Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.19871.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:35:30.066247Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T21:51:33.225309Z
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 f7e43187-f4bd-4690-9691-daf460efcd6b · inbound
The Challenger: When Do New Data Sources Justify Switching Machine Learning Models? cites this paper.
The Challenger: When Do New Data Sources Justify Switching Machine Learning Models? Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T15:07:33.559584Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a20054a1-e93a-4478-afad-da917771a0bf · inbound
An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness cites this paper.
An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:51:33.227373Z
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.
Observation a1fe8a5e-4118-4e77-94d7-5e36e8e63a7e · inbound
A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance cites this paper.
A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:35:30.066247Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.