Paper Citation Record · LEDGER

Geometric Generative Modeling with Noise-Conditioned Graph Networks

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.09391.

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

pith.paper-citation-record.v1
2507.09391 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:08:50.493690Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 8915ded1-9256-44cf-bb63-d3b65850a8c6 · outbound

This paper cites J., Bambrick, J., et al.

Geometric Generative Modeling with Noise-Conditioned Graph Networks J., Bambrick, J., et al

Reference 1

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Observation 4595e105-f94f-4651-8ebf-d845cf65da8f · outbound

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

Geometric Generative Modeling with Noise-Conditioned Graph Networks Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 2

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Observation 4faa8caf-d45a-4a37-8be9-d8f87fe5aa71 · outbound

This paper cites and Yahav, E.

Geometric Generative Modeling with Noise-Conditioned Graph Networks and Yahav, E

Reference 3

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Observation 93dea164-8754-4e4a-a0f9-017a8bc6f17c · outbound

This paper cites Flow network based generative models for non-iterative diverse candidate generation.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Flow network based generative models for non-iterative diverse candidate generation

Reference 4

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Observation 5d88daaa-458e-4b7a-ba26-de2c781d42d9 · outbound

This paper cites Flexivit: One model for all patch sizes.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Flexivit: One model for all patch sizes

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:08:54.151035Z

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Observation fa1a53b3-8a35-45d5-99a3-ff52156a5263 · outbound

This paper cites G., Gut, G., Del Castillo, J.

Geometric Generative Modeling with Noise-Conditioned Graph Networks G., Gut, G., Del Castillo, J

Reference 6

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Observation c7abf911-5f96-4ca4-b70a-76ac951397b6 · outbound

This paper cites DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.

Geometric Generative Modeling with Noise-Conditioned Graph Networks DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Reference 7

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Observation cbf3479c-7473-40cc-97fd-5eabd47091e0 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Imagenet: A large-scale hierarchical image database

Reference 8

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Observation 8ac142ec-df14-489b-8fac-21ac1d838d58 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Fast Graph Representation Learning with PyTorch Geometric

Reference 9

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This paper cites N., Duvenaud, D., Hern \'a ndez-Lobato, J.

Geometric Generative Modeling with Noise-Conditioned Graph Networks N., Duvenaud, D., Hern \'a ndez-Lobato, J

Reference 10

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Observation b021cdd5-2c3f-4350-ad4f-cdae59102813 · outbound

This paper cites Neighborhood attention transformer.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Neighborhood attention transformer

Reference 11

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Observation 3eef9b4e-b9ea-47e1-aac3-036c502c41d2 · outbound

This paper cites Denoising diffusion probabilistic models.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Denoising diffusion probabilistic models

Reference 12

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Observation 5b9b688c-4ea0-42fc-8380-44d02647b349 · outbound

This paper cites Torsional diffusion for molecular conformer generation.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Torsional diffusion for molecular conformer generation

Reference 13

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Observation 6463ed8a-d2ef-40c7-b769-ec85d019345d · outbound

This paper cites EigenFold: Generative Protein Structure Prediction with Diffusion Models.

Geometric Generative Modeling with Noise-Conditioned Graph Networks EigenFold: Generative Protein Structure Prediction with Diffusion Models

Reference 14

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Observation 22b79cde-9e1f-4de2-9a82-fcbb1d1608ec · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Highly accurate protein structure prediction with alphafold

Reference 15

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Observation 388e944a-e675-4e4f-a395-22605c4ff2f8 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Semi-Supervised Classification with Graph Convolutional Networks

Reference 16

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Observation a5308250-79f1-4b37-9b20-32d4221f8350 · outbound

This paper cites Neural Operator: Graph Kernel Network for Partial Differential Equations.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 17

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Observation 58f4fd1c-3606-4914-a17d-9bb4afd05097 · outbound

This paper cites Flow Matching for Generative Modeling.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Flow Matching for Generative Modeling

Reference 18

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Observation 1d3094df-790b-4ebe-8593-d6bd8fff6d73 · outbound

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

Geometric Generative Modeling with Noise-Conditioned Graph Networks Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 19

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Observation 8af91383-3e3e-473e-be2b-03f4b022e78c · outbound

This paper cites Predicting molecular conformation via dynamic graph score matching.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Predicting molecular conformation via dynamic graph score matching

Reference 20

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Observation 90acab0d-2595-4374-a0d9-d94d4974e835 · outbound

This paper cites Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures

Reference 21

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Observation 9449b48c-0b18-455f-bbcf-cb13551ed80c · outbound

This paper cites Gromov--wasserstein distances and the metric approach to object matching.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Gromov--wasserstein distances and the metric approach to object matching

Reference 22

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Observation 8754e9ff-b06c-4869-8b72-b2f7bc0d89d2 · outbound

This paper cites A., and Battaglia, P.

Geometric Generative Modeling with Noise-Conditioned Graph Networks A., and Battaglia, P

Reference 23

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Observation 0ab334af-2769-4446-8a9b-f11b957d10e0 · outbound

This paper cites Action matching: Learning stochastic dynamics from samples.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Action matching: Learning stochastic dynamics from samples

Reference 24

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Observation aebca4f3-35d9-4acb-9614-5da1cccbff44 · outbound

This paper cites and Suzuki, T.

Geometric Generative Modeling with Noise-Conditioned Graph Networks and Suzuki, T

Reference 25

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Observation 7fed7791-61af-4791-b498-1e78b1afffb3 · outbound

This paper cites Scalable multimer structure prediction using diffusion models.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Scalable multimer structure prediction using diffusion models

Reference 26

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Observation 73bf0602-322f-46b0-8f63-fa3baa2c682d · outbound

This paper cites and Xie, S.

Geometric Generative Modeling with Noise-Conditioned Graph Networks and Xie, S

Reference 27

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Observation f70c12e8-8f89-4d23-870d-8fba9d489fb6 · outbound

This paper cites stvcr: Reconstructing spatio-temporal dynamics of cell development using optimal transport.

Geometric Generative Modeling with Noise-Conditioned Graph Networks stvcr: Reconstructing spatio-temporal dynamics of cell development using optimal transport

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:08:52.583862Z

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Observation 3aee8b7a-cc6a-490a-b200-1145b9371b99 · outbound

This paper cites D., Weng, C., Hosseinzadeh, S., Yang, D., Pogson, A.

Geometric Generative Modeling with Noise-Conditioned Graph Networks D., Weng, C., Hosseinzadeh, S., Yang, D., Pogson, A

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:08:52.465459Z

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Observation eb426ae7-70da-4cfa-8898-eeee4402542a · outbound

This paper cites Y., Lu, Y., Yao, J., Jing, Z., Min, K.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Y., Lu, Y., Yao, J., Jing, Z., Min, K

Reference 30

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Observation 97d024c0-dee5-4c53-b4cf-cfeab757467b · outbound

This paper cites Digit patterning is controlled by a bmp-sox9-wnt turing network modulated by morphogen gradients.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Digit patterning is controlled by a bmp-sox9-wnt turing network modulated by morphogen gradients

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:08:51.960850Z

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Observation eafd08fd-84bc-4f1d-974b-d00078fd04a7 · outbound

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Geometric Generative Modeling with Noise-Conditioned Graph Networks and Ermon, S

Reference 32

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This paper cites Trajectorynet: A dynamic optimal transport network for modeling cellular dynamics.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Trajectorynet: A dynamic optimal transport network for modeling cellular dynamics

Reference 33

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Observation 996882b8-2f8a-461f-92ef-2afb93095d1b · outbound

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

Geometric Generative Modeling with Noise-Conditioned Graph Networks Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 34

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Geometric Generative Modeling with Noise-Conditioned Graph Networks Graph Attention Networks

Reference 35

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Observation bb0a3357-f2dc-463d-8198-6f79e3b786df · outbound

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Geometric Generative Modeling with Noise-Conditioned Graph Networks L., Juergens, D., Bennett, N

Reference 36

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This paper cites Demystifying oversmoothing in attention-based graph neural networks.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Demystifying oversmoothing in attention-based graph neural networks

Reference 37

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Observation 1e70346d-b613-45f1-a46c-2f80fef58c30 · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes.

Geometric Generative Modeling with Noise-Conditioned Graph Networks 3d shapenets: A deep representation for volumetric shapes

Reference 38

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Observation 484e09aa-74b0-40c8-8fab-5da53996a30c · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

Geometric Generative Modeling with Noise-Conditioned Graph Networks GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

Reference 39

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This paper cites Pointflow: 3d point cloud generation with continuous normalizing flows.

Geometric Generative Modeling with Noise-Conditioned Graph Networks Pointflow: 3d point cloud generation with continuous normalizing flows

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:08:50.770032Z

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Observation f11f0744-0b5e-4194-85ee-ec7df246ef54 · outbound

This paper cites write newline.

Geometric Generative Modeling with Noise-Conditioned Graph Networks write newline

Reference 41

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