Paper Citation Record · LEDGER

Privacy-Preserving Instructions for Aligning Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.13659.

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

pith.paper-citation-record.v1
2402.13659 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:55:24.531488Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T01:27:31.680002Z

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 439afbff-8488-40ff-bdb5-c81ddcf60480 · inbound

Small Language Models are the Future of Agentic AI cites this paper.

Small Language Models are the Future of Agentic AI Privacy-Preserving Instructions for Aligning Large Language Models

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:55:51.058609Z

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 b965c7f0-186e-4ccf-86f7-c291b94990a3 · 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 Instructions for Aligning Large Language Models

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:52:07.906231Z

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 ded81d64-c4b8-4b6e-acbc-93d23d15e2c1 · inbound

GUARD: Glocal Uncertainty-Aware Robust Decoding for Effective and Efficient Open-Ended Text Generation cites this paper.

GUARD: Glocal Uncertainty-Aware Robust Decoding for Effective and Efficient Open-Ended Text Generation Privacy-Preserving Instructions for Aligning Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T14:55:24.531488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcb3228b-c53d-492f-8595-d82a2f8d42c1 · inbound

CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation cites this paper.

CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation Privacy-Preserving Instructions for Aligning Large Language Models

Reference 127

Resolution: unresolved; no resolver link, observed 2026-08-03T20:29:00.948306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 042e56c1-f948-4226-ab85-e70ced49770f · inbound

PubSwap: Public-Data Off-Policy Coordination for Federated RLVR cites this paper.

PubSwap: Public-Data Off-Policy Coordination for Federated RLVR Privacy-Preserving Instructions for Aligning Large Language Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:21:00.143270Z

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 14934bd4-fcc1-4866-832f-dda84e123d10 · inbound

PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees cites this paper.

PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees Privacy-Preserving Instructions for Aligning Large Language Models

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:27:31.681344Z

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.