Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Adaptively Private Next-Token Prediction of Large Language Models
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.02016.
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 2026-08-07T10:47:28.294391Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T06:52:07.877480Z
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 ecc44196-f5bf-4b13-8fcb-1f2473f13400 · inbound
Clustering and Median Aggregation Improve Differentially Private Inference cites this paper.
Clustering and Median Aggregation Improve Differentially Private Inference Adaptively Private Next-Token Prediction of Large Language Models
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:47:28.294391Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1e949fb0-d86e-4ff6-af42-8ba9f2d8e35d · 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 Adaptively Private Next-Token Prediction of Large Language Models
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T06:52:07.879519Z
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.
Observation a61eb0bf-2722-49e1-93f6-ca8cfa0570c7 · inbound
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy cites this paper.
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy Adaptively Private Next-Token Prediction of Large Language Models
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T18:52:51.853837Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.