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Paper Citation Record · LEDGER

Rate of Model Collapse in Recursive Training

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2412.17646.

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

pith.paper-citation-record.v1
2412.17646 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:59:49.210447Z

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

0
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 d3feed96-cf1b-4aff-b7b3-ee260e477945 · 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 Rate of Model Collapse in Recursive Training

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T13:59:49.210447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:59:49.210447Z digest=sha256:531ec8048fab47a7bbea1ed74104759e1100981912cd77729cbe168bf288d09e

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

source=pdf_text observed=2026-08-02T22:51:31.140572Z digest=sha256:46eb5cabeb2a9c2e1eb668ab8afdf28ba18641fff64bdcb3486e5bc8e42e434e

Observation 03038d9a-2be1-4464-9476-8047b8c7a150 · inbound

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs cites this paper.

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs Rate of Model Collapse in Recursive Training

Reference 153

Resolution
verified exact
arxiv_id, observed 2026-06-30T01:34:09.480756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-30T01:29:42.919461Z digest=sha256:d6e310d14a97fb87aaf7e22b76751f1bd59c643599dd7d6792eddcf4827b17ab