Paper Citation Record · LEDGER

On the Stability of Iterative Retraining of Generative Models on their own Data

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.00429.

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

pith.paper-citation-record.v1
2310.00429 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

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

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:59:47.161812Z

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

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d513c4ce-3120-4734-ab2f-5ef4c7fde9ff · inbound

LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs cites this paper.

LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs On the Stability of Iterative Retraining of Generative Models on their own Data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:59:47.161812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44f66040-50f7-4a2e-807b-74db7ac2c898 · inbound

Forgetting is Everywhere cites this paper.

Forgetting is Everywhere On the Stability of Iterative Retraining of Generative Models on their own Data

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T23:42:03.642943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a50f251e-757c-4baa-b816-9e6764c5ca0c · inbound

Epistemic diversity across language models mitigates knowledge collapse cites this paper.

Epistemic diversity across language models mitigates knowledge collapse On the Stability of Iterative Retraining of Generative Models on their own Data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T15:57:13.997236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10be65be-fe0c-4977-a48b-fb97efcbe485 · inbound

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training cites this paper.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training On the Stability of Iterative Retraining of Generative Models on their own Data

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:29.223011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d439304-ac22-49cf-802d-04eea5cd692a · inbound

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data cites this paper.

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data On the Stability of Iterative Retraining of Generative Models on their own Data

Reference 128

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:57:21.501931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a920a8c7-0e48-4ad1-9099-3cf11b2df69f · inbound

Self-Poisoning in Adaptive Out-of-Distribution Detection: A Sharp-Threshold Theory and Certified Label-Free Calibration cites this paper.

Self-Poisoning in Adaptive Out-of-Distribution Detection: A Sharp-Threshold Theory and Certified Label-Free Calibration On the Stability of Iterative Retraining of Generative Models on their own Data

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-01T08:31:08.957172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.