Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Regurgitative Training: The Value of Real Data in Training Large Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.12835.
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 2026-08-04T09:18:34.943574Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T14:44:55.062783Z
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 2f997eef-b5ac-42d3-b5be-0ce54eda6abe · inbound
XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration cites this paper.
XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration Regurgitative Training: The Value of Real Data in Training Large Language Models
Reference 86
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T14:44:55.065152Z
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 70387254-4f81-42c0-8712-7c57faa073f7 · inbound
Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence cites this paper.
Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence Regurgitative Training: The Value of Real Data in Training Large Language Models
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T09:18:34.943574Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.