Paper Citation Record · LEDGER

Lipschitz-Guided Design of Interpolation Schedules in Generative Models

As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 9 inbound Pith citation observations for arXiv:2509.01629.

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

pith.paper-citation-record.v1
2509.01629 v3

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-21T21:53:15.115078Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:20:15.793063Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

50 of 50 outbound references displayed

verified exact14
verified fuzzy35
unresolved0
parse uncertain0
malformed identifier0
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External citation measurements

8
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation dd1f6ee1-9110-4880-9f74-63bf69ce19c1 · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-21T21:54:22.861411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc1290c5-a5bf-4395-a843-89b7563b6f97 · outbound

This paper cites Building normalizing flows with stochastic inter- polants.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Building normalizing flows with stochastic inter- polants

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.227047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3d0b4f0d-e0f4-4558-9acd-d836337e4ae8 · outbound

This paper cites Optimizing Noise Schedules of Generative Models in High Dimensionss.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Optimizing Noise Schedules of Generative Models in High Dimensionss

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.878573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e170e761-371a-4ed8-aeee-ca926ebd2bb0 · outbound

This paper cites Flow map matching with stochastic interpolants: A mathematical framework for consistency models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Flow map matching with stochastic interpolants: A mathematical framework for consistency models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.142948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0af39edf-f8e7-4802-b167-e5c1ad25b1a4 · outbound

This paper cites On the trajectory regularity of ode-based diffusion sampling.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models On the trajectory regularity of ode-based diffusion sampling

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.176250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 345bb909-f3de-46f0-b69e-0da237d7da6c · outbound

This paper cites Accelerating diffusion models with parallel sampling: Inference at sub-linear time complexity.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Accelerating diffusion models with parallel sampling: Inference at sub-linear time complexity

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.155884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7ed8c367-20a5-4e5c-b8a8-f739b760b4a4 · outbound

This paper cites New affine invariant ensemble samplers and their dimensional scaling.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models New affine invariant ensemble samplers and their dimensional scaling

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.865191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c1881a1-e8e2-4ad2-9c58-b2274f5c3e8f · outbound

This paper cites Probabilistic forecasting with stochastic interpolants and F¨ ollmer processes.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Probabilistic forecasting with stochastic interpolants and F¨ ollmer processes

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.167849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f24c030f-198a-4677-8bb4-5f319667af53 · outbound

This paper cites On the contractivity of stochastic interpolation flow.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models On the contractivity of stochastic interpolation flow

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.893550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 109a7f8b-787d-4817-9e88-45ae9145c5cb · outbound

This paper cites Accelerated Diffusion Models via Speculative Sampling.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Accelerated Diffusion Models via Speculative Sampling

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.832789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2c05ba7c-3151-4a97-8eaf-b7ffa3198525 · outbound

This paper cites Diffusion schr¨ odinger bridge with applications to score-based generative modeling.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Diffusion schr¨ odinger bridge with applications to score-based generative modeling

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.149601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 465d3a64-86c7-4cce-8101-d4ff6038ec04 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Diffusion models beat gans on image synthesis

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.173085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a1ad0dd9-fe2c-4015-bba2-ae0e70223048 · outbound

This paper cites Genie: Higher-order denoising diffusion solvers.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Genie: Higher-order denoising diffusion solvers

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.170541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 046d1ffa-0710-4b57-981b-477fdb4f59db · outbound

This paper cites One Step Diffusion via Shortcut Models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models One Step Diffusion via Shortcut Models

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-21T21:54:22.847782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c9ba371-365a-47ce-bfb9-1c092c5d6fdf · outbound

This paper cites Gaussian interpolation flows.Journal of Machine Learning Research, 25(253):1–52.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Gaussian interpolation flows.Journal of Machine Learning Research, 25(253):1–52

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.179367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dab6e1c5-c208-470c-b0e3-baf2fd38059f · outbound

This paper cites Wavelet score-based generative modeling.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Wavelet score-based generative modeling

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.158543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad7fa6be-e301-4d71-8ab2-acc9dcdc6a18 · outbound

This paper cites Mimicking the one-dimensional marginal distributions of processes having an itˆ o differential.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Mimicking the one-dimensional marginal distributions of processes having an itˆ o differential

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.204123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4a65d15d-e67d-4542-8d8c-923db3c55f3b · outbound

This paper cites Ergodicity of the 2d navier-stokes equations with degen- erate stochastic forcing.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Ergodicity of the 2d navier-stokes equations with degen- erate stochastic forcing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.235035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 01c9ff20-3f43-412b-a8ad-5a0e9abde49c · outbound

This paper cites Denoising diffusion probabilistic models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Denoising diffusion probabilistic models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.193240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d535ed84-c6c2-4892-8be9-d2e9b8bf9a36 · outbound

This paper cites Cascaded diffusion models for high fidelity image generation.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Cascaded diffusion models for high fidelity image generation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.198987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bb31db52-513c-4161-8f83-7c4de8193905 · outbound

This paper cites Subspace diffusion gener- ative models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Subspace diffusion gener- ative models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.182313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa523b67-c670-48f7-9ed9-26a1697ed6a4 · outbound

This paper cites Gotta Go Fast When Generating Data with Score-Based Models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Gotta Go Fast When Generating Data with Score-Based Models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.887801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fedacca4-eee0-42d4-afee-cb0c0e2011ff · outbound

This paper cites Elucidating the design space of diffusion- based generative models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Elucidating the design space of diffusion- based generative models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.224387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cf38b1f3-35c9-4cf0-83d9-0efb1d6cf4fa · outbound

This paper cites Consistency trajectory models: Learning probability flow ode trajectory of diffusion.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Consistency trajectory models: Learning probability flow ode trajectory of diffusion

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.209222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 92019a5a-3426-435a-a9ce-8aa7ffc4760b · outbound

This paper cites Variational diffusion models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Variational diffusion models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.190270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 065c7cef-27ee-4af8-b262-1c1ea20a603a · outbound

This paper cites Accelerating Convergence of Score-Based Diffusion Models, Provably.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Accelerating Convergence of Score-Based Diffusion Models, Provably

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.843207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ac6bc98e-8deb-43fe-ae71-3f9a447b37fa · outbound

This paper cites Flow match- ing for generative modeling.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Flow match- ing for generative modeling

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.146607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 94b8e2ec-b3f9-4b44-a7b5-91aad458eb63 · outbound

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

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.232099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 37fe7f4e-90c4-4953-9065-7ea3c08982ef · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.230010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 268f113a-4b92-44f7-ad1a-1486316e74fa · outbound

This paper cites Improved denoising dfiffusion probabilistic models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Improved denoising dfiffusion probabilistic models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.214075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7e276926-e8e6-4f4f-a6b6-d564e959ee27 · outbound

This paper cites Wavelet diffusion models are fast and scalable image generators.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Wavelet diffusion models are fast and scalable image generators

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.139721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f2c69d6c-8e2f-4131-b674-73aab1f1433f · outbound

This paper cites Align your steps: Optimizing sampling schedules in diffusion models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Align your steps: Optimizing sampling schedules in diffusion models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.185385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be9b3698-30bc-4c90-9639-fc7a18e26df5 · outbound

This paper cites Image super-resolution via iterative refinement.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Image super-resolution via iterative refinement

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.152895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a6ee6691-6383-40fa-8e2f-35207a9ceaf9 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Progressive Distillation for Fast Sampling of Diffusion Models

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-21T21:54:22.857950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 38b85b69-5fde-45f2-9566-7cef9e75d3e5 · outbound

This paper cites Noise Estimation for Generative Diffusion Models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Noise Estimation for Generative Diffusion Models

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.827160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 852834d5-fe5b-4a40-b1d3-0de0716f2e88 · outbound

This paper cites Bespoke solvers for generative flow models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Bespoke solvers for generative flow models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.211695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 81e215c8-0aeb-4bf3-aada-d29a3b88f43a · outbound

This paper cites Diffusion schr¨ odinger bridge matching.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Diffusion schr¨ odinger bridge matching

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.216792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1618d092-a14a-4508-8232-75b13c28888d · outbound

This paper cites Deep unsu- pervised learning using nonequilibrium thermodynamics.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Deep unsu- pervised learning using nonequilibrium thermodynamics

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.221579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c6b5495-0d31-4ab3-b32a-15d95274286c · outbound

This paper cites Consistency models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Consistency models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.206799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fd7a1fb7-73f6-42e2-a784-713e64472709 · outbound

This paper cites Generative modeling by estimating gradients of the data distribu- tion.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Generative modeling by estimating gradients of the data distribu- tion

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.201461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 161b8838-4683-4d0f-9ea1-2e370e9d245e · outbound

This paper cites Improved techniques for training score-based generative models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Improved techniques for training score-based generative models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.161301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ac1421ac-73d2-4752-a338-416d41984179 · outbound

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

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-21T21:54:22.873941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c15ad407-3165-4dc2-854f-c7cbbfaca01e · outbound

This paper cites Stork: Improving the fidelity of mid-nfe sampling for diffusion and flow matching models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Stork: Improving the fidelity of mid-nfe sampling for diffusion and flow matching models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.838003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 442897ad-ea06-4540-a507-0d2120915802 · outbound

This paper cites Optimal Scheduling of Dynamic Transport.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Optimal Scheduling of Dynamic Transport

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.869467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a6744f7-af64-499f-89ed-afd61a23780e · outbound

This paper cites Evaluating the design space of diffusion-based generative models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Evaluating the design space of diffusion-based generative models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.219203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 01d0c389-277e-4ebe-9818-91b3c64a068b · outbound

This paper cites Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T21:54:22.853473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1b567f8f-5e45-44a3-b1cb-62304adfadbd · outbound

This paper cites Accelerating diffusion sampling with optimized time steps.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Accelerating diffusion sampling with optimized time steps

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.196405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c9d37150-b5ed-4cfb-a797-fa8babe500f8 · outbound

This paper cites Diffusion models: A comprehensive survey of methods and appli- cations.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Diffusion models: A comprehensive survey of methods and appli- cations

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.187939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 44d836d8-0312-4210-980b-80b2e6b0f39d · outbound

This paper cites Wavelet flow: Fast training of high resolution normalizing flows.

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Wavelet flow: Fast training of high resolution normalizing flows

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:54:23.164479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e1171968-e485-4958-8314-4c1f99be106c · outbound

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

Lipschitz-Guided Design of Interpolation Schedules in Generative Models Fast Sampling of Diffusion Models with Exponential Integrator

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:54:22.883623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation c58355a8-8258-45c6-884f-07ba3e965852 · inbound

Scale-Adaptive Generative Flows for Multiscale Scientific Data cites this paper.

Scale-Adaptive Generative Flows for Multiscale Scientific Data Lipschitz-Guided Design of Interpolation Schedules in Generative Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T11:20:15.793063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa47ce9e-02f4-489d-8d99-1544dce61c17 · inbound

On The Hidden Biases of Flow Matching Samplers cites this paper.

On The Hidden Biases of Flow Matching Samplers Lipschitz-Guided Design of Interpolation Schedules in Generative Models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:02:54.269380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0b54f27-5c8c-4140-889d-7fbfbaa6da16 · inbound

Variational Optimality of F\"ollmer Processes in Generative Diffusions cites this paper.

Variational Optimality of F\"ollmer Processes in Generative Diffusions Lipschitz-Guided Design of Interpolation Schedules in Generative Models

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-21T13:30:11.821112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 97d1ce9b-1285-4339-9e0a-f5078fb26224 · inbound

Geometry-Aware Discretization Error of Diffusion Models cites this paper.

Geometry-Aware Discretization Error of Diffusion Models Lipschitz-Guided Design of Interpolation Schedules in Generative Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:02:54.269380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e88e2f5f-b287-4800-b949-19d37f9d75d0 · inbound

Noise Schedule Design for Diffusion Models: An Optimal Control Perspective cites this paper.

Noise Schedule Design for Diffusion Models: An Optimal Control Perspective Lipschitz-Guided Design of Interpolation Schedules in Generative Models

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-22T08:01:16.070708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8d6e1a8c-5986-4980-8374-771d5bd7a949 · inbound

Two-Parameter Flows for Learning Population Dynamics of Physical Systems cites this paper.

Two-Parameter Flows for Learning Population Dynamics of Physical Systems Lipschitz-Guided Design of Interpolation Schedules in Generative Models

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T22:44:00.995624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ebb813c8-16a1-49d8-937c-d453b3c896f8 · inbound

A Quantitative Approximation Framework for Flow Distillation in Diffusion Models cites this paper.

A Quantitative Approximation Framework for Flow Distillation in Diffusion Models Lipschitz-Guided Design of Interpolation Schedules in Generative Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-02T05:56:40.365370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0708ecfb-3524-4980-ad80-73188a382b84 · inbound

Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning cites this paper.

Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning Lipschitz-Guided Design of Interpolation Schedules in Generative Models

Reference 135

Resolution: metadata mismatch; local_arxiv, observed 2026-06-26T19:29:48.179055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d98299ad-974e-4b94-8e3c-404302078cbb · inbound

Mimicking diffusion processes with differential equations cites this paper.

Mimicking diffusion processes with differential equations Lipschitz-Guided Design of Interpolation Schedules in Generative Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T04:02:43.604591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.