Pith. sign in

Paper Citation Record · LEDGER

Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.11198.

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

pith.paper-citation-record.v1
2408.11198 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:42:48.458168Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T08:53:10.305963Z

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 7797ed5c-8f72-47d5-8ceb-9483a04ecb80 · inbound

Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach cites this paper.

Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T12:42:48.458168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:42:48.458168Z digest=sha256:c00873f4645cc22163a68a34507b47fe12b302b50d76cdd43101f471ad4d68d6

Observation 2c65fd6a-21fb-4fec-b0d8-5efb81cad71a · inbound

Mutation-Guided Unit Test Generation with a Large Language Model cites this paper.

Mutation-Guided Unit Test Generation with a Large Language Model Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm

Reference 63

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T11:07:15.241888Z

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.

source=pdf_text observed=2026-05-19T11:05:15.898419Z digest=sha256:17377ee1af841cd207f6051567fd762d75980b660fc4c1ef289d94b7fb19ad38

Observation 61a61d83-13b5-459d-86f8-8db2bba793df · inbound

Can LLMs Replace Humans During Code Chunking? cites this paper.

Can LLMs Replace Humans During Code Chunking? Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T23:10:12.988811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:10:12.988811Z digest=sha256:3e9315bb812d254f6374cdec53ef7e9ca7fb9ed0e5874f07aa5408389efcd667

Observation 652d6c47-6e26-47e9-be96-b5b7b9e729a6 · inbound

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation cites this paper.

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:13:13.153487Z

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.

source=pdf_text observed=2026-05-13T20:12:12.283350Z digest=sha256:c78ddf7122ba745f99fbec32a5ba9b3aaa89ac045754c24bdf73bdbddf58cbdd

Observation 841647b5-9268-48ae-bdd8-670e03463c29 · inbound

Prompt Optimization for LLM Code Generation via Reinforcement Learning cites this paper.

Prompt Optimization for LLM Code Generation via Reinforcement Learning Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-20T08:53:10.307518Z

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.

source=pdf_text observed=2026-05-20T08:49:36.986452Z digest=sha256:ed1d4a1153cc6aa5d3c2a05db95cc6a21474e85d4da55366ea1587f2e6a46ab0