Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Privacy-Preserving In-Context Learning for Large Language Models
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.01639.
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 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-26T04:29:16.386339Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T14:09:53.283718Z
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 295b203d-c422-4d14-9d47-5c7d32f72cdb · 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 Privacy-Preserving In-Context Learning for Large Language Models
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T06:52:07.892518Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 10a60d87-c73c-44b2-adfd-861f1b22377c · inbound
SnapAudit: Active Auditing of Differentially Private In-Context Learning via Snapshot-Based Simulation cites this paper.
SnapAudit: Active Auditing of Differentially Private In-Context Learning via Snapshot-Based Simulation Privacy-Preserving In-Context Learning for Large Language Models
Reference 6
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-17T22:22:08.987467Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 0e35297a-5272-40e1-b8b1-506b6b325710 · inbound
Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents cites this paper.
Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents Privacy-Preserving In-Context Learning for Large Language Models
Reference 118
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T14:09:53.285294Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.