Paper Citation Record · LEDGER

Private prediction for large-scale synthetic text generation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.12108.

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

pith.paper-citation-record.v1
2407.12108 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:05:02.478985Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T06:52:07.922463Z

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 d15e446e-47b6-442b-9e5e-0fbcaab2412c · inbound

Scaling Laws for Differentially Private Language Models cites this paper.

Scaling Laws for Differentially Private Language Models Private prediction for large-scale synthetic text generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T22:05:02.478985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecadc3e9-6a3a-43e6-9f61-4831d71b79b4 · inbound

InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy cites this paper.

InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy Private prediction for large-scale synthetic text generation

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:52:07.925161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1195cbc0-b761-45da-8d8f-9aa6dfb96d1b · inbound

Evaluating Differentially Private Generation of Domain-Specific Text cites this paper.

Evaluating Differentially Private Generation of Domain-Specific Text Private prediction for large-scale synthetic text generation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T15:09:03.216788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25297d5b-7998-4b2e-be85-57a4076537d3 · inbound

Differentially-private text generation degrades output language quality cites this paper.

Differentially-private text generation degrades output language quality Private prediction for large-scale synthetic text generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T17:03:11.832776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec8046e3-575e-4022-a586-b0bdeb422911 · inbound

Barriers to Counterfactual Credit Attribution for Autoregressive Models cites this paper.

Barriers to Counterfactual Credit Attribution for Autoregressive Models Private prediction for large-scale synthetic text generation

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T16:46:05.246029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.