd-b5cb-f6b23e66be5f · inbound

Normalizing Flows are Capable Models for Continuous Control cites this paper.

Normalizing Flows are Capable Models for Continuous Control Normalizing Flows are Capable Generative Models

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T12:50:03.733713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0726e273-d6b4-41ed-817f-b74826e0e18c · inbound

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

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis Normalizing Flows are Capable Generative Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:59.728304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 933732f5-60c7-45a1-9d3c-d45a2b192332 · inbound

Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history cites this paper.

Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history Normalizing Flows are Capable Generative Models

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:37:15.150087Z

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 f037e215-73a4-4023-96bd-0aa1bd80e113 · inbound

Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows cites this paper.

Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Normalizing Flows are Capable Generative Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:27.887508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceac2da8-ba61-4022-8a15-4c303a0d24d4 · inbound

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence cites this paper.

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence Normalizing Flows are Capable Generative Models

Reference 375

Resolution: unresolved; no resolver link, observed 2026-08-06T21:12:37.056608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8763492-f806-483f-9796-7a542edcbd4b · inbound

PixNerd: Pixel Neural Field Diffusion cites this paper.

PixNerd: Pixel Neural Field Diffusion Normalizing Flows are Capable Generative Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T10:59:53.635782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7e3f0e1-3c6a-4be4-978c-471f90c93974 · 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 Normalizing Flows are Capable Generative Models

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:49:08.276051Z

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 45103f62-a4c8-4c4b-b497-d38447a8448b · inbound

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

PixelGen: Improving Pixel Diffusion with Perceptual Supervision Normalizing Flows are Capable Generative Models

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T07:57:33.161231Z

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 004e005a-2115-4a6d-9485-980bb93dc995 · inbound

MPDiT: Multi-Patch Global-to-Local Transformer Architecture For Efficient Flow Matching and Diffusion Model cites this paper.

MPDiT: Multi-Patch Global-to-Local Transformer Architecture For Efficient Flow Matching and Diffusion Model Normalizing Flows are Capable Generative Models

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-14T23:48:19.239685Z

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 4b6e4714-d553-4e35-ac2d-7dc727ab4796 · inbound

Optimal Stability of KL Divergence under Gaussian Perturbations cites this paper.

Optimal Stability of KL Divergence under Gaussian Perturbations Normalizing Flows are Capable Generative Models

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:56:05.060415Z

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 045233db-61e4-4284-9ee2-30b570c99281 · inbound

Coevolving Representations in Joint Image-Feature Diffusion cites this paper.

Coevolving Representations in Joint Image-Feature Diffusion Normalizing Flows are Capable Generative Models

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:31:30.475307Z

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 1648b62d-7988-4fd9-ad67-7c4c1e0ecbff · inbound

Normalizing Flows with Iterative Denoising cites this paper.

Normalizing Flows with Iterative Denoising Normalizing Flows are Capable Generative Models

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:11:18.675867Z

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 f2177943-1463-4054-a167-988d1f052548 · 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 Normalizing Flows are Capable Generative Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:03:15.342384Z

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 50f7d88b-7bd1-4054-a49f-64941a2d3262 · 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 Normalizing Flows are Capable Generative Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:15:01.303687Z

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 423baf26-7a34-49e0-95fb-da112e6107a5 · 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 Normalizing Flows are Capable Generative Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T13:49:18.466475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 005df705-46a5-4519-af3e-ae2234d98c48 · inbound

WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling cites this paper.

WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling Normalizing Flows are Capable Generative Models

Reference 100

Resolution: verified exact; arxiv_id, observed 2026-07-02T05:16:39.794214Z

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 9f71efb7-08b6-4240-abf6-1fc21421e8f7 · inbound

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions cites this paper.

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions Normalizing Flows are Capable Generative Models

Reference 130

Resolution: verified exact; arxiv_id, observed 2026-06-27T19:11:10.646189Z

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 a1c7b1f4-8ce0-4ccc-87ef-196957a0ed7a · inbound

Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability cites this paper.

Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability Normalizing Flows are Capable Generative Models

Reference 71

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T05:59:37.844874Z

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 586dd2c2-bf0f-4db5-a1c4-7455d072d1f0 · 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 Normalizing Flows are Capable Generative Models

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:50:11.370238Z

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 26820345-c659-43f1-ae25-6c8c8e89649e · 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 Normalizing Flows are Capable Generative Models

Reference 52

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T09:35:40.204597Z

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 11fd454b-88ec-41fd-9608-70246f0a7a53 · 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 Normalizing Flows are Capable Generative Models

Reference 52

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74b39190-0f92-46d4-b4d7-e2bd668a0407 · 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 Normalizing Flows are Capable Generative Models

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T20:03:57.203701Z

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 bd1161e0-df30-4804-bd2b-6149da5ce93e · inbound

Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps cites this paper.

Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps Normalizing Flows are Capable Generative Models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:24:32.563356Z

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 4adc9fe1-f223-48f2-a571-f1b2acb1a82f · inbound

Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series cites this paper.

Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series Normalizing Flows are Capable Generative Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-11T20:41:10.528667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b89d0f5c-c86d-430f-9e2c-f21b2ed53bbb · inbound

A Generative Model-Free Form Deformation Approach for the Generation of Mesh Motions with Applications to PDE cites this paper.

A Generative Model-Free Form Deformation Approach for the Generation of Mesh Motions with Applications to PDE Normalizing Flows are Capable Generative Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T06:01:02.136932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d589d863-5153-4933-a6e7-7ce5cea92858 · inbound

Amortized Moment Matching for Visual Generation cites this paper.

Amortized Moment Matching for Visual Generation Normalizing Flows are Capable Generative Models

Reference 121

Resolution: unresolved; no resolver link, observed 2026-07-30T18:58:28.307144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.