Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2409.09510.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:31:19.652610Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T05:41:23.676877Z
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 c5528a8a-19c7-41bb-9970-bf010d91c0d4 · inbound
NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search cites this paper.
NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:31:19.652610Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a4a652a3-d0b7-415b-8ea6-302ed9c82e49 · inbound
Systematic Optimization of Open Source Large Language Models for Mathematical Reasoning cites this paper.
Systematic Optimization of Open Source Large Language Models for Mathematical Reasoning Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T22:38:09.682521Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cc7e9358-ec10-405a-ac78-6841f9b0f8ef · inbound
Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications cites this paper.
Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:41:23.679864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1f3c036a-d329-419d-b1c2-2136fa7f5979 · inbound
Frustratingly Simple Black-Box Adaptation of Language Models via Logit Bias cites this paper.
Frustratingly Simple Black-Box Adaptation of Language Models via Logit Bias Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T04:28:36.334772Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.