Paper Citation Record · LEDGER

Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2408.04693.

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

pith.paper-citation-record.v1
2408.04693 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:21:31.649792Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T19:43:23.640459Z

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 39772bbb-5a87-4013-9f12-6b7de78da1ff · inbound

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap cites this paper.

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:08:20.810202Z

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 5b5d5af9-26d8-4c6f-930d-c991c2276f06 · inbound

Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading cites this paper.

Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:43:23.643875Z

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 c814d9e2-6f2b-4662-ba0b-92284290a1af · inbound

Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes cites this paper.

Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

Reference 106

Resolution: unresolved; no resolver link, observed 2026-08-07T21:21:31.649792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80206d09-6131-40ca-b326-3c6ff54e3423 · inbound

A Word is Worth 4-bit: Efficient Log Parsing with Binary Coded Decimal Recognition cites this paper.

A Word is Worth 4-bit: Efficient Log Parsing with Binary Coded Decimal Recognition Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:41.735294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7619b72-63c5-4bf3-bdbb-d5505953588d · inbound

Multi-Model Synthetic Training for Mission-Critical Small Language Models cites this paper.

Multi-Model Synthetic Training for Mission-Critical Small Language Models Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:51:34.978639Z

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 5d93993a-d0e3-4def-a012-db8639f7f9a5 · inbound

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting cites this paper.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99770478-6db3-483e-b240-f34bf9c1266c · inbound

Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUs cites this paper.

Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUs Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:25:08.608483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.