Paper Citation Record · LEDGER

Fine-Tuning and Evaluating Open-Source Large Language Models for the Army Domain

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.20297.

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

pith.paper-citation-record.v1
2410.20297 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:29:40.794359Z

measured 0 of 1 external citation measurements

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 d7541814-53e9-46d6-8ae9-77e74bb2f578 · inbound

From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System cites this paper.

From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System Fine-Tuning and Evaluating Open-Source Large Language Models for the Army Domain

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T11:29:40.794359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-18T06:34:40.430872+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.