Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Fine-Tuning and Evaluating Open-Source Large Language Models for the Army Domain
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.20297.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T13:07:10.349429Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T15:26:09.475948Z
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 ed9ac893-2661-4f44-9966-5792fe3ac15c · inbound
Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation cites this paper.
Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation Fine-Tuning and Evaluating Open-Source Large Language Models for the Army Domain
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:07:10.349429Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f1ae5eb-996b-47a4-8903-778828311940 · inbound
ARMOR 2025: A Military-Aligned Benchmark for Evaluating Large Language Model Safety Beyond Civilian Contexts cites this paper.
ARMOR 2025: A Military-Aligned Benchmark for Evaluating Large Language Model Safety Beyond Civilian Contexts Fine-Tuning and Evaluating Open-Source Large Language Models for the Army Domain
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:26:09.482648Z
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 1defebdc-146f-4943-ba21-d80582e2a4fc · inbound
RRS-10K: A Multitask Vision-Language Model Benchmark for Rare Remote Sensing Image Interpretation cites this paper.
RRS-10K: A Multitask Vision-Language Model Benchmark for Rare Remote Sensing Image Interpretation Fine-Tuning and Evaluating Open-Source Large Language Models for the Army Domain
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T07:02:02.360414Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.