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Paper Citation Record · LEDGER

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

As of 10 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

Typed states for the displayed outbound observations.

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 2026-08-09T06:31:02.800959+00:00

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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=sha256:677f27ad4100bb416f8bdfe392fadd508c6b7c631eee7ca5e532f19b250f2c08

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=sha256:8be0f26bc5e2ba2b25264a68d894abeac73b1b7ebf7e787ae93638e38f8358a7

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=sha256:2debac2858734b5782e613fada6bd9ce9263e4d6e1060b8f89324a2c56b90979

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=sha256:58a13131978b895981431f07e1068c37a3ee042554d6091fef139529e045e05a

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=sha256:095cca3f4c350ee5cc1e2f4fb315896a8ad0a453df8a28d32fb3bf479f36d53c

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=sha256:0a43ae88d12e3a7fe9e74e9ba750bc0fb9bfd0aa0789d3b58893c9d73a73286c

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=sha256:8b7ec3a2b934d4f8fac4874c04c648dc6552ac2f71f1bdc3a5f189a431360d6a

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:ec91c7e76307d6cbd2e96ce416bf2516dcf06d89b8a1477ff278fa52a93922f1

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:f96924600edd104f53ed076f24b10a8d5302ac4d32141bbd733b76aeec7acafd

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=sha256:6a43b97f8e992c7648fb9acc4d14cd62f70901b13dadb4211d5242950623af99

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:b92ada3e6b1eacaa1f427fddd44806334eec04d1e935c45bd101a2291cdd1fa5

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:b19c74eeb8cc96e7e0b70520ba5aafda633be074e766e6d791718d6974c351a0

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=sha256:6ee21b145d2779d54600209f9af8675d09530b4a6a11ac99db8efcd0c0c2d62d

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=sha256:1a2e0c40497caf01731f02af0ffc76a305fd100a8ce742b9c26fe6f06bc31fa2

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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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:1c7878d4f9f3755bc9180818a12a06814f4c467618fcfacf4e11e233387db323

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=sha256:116549088014efe1bd4a14e05c30034b7eeead0ac087aed3cf88044627c0ed82

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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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:05c2ee810a0bcffb5857828c6cbee70ac7027068af581be3163b64f0c4963970

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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source=pdf_text observed=2026-07-12T21:23:44.468570Z digest=sha256:b7539f2f18c0674b9f5097b166df6d037f75947d4f05d2e07c7dac6e7f147561

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=sha256:5d7261ba4ac0013d3e42821f17a51788e777ee2a6e2651e02fddfd3d9524b2fa

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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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=sha256:31993a3b2f31149db45982a867a3f94c6c370bf35fe9ed91847b4e6f4f62ec4e

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=sha256:682498b365f4a8b88fa98c411f3ae7dc38d5d6d57b0454edbe09d43d55efd444

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=sha256:51171da543596aca6762f6df9900cc131c0c9cd1d9613878b59950c08553c511

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

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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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