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Paper Citation Record · LEDGER

Measuring Non-Adversarial Reproduction of Training Data in Large Language Models

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.10242.

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

pith.paper-citation-record.v1
2411.10242 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T01:40:53.284131Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T02:07:33.490588Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 125ac473-0634-420a-81d4-4ca8f561fee9 · inbound

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs cites this paper.

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs Measuring Non-Adversarial Reproduction of Training Data in Large Language Models

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T12:56:57.472921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T01:40:53.284131Z digest=sha256:9a8da0b7f4d899033921ac29448bb0fae3572fa79c1ac9bbc9adeeec4c4951af

Observation 403014c9-b9b9-47b7-a38c-6620ed20f3eb · inbound

SoK: Colluding Adversaries in Machine Learning Pipelines cites this paper.

SoK: Colluding Adversaries in Machine Learning Pipelines Measuring Non-Adversarial Reproduction of Training Data in Large Language Models

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-03T02:07:33.492459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-27T16:10:51.471822Z digest=sha256:db79f86c9e8735212f473490b414fa9c999d122fafdb00314152828ff68bcd3b