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Paper Citation Record · LEDGER

Preventing Model Collapse in Gaussian Process Latent Variable Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.01697.

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

pith.paper-citation-record.v1
2404.01697 v2

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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:59:48.470076Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:46:01.902747Z

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 d1c10129-ab63-4b83-a91a-725073fca136 · 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 Preventing Model Collapse in Gaussian Process Latent Variable Models

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T13:59:48.470076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:59:48.470076Z digest=sha256:c99332351dd8f6d424ef36a31112a7a06a81ca7bd67ef0b12b3579e829ffa4ed

Observation d7f35a14-4a48-42b7-94b6-9276d5cd05ce · inbound

Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process cites this paper.

Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process Preventing Model Collapse in Gaussian Process Latent Variable Models

Reference 22

Resolution
verified exact
local_arxiv, observed 2026-08-06T17:46:01.955928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T17:46:01.493178Z digest=sha256:d204bbe5fbfabebe77bceb18a2503867b1d839e0be0e774e609c4904291a2be0