Paper Citation Record · LEDGER

Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2310.10508.

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

pith.paper-citation-record.v1
2310.10508 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:09:36.325336Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T01:49:21.977472Z

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 196a7a00-9828-4bd2-b380-a351a5b55f35 · inbound

CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology cites this paper.

CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:58:51.223784Z

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 6d3e0532-4d82-42b1-9006-bfcc5a769866 · inbound

Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillation cites this paper.

Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillation Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:36.325336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b83dc8b9-f798-4924-98a5-c40550c8ecbb · inbound

Mobile Application Review Summarization using Chain of Density Prompting cites this paper.

Mobile Application Review Summarization using Chain of Density Prompting Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:28.686146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa3d31b5-84b5-4a89-8e51-3b6b347c02c2 · inbound

Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models cites this paper.

Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-19T07:32:08.685790Z

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 37ba835b-a86c-49ba-87c7-e1b1a02f7947 · inbound

A Pilot Study on LLM-Based Agentic Translation from Android to iOS: Pitfalls and Insights cites this paper.

A Pilot Study on LLM-Based Agentic Translation from Android to iOS: Pitfalls and Insights Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T15:24:10.695958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a41307d4-cdb9-4d73-807c-a83d07fe959b · inbound

Extension Decisions in Open Source Software Ecosystem cites this paper.

Extension Decisions in Open Source Software Ecosystem Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-06T11:05:32.621605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2e5c741-c7b8-4b93-a617-2ac3a0dfbdea · inbound

Prompt-Driven Code Summarization: A Systematic Literature Review cites this paper.

Prompt-Driven Code Summarization: A Systematic Literature Review Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 95

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:35:18.559436Z

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 e1975792-d0b9-4590-bcdd-0a888bcfffe2 · inbound

OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms cites this paper.

OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:51:24.310331Z

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 c599af39-e52d-4fa2-aad5-f12841744e05 · inbound

TDD Governance for Multi-Agent Code Generation via Prompt Engineering cites this paper.

TDD Governance for Multi-Agent Code Generation via Prompt Engineering Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:16:26.937686Z

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 e0993458-c552-46f1-84a0-6136dcccc9d3 · inbound

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models cites this paper.

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:27:29.548906Z

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 8540ef26-6a44-4416-8ca2-fa223cc2e69d · inbound

No Two Developers Think Alike: How Problem-Solving Styles and Experience Shape Needs in Conversational Interaction with Copilot cites this paper.

No Two Developers Think Alike: How Problem-Solving Styles and Experience Shape Needs in Conversational Interaction with Copilot Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T01:49:21.979113Z

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 d6ad79ec-e9ac-430a-a3b0-86000957642a · inbound

Comparing Large Language Models on Scrum Certification-Style Questions: Accuracy, Stability, and Error Patterns cites this paper.

Comparing Large Language Models on Scrum Certification-Style Questions: Accuracy, Stability, and Error Patterns Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:07:21.016257Z

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 70ae1072-d1b1-47b4-afc0-088627c9a665 · inbound

Prompting GPT-5 on Scrum Certification Questions: An Empirical Accuracy Study cites this paper.

Prompting GPT-5 on Scrum Certification Questions: An Empirical Accuracy Study Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:07:21.060144Z

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.