Paper Citation Record · LEDGER

An analytic theory of creativity in convolutional diffusion models

As of 18 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 33 inbound Pith citation observations for arXiv:2412.20292.

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

pith.paper-citation-record.v1
2412.20292 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:32:26.821828Z

measured 85 of 85 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:36:32.081606Z

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

52 of 52 outbound references displayed

verified exact1
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External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 772c271b-4c9b-47db-a242-7130c67c1a30 · outbound

This paper cites write newline.

An analytic theory of creativity in convolutional diffusion models write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T23:32:26.625404Z

Source-reported events for the cited work

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Observation 2da9481d-e47e-4407-be36-9957c76f9c68 · outbound

This paper cites Diffusion models in de novo drug design.

An analytic theory of creativity in convolutional diffusion models Diffusion models in de novo drug design

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:32:27.326692Z

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Observation 9a6d6ff8-4133-48bc-b645-7534c3fc8e2d · outbound

This paper cites In search of dispersed memories: Generative diffusion models are associative memory networks.

An analytic theory of creativity in convolutional diffusion models In search of dispersed memories: Generative diffusion models are associative memory networks

Reference 3

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Observation abac8e09-5acc-4835-80d5-cdd506238e50 · outbound

This paper cites Nearly d-linear convergence bounds for diffusion models via stochastic localization.

An analytic theory of creativity in convolutional diffusion models Nearly d-linear convergence bounds for diffusion models via stochastic localization

Reference 4

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Observation 5714a4ca-6354-499a-95ec-b7fed3cedd72 · outbound

This paper cites an unresolved cited work.

An analytic theory of creativity in convolutional diffusion models Unresolved cited work

Reference 5

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Observation b947f839-59ae-4949-8e28-4a3f5d131d47 · outbound

This paper cites Dynamical Regimes of Diffusion Models.

An analytic theory of creativity in convolutional diffusion models Dynamical Regimes of Diffusion Models

Reference 6

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Source-reported events for the cited work

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Observation 4e677c0a-7688-4280-b60e-12f6bc44efad · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

An analytic theory of creativity in convolutional diffusion models Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T23:32:26.649418Z

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Observation 62ef7639-c80f-4b24-9cf1-451cb11b8930 · outbound

This paper cites Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data.

An analytic theory of creativity in convolutional diffusion models Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data

Reference 8

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Observation 5bdfda86-0ebf-4774-93ae-27408eaae10a · outbound

This paper cites and Welling, M.

An analytic theory of creativity in convolutional diffusion models and Welling, M

Reference 9

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Observation fe595496-7a92-4d76-9969-d865930402b6 · outbound

This paper cites and Zdeborová, L.

An analytic theory of creativity in convolutional diffusion models and Zdeborová, L

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:32:27.275162Z

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Observation feb345f9-8962-4611-8fd2-86711cb67589 · outbound

This paper cites Analysis of learning a flow-based generative model from limited sample complexity.

An analytic theory of creativity in convolutional diffusion models Analysis of learning a flow-based generative model from limited sample complexity

Reference 11

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Observation 97c2345d-f7c3-4def-9f72-6dd6d4da7beb · outbound

This paper cites Convergence of denoising diffusion models under the manifold hypothesis.

An analytic theory of creativity in convolutional diffusion models Convergence of denoising diffusion models under the manifold hypothesis

Reference 12

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Observation b0f7b00f-250a-46b5-997c-b971faf2a8d0 · outbound

This paper cites and Nichol, A.

An analytic theory of creativity in convolutional diffusion models and Nichol, A

Reference 13

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An analytic theory of creativity in convolutional diffusion models Unresolved cited work

Reference 14

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Observation d7063638-9f1e-43d8-9afd-cd2748d31d38 · outbound

This paper cites On Memorization in Diffusion Models.

An analytic theory of creativity in convolutional diffusion models On Memorization in Diffusion Models

Reference 15

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Observation 77a688a2-e7a5-4441-8a54-0552f7825da5 · outbound

This paper cites Deep residual learning for image recognition.

An analytic theory of creativity in convolutional diffusion models Deep residual learning for image recognition

Reference 16

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Observation 8fd6d30a-6308-4a7e-8677-e500091eab90 · outbound

This paper cites Denoising diffusion probabilistic models.

An analytic theory of creativity in convolutional diffusion models Denoising diffusion probabilistic models

Reference 17

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Observation 510ec220-4066-40e4-a32b-e603a21c7148 · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

An analytic theory of creativity in convolutional diffusion models Imagen Video: High Definition Video Generation with Diffusion Models

Reference 18

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Observation ae585d9d-5f1e-478f-b98d-dc40136bc302 · outbound

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An analytic theory of creativity in convolutional diffusion models Unresolved cited work

Reference 19

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Observation 916dc0a8-fd02-42f6-96fc-3ebbc62c0f4e · outbound

This paper cites G., Vignac, C., and Welling, M.

An analytic theory of creativity in convolutional diffusion models G., Vignac, C., and Welling, M

Reference 20

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Observation 7afcaafe-2176-4097-a6fc-4b74dd9407ad · outbound

This paper cites Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models.

An analytic theory of creativity in convolutional diffusion models Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models

Reference 21

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This paper cites Learning multi-scale local conditional probability models of images.

An analytic theory of creativity in convolutional diffusion models Learning multi-scale local conditional probability models of images

Reference 22

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Observation 89f8a532-31f9-48dc-85dc-d90f3719d8a8 · outbound

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

An analytic theory of creativity in convolutional diffusion models Generalization in diffusion models arises from geometry-adaptive harmonic representations

Reference 23

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Observation 76c5f263-d32d-46a3-b81c-8dd4ae5ffec5 · outbound

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

An analytic theory of creativity in convolutional diffusion models Convergence for score-based generative modeling with polynomial complexity

Reference 24

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Observation 0dfbd00c-3e0d-4e5c-b9d7-d0c5e26dffba · outbound

This paper cites S., and Hashimoto, T.

An analytic theory of creativity in convolutional diffusion models S., and Hashimoto, T

Reference 25

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Observation b9f756f7-40e1-43e7-96a5-7520d31b4c67 · outbound

This paper cites Diffusion Models for Non-autoregressive Text Generation: A Survey.

An analytic theory of creativity in convolutional diffusion models Diffusion Models for Non-autoregressive Text Generation: A Survey

Reference 26

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This paper cites Detecting Multimedia Generated by Large AI Models: A Survey.

An analytic theory of creativity in convolutional diffusion models Detecting Multimedia Generated by Large AI Models: A Survey

Reference 27

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Observation f6b3edb7-281c-4aa3-a759-cd34b4b8b8f7 · outbound

This paper cites Flow Matching for Generative Modeling.

An analytic theory of creativity in convolutional diffusion models Flow Matching for Generative Modeling

Reference 28

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An analytic theory of creativity in convolutional diffusion models Unresolved cited work

Reference 29

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Observation ffcbb211-b449-449c-b2f4-b8d7ee9c6874 · outbound

This paper cites Towards a Mechanistic Explanation of Diffusion Model Generalization.

An analytic theory of creativity in convolutional diffusion models Towards a Mechanistic Explanation of Diffusion Model Generalization

Reference 30

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An analytic theory of creativity in convolutional diffusion models S., Dick, R., and Tanaka, H

Reference 31

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This paper cites Diffusion models are minimax optimal distribution estimators.

An analytic theory of creativity in convolutional diffusion models Diffusion models are minimax optimal distribution estimators

Reference 32

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Observation 85b41a1c-9579-470a-82f3-985332110d46 · outbound

This paper cites and Xie, S.

An analytic theory of creativity in convolutional diffusion models and Xie, S

Reference 33

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This paper cites J., Ambrogioni, L., and Krotov, D.

An analytic theory of creativity in convolutional diffusion models J., Ambrogioni, L., and Krotov, D

Reference 34

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Observation 4b561594-3707-44c5-b000-0649d9817430 · outbound

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

An analytic theory of creativity in convolutional diffusion models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 35

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An analytic theory of creativity in convolutional diffusion models High-resolution image synthesis with latent diffusion models

Reference 36

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An analytic theory of creativity in convolutional diffusion models U-net: Convolutional networks for biomedical image segmentation

Reference 37

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An analytic theory of creativity in convolutional diffusion models Closed-Form Diffusion Models

Reference 38

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An analytic theory of creativity in convolutional diffusion models A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data

Reference 39

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An analytic theory of creativity in convolutional diffusion models Minimal implementation of diffusion models

Reference 40

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Observation 58ff5aa7-5768-40c8-8edc-d9b40c55675f · outbound

This paper cites Rethinking the spatial inconsistency in classifier-free diffusion guidance.

An analytic theory of creativity in convolutional diffusion models Rethinking the spatial inconsistency in classifier-free diffusion guidance

Reference 41

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

An analytic theory of creativity in convolutional diffusion models Deep unsupervised learning using nonequilibrium thermodynamics

Reference 42

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An analytic theory of creativity in convolutional diffusion models Diffusion art or digital forgery? investigating data replication in diffusion models

Reference 43

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Observation b8352d46-edc9-4a54-a529-0d2010cc0fc3 · outbound

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An analytic theory of creativity in convolutional diffusion models Denoising Diffusion Implicit Models

Reference 44

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An analytic theory of creativity in convolutional diffusion models and Ermon, S

Reference 45

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An analytic theory of creativity in convolutional diffusion models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 46

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An analytic theory of creativity in convolutional diffusion models Manifolds, Random Matrices and Spectral Gaps: The geometric phases of generative diffusion

Reference 47

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An analytic theory of creativity in convolutional diffusion models and Vastola, J

Reference 48

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Observation 930e9308-789d-4785-a05a-0a06c95d426d · outbound

This paper cites Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later.

An analytic theory of creativity in convolutional diffusion models Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later

Reference 49

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This paper cites Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions.

An analytic theory of creativity in convolutional diffusion models Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions

Reference 50

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An analytic theory of creativity in convolutional diffusion models L., Juergens, D., Bennett, N

Reference 51

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An analytic theory of creativity in convolutional diffusion models The emergence of reproducibility and consistency in diffusion models

Reference 52

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Pith citing papers

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A solvable generative model with a linear, one-step denoiser cites this paper.

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

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Towards a Mechanistic Explanation of Diffusion Model Generalization cites this paper.

Towards a Mechanistic Explanation of Diffusion Model Generalization An analytic theory of creativity in convolutional diffusion models

Reference 2024

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Compositional Generalization via Forced Rendering of Disentangled Latents cites this paper.

Compositional Generalization via Forced Rendering of Disentangled Latents An analytic theory of creativity in convolutional diffusion models

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Density Ratio Estimation with Conditional Probability Paths cites this paper.

Density Ratio Estimation with Conditional Probability Paths An analytic theory of creativity in convolutional diffusion models

Reference 25

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Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models cites this paper.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models An analytic theory of creativity in convolutional diffusion models

Reference 24

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An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Models cites this paper.

An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Models An analytic theory of creativity in convolutional diffusion models

Reference 25

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Observation 8859bc1a-94c2-4fb7-a10d-e3da8583f9ae · inbound

Generalization through variance: how noise shapes inductive biases in diffusion models cites this paper.

Generalization through variance: how noise shapes inductive biases in diffusion models An analytic theory of creativity in convolutional diffusion models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T12:36:32.081606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edb9b9ea-c38b-47f2-8ed5-0b22fa3b0bac · inbound

Predicting Forced Responses of Probability Distributions via the Fluctuation-Dissipation Theorem and Generative Modeling cites this paper.

Predicting Forced Responses of Probability Distributions via the Fluctuation-Dissipation Theorem and Generative Modeling An analytic theory of creativity in convolutional diffusion models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T12:17:15.408361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 066ad954-f557-456a-b4dc-4eb8f98807f9 · inbound

Resolving Memorization in Empirical Diffusion Model for Manifold Data in High-Dimensional Spaces cites this paper.

Resolving Memorization in Empirical Diffusion Model for Manifold Data in High-Dimensional Spaces An analytic theory of creativity in convolutional diffusion models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T01:03:33.555266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 346fd06d-57f1-48df-a048-332ef855b60e · inbound

Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models cites this paper.

Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models An analytic theory of creativity in convolutional diffusion models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:51.559529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 263fe1e0-4dc0-44d6-bbae-5a84bc271634 · inbound

GuessBench: Sensemaking Multimodal Creativity in the Wild cites this paper.

GuessBench: Sensemaking Multimodal Creativity in the Wild An analytic theory of creativity in convolutional diffusion models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:01:41.769245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4950ea7c-ef66-402a-827a-8630bfa62772 · inbound

Ambient Diffusion Omni: Training Good Models with Bad Data cites this paper.

Ambient Diffusion Omni: Training Good Models with Bad Data An analytic theory of creativity in convolutional diffusion models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:14.266952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08fc5261-a3af-4061-9e5a-7cce98455938 · inbound

Local Learning Rules for Out-of-Equilibrium Physical Generative Models cites this paper.

Local Learning Rules for Out-of-Equilibrium Physical Generative Models An analytic theory of creativity in convolutional diffusion models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:11.114673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e9f6f45-ef9c-40aa-a7f9-0836ef89fc88 · inbound

Spooky Action at a Distance: Normalization Layers Enable Side-Channel Spatial Communication cites this paper.

Spooky Action at a Distance: Normalization Layers Enable Side-Channel Spatial Communication An analytic theory of creativity in convolutional diffusion models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T19:46:07.094069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bfa087b-a96a-437c-9afb-9866830850d6 · inbound

Local Diffusion Models and Phases of Data Distributions cites this paper.

Local Diffusion Models and Phases of Data Distributions An analytic theory of creativity in convolutional diffusion models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T23:36:54.654794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f11127d9-15c7-4eed-9100-e172b8e1d893 · inbound

Score-based Membership Inference on Diffusion Models cites this paper.

Score-based Membership Inference on Diffusion Models An analytic theory of creativity in convolutional diffusion models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:36:22.542646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dfd05fc4-9c2f-47f6-91b5-82076471024c · inbound

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling cites this paper.

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling An analytic theory of creativity in convolutional diffusion models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:11:20.706342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a2a5aa5-82a3-409a-b543-7dfe0e034d13 · inbound

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling cites this paper.

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling An analytic theory of creativity in convolutional diffusion models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T16:56:30.904893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37dbc0fb-f760-4a1e-b5b8-c4075851a3cd · inbound

A Random Matrix Theory Perspective on the Consistency of Diffusion Models cites this paper.

A Random Matrix Theory Perspective on the Consistency of Diffusion Models An analytic theory of creativity in convolutional diffusion models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T05:17:22.790370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b778e81-69ae-4cc7-a3e8-6de061d3c2a7 · inbound

A Random Matrix Theory Perspective on the Consistency of Diffusion Models cites this paper.

A Random Matrix Theory Perspective on the Consistency of Diffusion Models An analytic theory of creativity in convolutional diffusion models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T05:17:22.711308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ecb3e9a-c028-40e6-ac94-f2a69753edf7 · inbound

Momentum Guidance: Plug-and-Play Guidance for Flow Models cites this paper.

Momentum Guidance: Plug-and-Play Guidance for Flow Models An analytic theory of creativity in convolutional diffusion models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T21:26:59.580841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e968bcad-87f7-4717-80fe-e2f2124da6c6 · inbound

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

Diffusion Models Memorize in Training -- and Generalize in Inference An analytic theory of creativity in convolutional diffusion models

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.850284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9673363e-e3e8-4371-934d-fac30a2d26f7 · inbound

A Minimal Model of Representation Collapse: Frustration, Stop-Gradient, and Dynamics cites this paper.

A Minimal Model of Representation Collapse: Frustration, Stop-Gradient, and Dynamics An analytic theory of creativity in convolutional diffusion models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:30:57.489350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e8c197ea-3d53-4650-9836-7a0b8ac29e68 · inbound

Generalization in LLM Problem Solving: The Case of the Shortest Path cites this paper.

Generalization in LLM Problem Solving: The Case of the Shortest Path An analytic theory of creativity in convolutional diffusion models

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T10:39:38.216807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 808f2e6a-81e9-4658-a475-b914e444f2ad · inbound

Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data cites this paper.

Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data An analytic theory of creativity in convolutional diffusion models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:11:27.281107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e8c71984-eb6f-44cb-a05f-c39d13411e80 · inbound

When Do Diffusion Models learn to Generate Multiple Objects? cites this paper.

When Do Diffusion Models learn to Generate Multiple Objects? An analytic theory of creativity in convolutional diffusion models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-01T08:15:32.086639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b1a053e-003a-4eff-87e6-330d6b5d7320 · inbound

Concurrence of Symmetry Breaking and Nonlocality Phase Transitions in Diffusion Models cites this paper.

Concurrence of Symmetry Breaking and Nonlocality Phase Transitions in Diffusion Models An analytic theory of creativity in convolutional diffusion models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:36:04.457825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 881a0c71-dd5c-406f-8cb9-232fea7e7c62 · inbound

Flow-Direct: Feedback-Efficient and Reusable Guidance for Flow Models via Non-Parametric Guidance Field cites this paper.

Flow-Direct: Feedback-Efficient and Reusable Guidance for Flow Models via Non-Parametric Guidance Field An analytic theory of creativity in convolutional diffusion models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:23:47.746855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f505f440-273a-4e71-bec4-2924befbf161 · inbound

Mechanisms of Misgeneralization in Physical Sequence Modeling cites this paper.

Mechanisms of Misgeneralization in Physical Sequence Modeling An analytic theory of creativity in convolutional diffusion models

Reference 106

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:44:48.771640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1f8ab69a-3aa0-4d45-b776-bf4d048cf761 · inbound

Training-Free Imitation Learning with Closed-Form Diffusion Policies cites this paper.

Training-Free Imitation Learning with Closed-Form Diffusion Policies An analytic theory of creativity in convolutional diffusion models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-28T17:12:24.904229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 653dfbfc-d1f7-4dfe-bb8e-777e2049609a · inbound

Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory cites this paper.

Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory An analytic theory of creativity in convolutional diffusion models

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:46:55.243902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bf495850-7beb-48eb-8089-a07374930434 · inbound

Unsupervised Causal Abstractions Discovery cites this paper.

Unsupervised Causal Abstractions Discovery An analytic theory of creativity in convolutional diffusion models

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T20:49:57.417203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 53a221c2-71ce-4987-a852-4d8da12d1146 · inbound

Toward a mechanistic understanding of inference in visual cortex and diffusion models cites this paper.

Toward a mechanistic understanding of inference in visual cortex and diffusion models An analytic theory of creativity in convolutional diffusion models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-01T22:40:53.458793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.