Paper Citation Record · LEDGER

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching

As of 20 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 3 inbound Pith citation observations for arXiv:2507.11842.

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

pith.paper-citation-record.v1
2507.11842 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:05:14.409671Z

measured 22 of 22 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:41:55.266846Z

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

19 of 19 outbound references displayed

verified exact0
verified fuzzy4
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 12f3bc74-8ad1-4036-94f8-73bc57327793 · outbound

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

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:12.837091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e283e73-a263-485d-94b5-f8045e1cc6fd · outbound

This paper cites Latent space representations of cosmological fields.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Latent space representations of cosmological fields

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:12.842353Z

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Observation 0e184abf-4cd4-456a-942e-e5394897cc15 · outbound

This paper cites Advances in neural information processing systems 31 (2018).

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Advances in neural information processing systems 31 (2018)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:14.775239Z

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 c09c42e4-98d8-407c-8d49-467beebf58a5 · outbound

This paper cites SODA: Bottleneck Diffusion Models for Representation Learning.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching SODA: Bottleneck Diffusion Models for Representation Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:13.234629Z

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Observation 7d97044e-32d5-44cf-8c10-154b51b2dbd5 · outbound

This paper cites Flow Matching for Generative Modeling.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Flow Matching for Generative Modeling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:13.431842Z

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Observation 41393bd0-2aa8-446b-a9c2-fd56306c79d5 · outbound

This paper cites The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:13.683830Z

Source-reported events for the cited work

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Observation 7f5e220b-94d6-44c9-a963-d3d11a386f72 · outbound

This paper cites Multifield Cosmology with Artificial Intelligence.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Multifield Cosmology with Artificial Intelligence

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:14.113855Z

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Observation 7c9b6b2d-5883-4e29-be53-42dee2947621 · outbound

This paper cites Advances in Neural Information Processing Systems 36 (2023), 64971–64995.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Advances in Neural Information Processing Systems 36 (2023), 64971–64995

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:14.747386Z

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 04cc12ab-9bb3-445b-8ea5-4128bcafbb3d · outbound

This paper cites Exploring Diffusion Time-steps for Unsupervised Representation Learning.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Exploring Diffusion Time-steps for Unsupervised Representation Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:14.291391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae79a379-bd8d-42c0-9262-e47e0be31e32 · outbound

This paper cites The decoder mirrors the encoder in architecture, and is composed of 4 upsampling convolutions each followed by an inverse GDN layer.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching The decoder mirrors the encoder in architecture, and is composed of 4 upsampling convolutions each followed by an inverse GDN layer

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:14.733193Z

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 c8a39434-92d2-490a-a88b-39b3cc7ec6ae · outbound

This paper cites In Medical image computing and computer-assisted intervention– MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching In Medical image computing and computer-assisted intervention– MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:14.761118Z

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 8aa129cd-e6a4-4788-80a9-c293eecc02a0 · outbound

This paper cites End-to-end Optimized Image Compression.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching End-to-end Optimized Image Compression

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:12.907724Z

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Observation be6c071f-3089-45a8-b68b-24875da04c21 · outbound

This paper cites Variational image compression with a scale hyperprior.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Variational image compression with a scale hyperprior

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:13.040965Z

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Observation 592a8d2b-3f37-4270-9619-63b77b668dbb · outbound

This paper cites Decoupled Weight Decay Regularization.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Decoupled Weight Decay Regularization

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:13.610697Z

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Observation 76d033ff-b7b5-44a8-aa65-a40d35372922 · outbound

This paper cites Denoising Diffusion Implicit Models.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Denoising Diffusion Implicit Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:13.896984Z

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Observation 94549db5-9494-4c1b-85ae-b08cbe9d7ad4 · outbound

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

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Score-Based Generative Modeling through Stochastic Differential Equations

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:14.010962Z

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Observation cfd07257-d07d-4ebc-9de1-c63cd986f414 · outbound

This paper cites Auto-Encoding Variational Bayes.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Auto-Encoding Variational Bayes

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:13.340050Z

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Observation b1e5122c-20dc-4875-9ebe-7ddafda9f86d · outbound

This paper cites 2023), 7459–7481.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching 2023), 7459–7481

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:12.800316Z

Source-reported events for the cited work

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Observation 9b1a9476-797f-4f87-98b6-97bc2c77b386 · outbound

This paper cites Flow Matching Guide and Code.

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Flow Matching Guide and Code

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:13.544526Z

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Pith citing papers

Observation d58e4073-94a9-4567-a324-f000d1d3eec3 · inbound

Cosmo3DFlow: Wavelet Flow Matching for Spatial-to-Spectral Compression in Reconstructing the Early Universe cites this paper.

Cosmo3DFlow: Wavelet Flow Matching for Spatial-to-Spectral Compression in Reconstructing the Early Universe CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T02:41:55.266846Z

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Unavailable: canonical work link unavailable.

Observation d0f52779-ea1f-44bd-bc73-23bcef1d6f93 · inbound

Increasing the Precision of Surrogate Models for Weak Lensing Mass Maps with Flow Matching cites this paper.

Increasing the Precision of Surrogate Models for Weak Lensing Mass Maps with Flow Matching CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:00:19.186134Z

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 9588ac08-a813-4af9-97bb-7852630118c4 · inbound

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions cites this paper.

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T19:11:10.735884Z

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.