Paper Citation Record · LEDGER

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training

As of 7 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2602.16065.

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

pith.paper-citation-record.v1
2602.16065 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T22:51:31.497576Z

measured 22 of 22 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy0
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cee95578-7f1b-4f8d-bed9-4b5d3e1c6dc9 · outbound

This paper cites an unresolved cited work.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:31.417228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fee4ba4-1f9a-4785-9009-539be2e30a9b · outbound

This paper cites The Rise of AI-Generated Content in Wikipedia.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training The Rise of AI-Generated Content in Wikipedia

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:29.355402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56de18f0-163f-4ff7-ab8d-180ab2c89b04 · outbound

This paper cites Accessed: 2025-06-26.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Accessed: 2025-06-26

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:29.612552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0abc1751-955e-409b-bf97-d44f4cf30030 · outbound

This paper cites Data-Free Knowledge Distillation for Deep Neural Networks.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Data-Free Knowledge Distillation for Deep Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:30.218317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87ac8004-9039-444e-ad09-754fff7b8bd9 · outbound

This paper cites Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:30.546228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 986e5630-acd8-489d-88ee-9c0e0a79ee7b · outbound

This paper cites A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:30.658443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed225bc6-5d12-40b8-bf3d-76e47ddcb511 · outbound

This paper cites The Curse of Recursion: Training on Generated Data Makes Models Forget.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training The Curse of Recursion: Training on Generated Data Makes Models Forget

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:31.062816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 855e7720-495d-4353-bca4-bbb0968c09e4 · outbound

This paper cites Rate of Model Collapse in Recursive Training.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Rate of Model Collapse in Recursive Training

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:31.140572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ce66746-1de4-401b-a93a-e901182230ae · outbound

This paper cites Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution Estimation.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution Estimation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:31.190156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6447816-1037-462d-92d2-2b269a1ced9e · outbound

This paper cites Differentially Private Generative Adversarial Network.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Differentially Private Generative Adversarial Network

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:31.257678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d138cf6-941f-4cd0-aad0-2e387a0c9b19 · outbound

This paper cites At iterationt, a batch of m1 new samples fromP 0 is appended to the dataset, together withm 2 = ((1−α)/α)m 1 synthetic samples generated from the previous iterate bPt−1.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training At iterationt, a batch of m1 new samples fromP 0 is appended to the dataset, together withm 2 = ((1−α)/α)m 1 synthetic samples generated from the previous iterate bPt−1

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:31.497576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0df71f4-7fe1-46a5-8290-a8ed49364dab · outbound

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

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Reference 1959

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:29.726873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c628425b-bc60-48dc-931f-1ccdb861e2f1 · outbound

This paper cites CAD2RL: Real Single-Image Flight without a Single Real Image.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training CAD2RL: Real Single-Image Flight without a Single Real Image

Reference 1976

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:30.768635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d99ac721-78bd-4634-adcf-a95836fbf90f · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Distilling the Knowledge in a Neural Network

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:29.886014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8646fdf0-d8e9-4f19-898f-453c900f48a5 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:30.869458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f782262-4796-4697-a9b9-7f08470d77ee · outbound

This paper cites How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:30.051221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35a2193f-707a-4e97-95d9-c876e61555c1 · outbound

This paper cites The woman worked as a babysitter: On biases in language generation.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training The woman worked as a babysitter: On biases in language generation

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:30.967040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fea741e6-1c88-424c-9151-05bc83426b09 · outbound

This paper cites Bias in Generative AI.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Bias in Generative AI

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:31.337608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d21097e9-9033-42da-9c89-432c69c68e6a · outbound

This paper cites Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:30.382113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 938a8f11-b6cc-4d76-a3d5-4585227e8376 · outbound

This paper cites GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:29.348864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3391c67b-6c9c-466e-a363-954df390cd00 · outbound

This paper cites Convergence of denoising diffusion models under the manifold hypothesis.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Convergence of denoising diffusion models under the manifold hypothesis

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:29.458993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites On the Stability of Iterative Retraining of Generative Models on their own Data.

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.

Pith citing papers

No inbound Pith citation observations are available.