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Paper Citation Record · LEDGER

Privacy-Preserving In-Context Learning for Large Language Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2305.01639.

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

pith.paper-citation-record.v1
2305.01639 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 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:50:04.685246Z

measured 0 of 1 external citation measurements

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 c82de657-f8eb-4705-a34b-62d88e5307ad · inbound

RAG with Differential Privacy cites this paper.

RAG with Differential Privacy Privacy-Preserving In-Context Learning for Large Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T00:50:04.685246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:50:04.685246Z digest=sha256:682a612a708210b3ab1b2b32f1eb661521cba4cf7856c27890665a5032f86597

Observation e12c0c87-814e-4150-94ea-a3d6e59210d2 · inbound

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs cites this paper.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Privacy-Preserving In-Context Learning for Large Language Models

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-09T20:51:50.066321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T20:51:50.066321Z digest=sha256:d4b3520d10d49532cfd18fdd4fad177f759a0745c9e33ba8df1dcfdb26f77e94

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-19T06:51:03.385016Z digest=sha256:2d9cfac00793682d467b405dd2a494e2ab12028bc7dabdc6bbb34c6554b84ab8

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-17T22:20:53.561649Z digest=sha256:67e7c59c9be5b40a8473ad481679cbac78284648f963d29624a01aab107c12e3

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-26T04:29:16.386339Z digest=sha256:d13138c0f288fdbd98fe39d535d1599bf09b2c63c488d812fdb3117252ec1c3a