Paper Citation Record · LEDGER

A solvable generative model with a linear, one-step denoiser

As of 21 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2411.17807.

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

pith.paper-citation-record.v1
2411.17807 v3

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:01:21.926689Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:28:50.212651Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T10:54:07.982528Z

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy5
unresolved34
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a6b7ad3d-957b-4ecd-8f9e-3880f57b6f96 · outbound

This paper cites Explaining Neural Scaling Laws.

A solvable generative model with a linear, one-step denoiser Explaining Neural Scaling Laws

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.761099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f08e24af-568b-47a2-8fe4-912215dcc377 · outbound

This paper cites Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization.

A solvable generative model with a linear, one-step denoiser Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.765898Z

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Observation 441db321-fd75-4e46-b96a-cfebdfcee63d · outbound

This paper cites Classifier-Free Guidance is a Predictor-Corrector.

A solvable generative model with a linear, one-step denoiser Classifier-Free Guidance is a Predictor-Corrector

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.775052Z

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Unavailable: canonical work link unavailable.

Observation e5a387af-de49-4c5b-b64f-a7e83a5002dd · outbound

This paper cites On the Importance of Noise Scheduling for Diffusion Models.

A solvable generative model with a linear, one-step denoiser On the Importance of Noise Scheduling for Diffusion Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.784199Z

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Unavailable: canonical work link unavailable.

Observation 0a7d84c3-5081-4df2-95b6-f6f5dcf2598b · outbound

This paper cites What does guidance do? A fine-grained analysis in a simple setting.

A solvable generative model with a linear, one-step denoiser What does guidance do? A fine-grained analysis in a simple setting

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.788813Z

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Unavailable: canonical work link unavailable.

Observation 6921097b-ae96-44bc-b45d-f888d38eabcf · outbound

This paper cites Neural Network Field Theories: Non-Gaussianity, Actions, and Locality.

A solvable generative model with a linear, one-step denoiser Neural Network Field Theories: Non-Gaussianity, Actions, and Locality

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.793079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2877ec30-bf06-4ca4-b08c-1a78fef767c7 · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

A solvable generative model with a linear, one-step denoiser Diffusion Models Beat GANs on Image Synthesis

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.797391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 958dd0e5-6c77-43bc-8157-a0e4a5f67ee4 · outbound

This paper cites Universality laws for high-dimensional learning with random features.

A solvable generative model with a linear, one-step denoiser Universality laws for high-dimensional learning with random features

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:01:22.464310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3411f095-a1ae-481e-9bc8-900c297df070 · outbound

This paper cites Scalable Adaptive Computation for Iterative Generation.

A solvable generative model with a linear, one-step denoiser Scalable Adaptive Computation for Iterative Generation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.821238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89047bd0-5d31-4e21-9ca1-69e44610585c · outbound

This paper cites Arthur Jacot, Franck Gabriel, and Cl´ ement Hongler.

A solvable generative model with a linear, one-step denoiser Arthur Jacot, Franck Gabriel, and Cl´ ement Hongler

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:01:22.451505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bc04e6c6-ed4f-4766-b453-07e208b14e19 · outbound

This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

A solvable generative model with a linear, one-step denoiser Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.829061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdb82886-b5c1-47cc-afed-2c2dffe5cea8 · outbound

This paper cites Generalization in diffusion models arises from geometry-adaptive harmonic representations.

A solvable generative model with a linear, one-step denoiser Generalization in diffusion models arises from geometry-adaptive harmonic representations

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.833413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e669ea83-e84a-45e8-a3a5-a36e8e2404cc · outbound

This paper cites An analytic theory of creativity in convolutional diffusion models.

A solvable generative model with a linear, one-step denoiser An analytic theory of creativity in convolutional diffusion models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.837544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c897229-2de3-4e14-b3c1-e38dde8a0b9b · outbound

This paper cites Elucidating the Design Space of Diffusion-Based Generative Models.

A solvable generative model with a linear, one-step denoiser Elucidating the Design Space of Diffusion-Based Generative Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.841836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70251dff-64fe-4db8-b68f-9a7d4b866ce2 · outbound

This paper cites Convergence for score-based generative modeling with polynomial complexity.

A solvable generative model with a linear, one-step denoiser Convergence for score-based generative modeling with polynomial complexity

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.846281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d39f3736-b790-422d-8ea4-6ea9458ad96e · outbound

This paper cites A Solvable Model of Neural Scaling Laws.

A solvable generative model with a linear, one-step denoiser A Solvable Model of Neural Scaling Laws

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.856440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4b8d289-35a0-4a99-bc01-9a7d9cd5b533 · outbound

This paper cites doi: 10.1073/pnas.1806579115.

A solvable generative model with a linear, one-step denoiser doi: 10.1073/pnas.1806579115

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.861312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abd1c615-f8fe-4487-86a9-233687768668 · outbound

This paper cites More Data Can Hurt for Linear Regression: Sample-wise Double Descent.

A solvable generative model with a linear, one-step denoiser More Data Can Hurt for Linear Regression: Sample-wise Double Descent

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.865366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38bf5865-2f47-4bd7-a087-745671341fb7 · outbound

This paper cites Improved Denoising Diffusion Probabilistic Models.

A solvable generative model with a linear, one-step denoiser Improved Denoising Diffusion Probabilistic Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.870039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7a7f54e-a9fe-4147-a1b4-d6159c8585b2 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

A solvable generative model with a linear, one-step denoiser Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.874175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93da91c2-3440-4a14-bc17-84ca3b415937 · outbound

This paper cites doi: 10.1017/9781009023405.

A solvable generative model with a linear, one-step denoiser doi: 10.1017/9781009023405

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.877801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 584237e3-3d2b-425b-bdd5-c0088001139a · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

A solvable generative model with a linear, one-step denoiser High-Resolution Image Synthesis with Latent Diffusion Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.882696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dce61e3-99eb-4716-8166-c042494fd4de · outbound

This paper cites Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.

A solvable generative model with a linear, one-step denoiser Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.892184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f8128d3-f482-4eea-a7e1-71308d33c80c · outbound

This paper cites Learning Mixtures of Gaussians Using the DDPM Objective.

A solvable generative model with a linear, one-step denoiser Learning Mixtures of Gaussians Using the DDPM Objective

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.896651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65ee2a81-0243-4332-8f43-6e4ef8159318 · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

A solvable generative model with a linear, one-step denoiser Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.901133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 570cf89b-ea6b-4efa-b5de-082b04677b02 · outbound

This paper cites Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole.

A solvable generative model with a linear, one-step denoiser Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:01:22.425345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c7f83dee-a7a3-4ef0-9dbf-32cc867360c8 · outbound

This paper cites Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models.

A solvable generative model with a linear, one-step denoiser Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.915161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3b9c1dd-87ac-492b-813d-3099cb0d50f5 · outbound

This paper cites TaeHo Yoon, Joo Young Choi, Sehyun Kwon, and Ernest K.

A solvable generative model with a linear, one-step denoiser TaeHo Yoon, Joo Young Choi, Sehyun Kwon, and Ernest K

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:01:22.411871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9e2ea0d9-3927-488c-b382-50c9221a37fc · outbound

This paper cites The Emergence of Reproducibility and Generalizability in Diffusion Models.

A solvable generative model with a linear, one-step denoiser The Emergence of Reproducibility and Generalizability in Diffusion Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.922740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 297cd266-ff9a-4aef-9de0-42f920a7229b · outbound

This paper cites Diffusion Normalizing Flow.

A solvable generative model with a linear, one-step denoiser Diffusion Normalizing Flow

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.926689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfb3e229-9056-42c8-8905-7824f0b7fe2b · outbound

This paper cites 22 A solvable generative model with a linear, one-step denoiser Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, and Yuting Wei.

A solvable generative model with a linear, one-step denoiser 22 A solvable generative model with a linear, one-step denoiser Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, and Yuting Wei

Reference 2011

Resolution: malformed identifier; no resolver link, observed 2026-08-12T12:01:21.910839Z

Source-reported events for the cited work

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Observation e3b566e8-7413-4d00-af13-5b2a9914e16f · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

A solvable generative model with a linear, one-step denoiser U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.887660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4d9c465-295e-4787-8378-6c61d003d132 · outbound

This paper cites URL https://doi.org/10.3150/14-BEJ609.

A solvable generative model with a linear, one-step denoiser URL https://doi.org/10.3150/14-BEJ609

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.802019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93d8d3fd-35c7-4025-ace1-bf8eb5788528 · outbound

This paper cites URL https://doi.org/10.1214/17-AOS1549.

A solvable generative model with a linear, one-step denoiser URL https://doi.org/10.1214/17-AOS1549

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.806183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0410298f-f478-4c63-a0ef-d89f03a8f1a6 · outbound

This paper cites Marvin Li and Sitan Chen.

A solvable generative model with a linear, one-step denoiser Marvin Li and Sitan Chen

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:01:22.438430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 83cde080-2aac-467e-9949-adf6f0afefc1 · outbound

This paper cites How Compositional Generalization and Creativity Improve as Diffusion Models are Trained.

A solvable generative model with a linear, one-step denoiser How Compositional Generalization and Creativity Improve as Diffusion Models are Trained

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.809967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92174a88-317b-47b6-8ec5-4a47599c5aa2 · outbound

This paper cites Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks.

A solvable generative model with a linear, one-step denoiser Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.770518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8930e371-0787-40ce-b1cd-f83e1a157426 · outbound

This paper cites Classifier-Free Diffusion Guidance.

A solvable generative model with a linear, one-step denoiser Classifier-Free Diffusion Guidance

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.813722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25dc32df-5a1c-4397-864f-a7f0878401d8 · outbound

This paper cites Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions.

A solvable generative model with a linear, one-step denoiser Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.779618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 590ce95a-ccec-4605-b282-4e94fcb2b5ac · outbound

This paper cites Scaling and renormalization in high-dimensional regression.

A solvable generative model with a linear, one-step denoiser Scaling and renormalization in high-dimensional regression

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T12:01:21.756132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e140d5ea-1b00-44e8-900d-4ee73d66c4cd · inbound

Diffusion models under low-noise regime cites this paper.

Diffusion models under low-noise regime A solvable generative model with a linear, one-step denoiser

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:28:50.212651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5529318b-ff46-4fc0-8118-4bab545363d5 · inbound

Diffusion Models Memorize in Training -- and Generalize in Inference cites this paper.

Diffusion Models Memorize in Training -- and Generalize in Inference A solvable generative model with a linear, one-step denoiser

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.984585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.