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Paper Citation Record · LEDGER

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics

As of 7 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2606.05168.

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

pith.paper-citation-record.v1
2606.05168 v1

Coverage vector

measured 28 of 28 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-12T21:23:44.468570Z

measured 28 of 28 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

28 of 28 outbound references displayed

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Outbound references

Observation e22355e9-879f-4ba0-a399-14676c306756 · outbound

This paper cites Self-Consuming Generative Models Go MAD.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Self-Consuming Generative Models Go MAD

Reference 1

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:c77831abd4a7dcd7c04dd19d90093e53d20589f65b7f70e8bd9864762547b0e8

Observation 7e02c81a-3119-4d2b-855d-297cb07fd6f0 · outbound

This paper cites Dynamical Models of Tuberculosis and Their Applications.Mathematical Biosciences and Engineering, volume 1, pp.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Dynamical Models of Tuberculosis and Their Applications.Mathematical Biosciences and Engineering, volume 1, pp

Reference 2

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:4fa13788663d7f290d3d5f8046546861026a55a179c0a0efc235214e2eee2b86

Observation 618d6c32-e002-49db-b616-b0ab1887e9be · outbound

This paper cites an unresolved cited work.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Unresolved cited work

Reference 3

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:1b3c51727ff7f3deb9de78da2b396deee6d46f08f796f9c11302fefb8fcc56b7

Observation c65e37d2-737d-45c3-b5f3-213b3dff9a4d · outbound

This paper cites Strong Model Collapse.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Strong Model Collapse

Reference 4

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:f42813845576554a06fa01399767eda92374fa4a25857514d7f7e6f2fae1a8a8

Observation 73141138-8704-469f-b00a-6d3bed9e3ebe · outbound

This paper cites Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Reference 5

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:028bb18a2908e1a2ab438ab7bc785daa7d5e7b64172a271dd3d640850527e21a

Observation cb9b4774-9833-46a9-8aa3-a57f506a1ef7 · outbound

This paper cites The Mathematics of Infectious Diseases.SIAM Review, volume 42, pp.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics The Mathematics of Infectious Diseases.SIAM Review, volume 42, pp

Reference 6

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:3729d5a912ebba5985e6ebc1ed5c3dec92842bbf4bd2cd8722c421b9b986e8c9

Observation a8cb3eb6-c4f9-4679-8e2d-3b5f8f356f85 · outbound

This paper cites Epidemiologi- cal Modeling of News and Rumors on Twitter.Proceedings of the Workshop on Social Network Mining and Analysis, pp.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Epidemiologi- cal Modeling of News and Rumors on Twitter.Proceedings of the Workshop on Social Network Mining and Analysis, pp

Reference 7

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:bf6b114c6a16e14014ab1012f82699a89c85aa6686ab8f66a8e4a23a3bac3515

Observation 2c1dbe3d-466a-41d0-8071-1c3ca24a0415 · outbound

This paper cites Measuring and Modeling Computer Virus Prevalence.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Measuring and Modeling Computer Virus Prevalence

Reference 8

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:795aaff5bad281781cf4dfcc01c9487255a9d734f332e375ccb84ed1f40218ae

Observation 16bc07e2-de9f-4780-bacb-a96fe7bd81ea · outbound

This paper cites A Contribution to the Mathematical Theory of Epidemics.Proceedings of the Royal Society of London.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics A Contribution to the Mathematical Theory of Epidemics.Proceedings of the Royal Society of London

Reference 9

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:aee521fc9daa024e238535f2edbd289f3c576a3fbe348b909cbfa86d4155dc72

Observation 632a5ec0-f95b-4225-b8fb-887856c81e54 · outbound

This paper cites A Watermark for Large Language Models.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics A Watermark for Large Language Models

Reference 10

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:b5bfcecf2186225e9c8fbe4f548c23bcebc30bd183a71e7cc51010b2af7b0b21

Observation bc750028-1782-4fd2-b6c4-b7219a74ae6a · outbound

This paper cites Monitoring AI- Modified Content at Scale.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Monitoring AI- Modified Content at Scale

Reference 11

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:673fc3e9680a087c82cba1079f4ed4d0019ee92f943b987759cd6bc00eb24f3b

Observation 190cbd97-1d64-49a7-b860-948616d5b451 · outbound

This paper cites A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity

Reference 12

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:df42cc7f6f8856003970b828d6ea9aa3533253035c269266813b443dadf01c05

Observation 54862341-d493-4ef7-aa6b-274597f84022 · outbound

This paper cites Pointer Sentinel Mixture Models.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Pointer Sentinel Mixture Models

Reference 13

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:928cd9949781c61aae9d81265d551667378c79e03e38509e15534006aea4bd54

Observation dc515572-43e5-4e1d-a02d-f6adcfcc3486 · outbound

This paper cites Model Cards for Model Reporting.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Model Cards for Model Reporting

Reference 14

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:4b07d9dbeacd3e6be058da0ea9fcd9908a8f7b53f4b58919df3e0fdb70b86291

Observation ab75398d-4194-47b8-8dbd-4b4b2daa3f3d · outbound

This paper cites Epidemic Processes in Complex Networks.Reviews of Modern Physics, volume 87, pp.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Epidemic Processes in Complex Networks.Reviews of Modern Physics, volume 87, pp

Reference 15

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Observation 667124e1-f1a7-49ed-8256-96dcbb701cbc · outbound

This paper cites an unresolved cited work.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Unresolved cited work

Reference 16

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:43068b112be14ad71cac28eb18a15a861603951c83528984af679b528fc7efa9

Observation 1450a001-d7ab-4a64-b82c-77e47e3a0ff5 · outbound

This paper cites Language Models are Unsupervised Multitask Learners.OpenAI Blog, 2019.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Language Models are Unsupervised Multitask Learners.OpenAI Blog, 2019

Reference 17

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Observation fb9d8010-3c75-4840-82a2-65f0be2e77fe · outbound

This paper cites Variance Based Sensitivity Analysis of Model Output.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Variance Based Sensitivity Analysis of Model Output

Reference 18

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Observation be72ae39-e5e5-4674-94d4-df177db1afdd · outbound

This paper cites How Bad is Training on Synthetic Data? A Statistical Analysis.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics How Bad is Training on Synthetic Data? A Statistical Analysis

Reference 19

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:8209f20e729e8849b0bd296db270d8add41cb35ead089cca00ccd8b6117b002d

Observation 655447cc-815b-4133-a45a-72c4a67f459f · outbound

This paper cites AI models collapse when trained on recursively generated data.Nature, volume 631, pp.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics AI models collapse when trained on recursively generated data.Nature, volume 631, pp

Reference 20

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:c99e6da3bb5db76f18b4919958ea90235af8cd5e8bb4f47492302ede49d95322

Observation 63a3f436-394a-4684-b263-7fbe20f27f16 · outbound

This paper cites The Science of Detecting LLM-Generated Text.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics The Science of Detecting LLM-Generated Text

Reference 21

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:b5cbc4ed0dd2bd9c3a26ac8978f68ef9f87f35de2f2145ec36f8d2c5b2162eee

Observation 92120aa8-aa04-459d-a944-21d0b1aa39f9 · outbound

This paper cites AI-Generated Content Prevalence in Web Corpora.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics AI-Generated Content Prevalence in Web Corpora

Reference 22

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:9623b608fcd487f4275d98f7f42a4a56191c0b9bfc900eb162142a6c2d715be8

Observation 7ca09481-2e47-438e-a150-a4badfe250f0 · outbound

This paper cites Reproduction Numbers and Sub-threshold Endemic Equilibria for Compartmental Models of Disease Transmission.Mathematical Bio- sciences, volume 180, pp.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Reproduction Numbers and Sub-threshold Endemic Equilibria for Compartmental Models of Disease Transmission.Mathematical Bio- sciences, volume 180, pp

Reference 23

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Observation c7b418f3-43a6-4b51-b20e-3a7ea867939e · outbound

This paper cites The Spread of True and False News Online.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics The Spread of True and False News Online

Reference 24

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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:791e0e9bea22eff5a045667d471762ac99429f6e09b0226ace2c29b59c107153

Observation 96067dd7-092d-4a81-b39d-4ac2e472fc70 · outbound

This paper cites Infection occurs with probabilityp(Bernoulli trial).

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Infection occurs with probabilityp(Bernoulli trial)

Reference 25

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Observation 49dd3c83-7b8a-41b5-961c-0c527d83e410 · outbound

This paper cites 3.Recovery: Each infected node recovers with probabilityγ i per step.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics 3.Recovery: Each infected node recovers with probabilityγ i per step

Reference 26

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Observation 6dfbde08-ff32-4e66-8d0d-1b3ef0e60525 · outbound

This paper cites an unresolved cited work.

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics Unresolved cited work

Reference 27

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Observation 10b608e1-6fb6-44fd-af06-02006342a180 · outbound

This paper cites 20 realizations are run per configuration for 50 time steps (default).

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics 20 realizations are run per configuration for 50 time steps (default)

Reference 28

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Pith citing papers

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