Paper Citation Record · LEDGER

FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge into LLMs?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2411.05059.

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

pith.paper-citation-record.v1
2411.05059 v2

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 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:05:45.491178Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:26:48.051054Z

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 fd2586f8-4202-4c76-92c1-6f458ae2bf17 · inbound

Can Large Language Models Match the Conclusions of Systematic Reviews? cites this paper.

Can Large Language Models Match the Conclusions of Systematic Reviews? FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge into LLMs?

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:05:45.491178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4249c5f9-cf77-48c2-8599-12f34c810418 · inbound

Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning cites this paper.

Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge into LLMs?

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:20:49.389940Z

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 23a69f12-b9f3-4c4d-beef-93f681c24f95 · 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 FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge into LLMs?

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:41:23.743689Z

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 1fab1e6a-b5dc-49d6-8b79-80925443a041 · inbound

Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison cites this paper.

Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge into LLMs?

Reference 132

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T08:26:48.052423Z

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.