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Paper Citation Record · LEDGER

Generative Modeling via Kernelized Stochastic Interpolants

As of 9 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2602.20070.

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

pith.paper-citation-record.v1
2602.20070 v3

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:32:20.091791Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

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External citation measurements

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Outbound references

Observation 69e1fbc5-74de-472f-9a5f-92c04bed78cc · outbound

This paper cites Deep MMD Gradient Flow without adversarial training.

Generative Modeling via Kernelized Stochastic Interpolants Deep MMD Gradient Flow without adversarial training

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T21:32:19.344045Z digest=sha256:b366ada67f97fab4518cb0fc9bc474a43f0f7173431faea82e141998b4ce2b17

Observation dfc2605d-9ba9-4182-ad0c-e188690c9a28 · outbound

This paper cites Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching.

Generative Modeling via Kernelized Stochastic Interpolants Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching

Reference 6

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source=pdf_text observed=2026-08-02T21:32:19.397296Z digest=sha256:02acc4ec0a7d71eea9ad8725d60dfd67a1466748a5df26b5108144b2fee555f4

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

This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

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

Reference 9

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source=pdf_text observed=2026-08-02T21:32:19.617512Z digest=sha256:1b530458fb6b1371a9653aa8c523140f62df65feb45a4a71ed4758ed43134e2c

Observation 64cf4473-6bbb-4cf9-b032-99c451754316 · outbound

This paper cites Sampling in Unit Time with Kernel Fisher-Rao Flow.

Generative Modeling via Kernelized Stochastic Interpolants Sampling in Unit Time with Kernel Fisher-Rao Flow

Reference 10

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source=pdf_text observed=2026-08-02T21:32:19.682943Z digest=sha256:d823e199cb1df604e54f19200c36bcfa13a12bb3e658490b9c75b1196db7895a

Observation 6be67856-e51a-49ad-a509-da696367a9ca · outbound

This paper cites Jean Morlet, Georges Arens, Eliane Fourgeau, and Dominique Glard.

Generative Modeling via Kernelized Stochastic Interpolants Jean Morlet, Georges Arens, Eliane Fourgeau, and Dominique Glard

Reference 11

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source=pdf_text observed=2026-08-02T21:32:19.761974Z digest=sha256:8bbc6cc7aed0d6c7eafc0d42c82939ccaebbeb7b5a723f70e3983933362ef949

Observation 6792876b-21ea-4040-998a-098668cc9708 · outbound

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

Generative Modeling via Kernelized Stochastic Interpolants Score-Based Generative Modeling through Stochastic Differential Equations

Reference 14

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source=pdf_text observed=2026-08-02T21:32:19.944724Z digest=sha256:53801621a96bc49c93b14500bab7f5a59bb08d42fd489be4be9b235a852d6040

Observation ae4dc37e-bdab-4728-920d-419f1853ee87 · outbound

This paper cites Denoising Data with Measurement Error Using a Reproducing Kernel-based Diffusion Model.

Generative Modeling via Kernelized Stochastic Interpolants Denoising Data with Measurement Error Using a Reproducing Kernel-based Diffusion Model

Reference 15

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source=pdf_text observed=2026-08-02T21:32:20.007936Z digest=sha256:2422b324c9ba60542198e30c45d1cdfb207ec7e74441e8bdb870b59401c11c87

Observation 7484ed44-5f37-4b5d-97a6-7ca8b3e1933f · outbound

This paper cites Proof.Consider the ODE ˙X ∗ t =⟨∇ϕ(X ∗ t ), η∗ t ⟩F withX ∗ 0 =I 0 ∼N(0,Id d), whereη ∗ t solvesE X ∗ t [∇ϕ(X ∗ t )· ∇ϕ(X ∗ t )]η ∗ t =E It [∇ϕ(It)· ˙It].

Generative Modeling via Kernelized Stochastic Interpolants Proof.Consider the ODE ˙X ∗ t =⟨∇ϕ(X ∗ t ), η∗ t ⟩F withX ∗ 0 =I 0 ∼N(0,Id d), whereη ∗ t solvesE X ∗ t [∇ϕ(X ∗ t )· ∇ϕ(X ∗ t )]η ∗ t =E It [∇ϕ(It)· ˙It]

Reference 16

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source=pdf_text observed=2026-08-02T21:32:20.091791Z digest=sha256:63d08349d6c8e70d31c04f92785058c65ed074c294a79821582c3b147f012fcc

Observation 8dae36db-68f7-41ce-bc97-8d25d618706e · outbound

This paper cites ISBN 9781595937643.

Generative Modeling via Kernelized Stochastic Interpolants ISBN 9781595937643

Reference 2007

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source=pdf_text observed=2026-08-02T21:32:19.903034Z digest=sha256:e1175a0e6d7b5bd0646637a2c7dc676f759091b3656e17eb3ce105b3283bf684

Observation 0c5d0ab7-39be-4296-97d3-28a926e9c394 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Generative Modeling via Kernelized Stochastic Interpolants LoRA: Low-Rank Adaptation of Large Language Models

Reference 2015

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source=pdf_text observed=2026-08-02T21:32:19.465065Z digest=sha256:ff5c9cd5ee1f8ec1c810ae9c9edf768c760d8098a19c8c4bc24c5049783baa74

Observation ee7a5a6e-0738-4d79-85b4-ad47d17cc47d · outbound

This paper cites Data exploration of turbulence simulations using a database cluster.

Generative Modeling via Kernelized Stochastic Interpolants Data exploration of turbulence simulations using a database cluster

Reference 2017

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source=pdf_text observed=2026-08-02T21:32:19.808354Z digest=sha256:621eeec81c391d89baefb318b8c058bac6d44b6cfab89ed9930c5a3c8a36ccd2

Observation 6c6f8971-1878-4717-9e30-6422ee87461a · outbound

This paper cites Diffusion soup: Model merging for text-to-image diffusion models.

Generative Modeling via Kernelized Stochastic Interpolants Diffusion soup: Model merging for text-to-image diffusion models

Reference 2019

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source=pdf_text observed=2026-08-02T21:32:19.169214Z digest=sha256:4b5f7dfe7d3e4d0e66b4eb3f2d81c26c422e9ed4e03898bbe6e3a0f6493e6e5b

Observation 49d0e3b3-0fe9-400f-a8c4-61ff8cfee664 · outbound

This paper cites MGD: Moment guided diffusion for maximum entropy generation.arXiv preprint arXiv:2602.17211,.

Generative Modeling via Kernelized Stochastic Interpolants MGD: Moment guided diffusion for maximum entropy generation.arXiv preprint arXiv:2602.17211,

Reference 2021

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source=pdf_text observed=2026-08-02T21:32:19.516453Z digest=sha256:b4c8af2f8a8b6fb919a06873340340f84c5d62d5d2bf5af7af680a491dd30ecf

Observation 2d211fe1-626f-424b-a3a7-56cf8c929dfd · outbound

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

Generative Modeling via Kernelized Stochastic Interpolants Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 2022

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source=pdf_text observed=2026-08-02T21:32:19.117678Z digest=sha256:fa99d645e22118846db589e8d9b51372b9714f59dce5caec49203cea78ca9407

Observation 7749e3bb-2235-4cf5-911f-25a7c2b5bb35 · outbound

This paper cites Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes.

Generative Modeling via Kernelized Stochastic Interpolants Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 2024

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source=pdf_text observed=2026-08-02T21:32:19.272053Z digest=sha256:911693b05d83fb074409c00c57158b6e4688464cec2dba410f0b21ccd677bca8

Observation 5144d2e7-7bc2-4893-83f2-fa0e2e95c742 · outbound

This paper cites Distributional Diffusion Models with Scoring Rules.

Generative Modeling via Kernelized Stochastic Interpolants Distributional Diffusion Models with Scoring Rules

Reference 2025

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source=pdf_text observed=2026-08-02T21:32:19.213205Z digest=sha256:896642844626329930ced972e50239442a9a0153b0ce4bffb264e6238ea9cf69

Pith citing papers

No inbound Pith citation observations are available.