Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2210.06441.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:20:41.160597Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T13:32:37.750784Z
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 28f039fc-f575-4c5a-840f-8ed168ea6fe1 · inbound
Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations cites this paper.
Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:20:41.160597Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 90a86424-9c58-4fba-b2da-1c2b6dd6003c · inbound
Model Merging Scaling Laws in Large Language Models cites this paper.
Model Merging Scaling Laws in Large Language Models How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:32:37.754465Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.