Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Low-Parameter Federated Learning with Large Language Models
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2307.13896.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T04:46:24.059805Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T23:20:59.737521Z
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 b5a42924-bba5-42f9-9905-8263329c9160 · inbound
When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair cites this paper.
When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair Low-Parameter Federated Learning with Large Language Models
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T04:46:24.059805Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d0231e11-4cc1-4c7c-9d09-9a4fd678875c · inbound
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation cites this paper.
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Low-Parameter Federated Learning with Large Language Models
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:36:20.824321Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eada7234-5406-4034-aa7c-e0c7ee34e522 · inbound
A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication cites this paper.
A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication Low-Parameter Federated Learning with Large Language Models
Reference 84
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T23:20:59.824814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.