Paper Citation Record · LEDGER

A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2108.11018.

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

pith.paper-citation-record.v1
2108.11018 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:06:10.552420Z

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

5
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 9a3d48dc-e949-418d-8c94-bf0a7e8e4607 · inbound

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling cites this paper.

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?

Reference 121

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:45:17.780077Z

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 92670ca0-a4eb-48f8-be1b-702cd1f45d28 · inbound

Lessons from the Trenches on Reproducible Evaluation of Language Models cites this paper.

Lessons from the Trenches on Reproducible Evaluation of Language Models A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:44:49.719947Z

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 9a269385-0574-4191-972b-a4f4ff103b18 · inbound

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data cites this paper.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:10.552420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a3bb4f6-7593-410a-8cf4-7545bdd7e3bd · inbound

Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development cites this paper.

Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?

Reference 130

Resolution: unresolved; no resolver link, observed 2026-08-02T07:40:25.947308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.