Paper Citation Record · LEDGER

Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.10930.

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

pith.paper-citation-record.v1
2407.10930 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 2026-08-06T22:05:36.753644Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T05:19:45.781488Z

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 11a0699d-8b6a-468d-ba08-696ebbd53d14 · inbound

Evaluating Hybrid Retrieval Augmented Generation using Dynamic Test Sets: LiveRAG Challenge cites this paper.

Evaluating Hybrid Retrieval Augmented Generation using Dynamic Test Sets: LiveRAG Challenge Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:36.753644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 125093d4-d047-463e-a53e-f10a5fe0ea33 · inbound

Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models cites this paper.

Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T17:44:49.988365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c25b548-3a11-4063-bf2b-e2d8a7158111 · inbound

Learning, Fast and Slow: Towards LLMs That Adapt Continually cites this paper.

Learning, Fast and Slow: Towards LLMs That Adapt Continually Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:18.428439Z

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 ffce0901-474c-4101-b6df-bd29879ba9dc · inbound

Learning, Fast and Slow: Towards LLMs That Adapt Continually cites this paper.

Learning, Fast and Slow: Towards LLMs That Adapt Continually Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:19:45.785129Z

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.