Paper Citation Record · LEDGER

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization

As of 15 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2506.22304.

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

pith.paper-citation-record.v1
2506.22304 v3

Coverage vector

measured 49 of 49 reference resolution

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T08:05:16.851267Z

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Source: pith, observed 2026-07-03T08:07:44.908855Z

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation 4fb72f01-91f9-42d3-9736-49812359a442 · outbound

This paper cites Forecasting sequential data using consistent koopman autoencoders.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Forecasting sequential data using consistent koopman autoencoders

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:53.034544Z

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Observation b0652ad0-aa1e-44c7-a251-07a8471bf3ea · outbound

This paper cites One-step offline distillation of diffusion-based models via koopman modeling.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization One-step offline distillation of diffusion-based models via koopman modeling

Reference 2

Resolution: verified exact; raw_fallback, observed 2026-08-06T22:15:49.265299Z

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Observation d0ff3984-9ba0-4731-96d9-11cc9d8e43be · outbound

This paper cites Koopman operator dynamical models: Learning, analysis and control.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Koopman operator dynamical models: Learning, analysis and control

Reference 3

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Observation f2c068fa-06ed-4225-b4bb-9ad70be4bbfe · outbound

This paper cites Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models

Reference 4

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Observation 65fd29b6-ec99-43c8-b77a-773a2c69eba4 · outbound

This paper cites Modern Koopman Theory for Dynamical Systems.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Modern Koopman Theory for Dynamical Systems

Reference 5

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Observation c7f2d596-5c23-4e22-98ab-838b4e4558cb · outbound

This paper cites Applied koopmanism.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Applied koopmanism

Reference 6

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Observation 8ad29275-dce2-4194-9eca-33d24f548c47 · outbound

This paper cites A survey on generative diffusion models.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization A survey on generative diffusion models

Reference 7

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Observation 0ee23c09-cf69-4cdd-81ae-6e136a458025 · outbound

This paper cites Neural ordinary differential equations.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Neural ordinary differential equations

Reference 8

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Observation fb32d145-b44d-4f0e-95a6-6ebb624b972a · outbound

This paper cites Nice: Non-linear independent components estimation.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Nice: Non-linear independent components estimation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:52.253112Z

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Observation b74962f9-43bf-48ed-9781-e7c54fef582b · outbound

This paper cites Density estimation using real nvp.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Density estimation using real nvp

Reference 10

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Observation 2e4023aa-8632-47ab-92ff-500ff17c18cf · outbound

This paper cites One Step Diffusion via Shortcut Models.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization One Step Diffusion via Shortcut Models

Reference 11

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Observation d0ea069e-d479-42c7-be33-522c5b5e6641 · outbound

This paper cites Koopman theory for generative modeling of chaotic time series.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Koopman theory for generative modeling of chaotic time series

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:52.106962Z

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Observation 81c7ef40-4f33-4227-b169-007a8cb0b639 · outbound

This paper cites Borgwardt, Malte J.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Borgwardt, Malte J

Reference 13

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Observation 67614cf4-399d-4361-86f7-23e8a1341360 · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 14

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Observation 2bf547bf-99a1-4ce8-88b8-7a53774ea7cb · outbound

This paper cites Denoising diffusion probabilistic models.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Denoising diffusion probabilistic models

Reference 15

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Observation 6a068842-a361-4804-9fca-dcd0eed0a0e5 · outbound

This paper cites Efficient 3d molecular generation with flow matching and scale optimal transport.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Efficient 3d molecular generation with flow matching and scale optimal transport

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:51.925232Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4077e1c2-d7af-4fd0-a6a5-8c935fd839eb · outbound

This paper cites Glow: Generative flow with invertible 1x1 convolutions.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Glow: Generative flow with invertible 1x1 convolutions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:51.696250Z

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Observation 3ae29550-4236-4a24-afb1-9e7e1e7aca55 · outbound

This paper cites u ske, P \'e ter Koltai, Hao Wu, Ioannis Kevrekidis, Christof Sch \.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization u ske, P \'e ter Koltai, Hao Wu, Ioannis Kevrekidis, Christof Sch \

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:51.566881Z

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Observation a0513561-ae09-4df1-be22-c2a3eb8a8c35 · outbound

This paper cites Hamiltonian systems and transformation in hilbert space.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Hamiltonian systems and transformation in hilbert space

Reference 19

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Observation e8d7f772-8634-49c8-9ce9-8408a55b2110 · outbound

This paper cites Dynamical systems of continuous spectra.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Dynamical systems of continuous spectra

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:51.324126Z

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Observation 446a5d3f-98d6-471e-aa21-d1bb3177cf2a · outbound

This paper cites Dynamic mode decomposition: data-driven modeling of complex systems.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Dynamic mode decomposition: data-driven modeling of complex systems

Reference 21

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Observation bfc75f53-d1ca-44be-b246-be0888afa694 · outbound

This paper cites Flow matching for generative modeling.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Flow matching for generative modeling

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:51.076500Z

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Observation 6b5a3b4c-2486-4ecf-9d0b-f44d59539cd5 · outbound

This paper cites Distilled decoding 1: One-step sampling of image auto-regressive models with flow matching.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Distilled decoding 1: One-step sampling of image auto-regressive models with flow matching

Reference 23

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Observation 6e666e3d-1221-4c2d-b12b-ba54c5343100 · outbound

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

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 24

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Observation 4633f0fb-ea0a-4b31-812e-74487bee8ccc · outbound

This paper cites Flow straight and fast: Learning to generate straight lines improves generalization and efficiency.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Flow straight and fast: Learning to generate straight lines improves generalization and efficiency

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:50.845846Z

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Observation 75d49fff-d80d-4177-b0ca-5b66920ecb08 · outbound

This paper cites Flow matching with stochastic differential equations.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Flow matching with stochastic differential equations

Reference 26

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Observation bcbf64a5-421d-4942-89f6-3f527f084704 · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 27

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This paper cites Deep learning for universal linear embeddings of nonlinear dynamics.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Deep learning for universal linear embeddings of nonlinear dynamics

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:50.735940Z

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Observation 3592af76-ae1d-499f-b3cf-fe676e424b0c · outbound

This paper cites Interpretable learning of effective dynamics for multiscale systems.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Interpretable learning of effective dynamics for multiscale systems

Reference 29

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Observation 9d15c8ce-3fc1-4dd2-bfb1-2fe1d46437f9 · outbound

This paper cites Spectral properties of dynamical systems, model reduction and decompositions.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Spectral properties of dynamical systems, model reduction and decompositions

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:50.504850Z

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Observation da413f3f-fa3c-46c4-884d-bfe7ac3e4b53 · outbound

This paper cites Koopman Operator, Geometry, and Learning.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Koopman Operator, Geometry, and Learning

Reference 31

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This paper cites Linearly recurrent autoencoder networks for learning dynamics.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Linearly recurrent autoencoder networks for learning dynamics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:50.366945Z

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This paper cites Functional maps: a flexible representation of maps between shapes.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Functional maps: a flexible representation of maps between shapes

Reference 33

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Observation edc3826b-911d-4544-a348-92c2a6d25975 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 34

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Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Pedregosa, G

Reference 35

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This paper cites State of the art on diffusion models for visual computing.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization State of the art on diffusion models for visual computing

Reference 36

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This paper cites Limits from the grave: resurrecting Hitomi for decaying dark matter and forecasting leading sensitivity for XRISM.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Limits from the grave: resurrecting Hitomi for decaying dark matter and forecasting leading sensitivity for XRISM

Reference 37

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Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 38

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This paper cites Spectral analysis of nonlinear flows.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Spectral analysis of nonlinear flows

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:49.921945Z

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Observation 358274ef-0755-463c-b339-7c82431bc2b0 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Generative modeling by estimating gradients of the data distribution

Reference 40

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Observation a3bf19df-4bf1-41d1-ba94-3289ac74b39b · outbound

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

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Score-Based Generative Modeling through Stochastic Differential Equations

Reference 41

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Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Consistency models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:49.765260Z

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Observation ccd81675-daab-4cc7-ba2f-25f16a0bc1dc · outbound

This paper cites Strogatz.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Strogatz

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:49.677514Z

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Observation 97b7a447-db9a-4bfe-b93b-15573fa099aa · outbound

This paper cites Applied koopman operator theory for power systems technology.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Applied koopman operator theory for power systems technology

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:49.566359Z

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Observation 01b57244-12f6-4136-831b-7227d2b9f112 · outbound

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

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 45

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Observation ce0d4caa-a8fe-4f1e-92e5-12de4969aaae · outbound

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

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 46

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This paper cites Torchcfm: A conditional flow matching library.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Torchcfm: A conditional flow matching library

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:49.448739Z

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Observation 0a085bb2-0121-485c-817f-4159781d096c · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Diffusion models: A comprehensive survey of methods and applications

Reference 48

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Source-reported events for the cited work

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Observation 3bdb0326-34fd-41f3-a02d-bf2197a729a5 · outbound

This paper cites Learning deep neural network representations for koopman operators of nonlinear dynamical systems.

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization Learning deep neural network representations for koopman operators of nonlinear dynamical systems

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:15:49.356316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 132969fa-1644-4689-a69e-adc0b96b016d · inbound

Koopman operator theory: fundamentals, control, and applications cites this paper.

Koopman operator theory: fundamentals, control, and applications Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization

Reference 193

Resolution: verified exact; local_arxiv, observed 2026-07-03T08:07:44.911018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.