Paper Citation Record · LEDGER

Effective LLM-Driven Code Generation with Pythoness

As of 14 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2501.02138.

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

pith.paper-citation-record.v1
2501.02138 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:16:43.663247Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T16:40:08.788179Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T16:43:34.745256Z

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy18
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9a8781bd-1e6b-4269-8ad1-32c3cccc8645 · outbound

This paper cites GitHub Copilot,.

Effective LLM-Driven Code Generation with Pythoness GitHub Copilot,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:44.213894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 833d0bb4-d13e-4ac6-8a2c-cc8983a7bbc0 · outbound

This paper cites ChatGPT,.

Effective LLM-Driven Code Generation with Pythoness ChatGPT,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:44.189790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0f721661-5524-4a8a-a7d6-e1ee41f52917 · outbound

This paper cites How much does AI impact development speed? An enterprise-based randomized controlled trial.

Effective LLM-Driven Code Generation with Pythoness How much does AI impact development speed? An enterprise-based randomized controlled trial

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T22:16:43.539320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e52cc0d1-edff-4f93-971f-ae318b9c1a15 · outbound

This paper cites Expectation vs. experi- ence: Evaluating the usability of code generation tools powered by large language models,.

Effective LLM-Driven Code Generation with Pythoness Expectation vs. experi- ence: Evaluating the usability of code generation tools powered by large language models,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:44.169483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 972703bb-3c69-438c-8d22-db3ff3e8005d · outbound

This paper cites Grounded copilot: How programmers interact with code-generating models,.

Effective LLM-Driven Code Generation with Pythoness Grounded copilot: How programmers interact with code-generating models,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:44.138648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e6a1f607-4fc5-4dff-8ae1-cf89ea587dd3 · outbound

This paper cites Conversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities.

Effective LLM-Driven Code Generation with Pythoness Conversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T22:16:43.558392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c06c516c-a25b-47d9-9604-80d9ca2d4aef · outbound

This paper cites Research: Quantifying GitHub Copilot’s Impact on Developer Productivity and Happiness,.

Effective LLM-Driven Code Generation with Pythoness Research: Quantifying GitHub Copilot’s Impact on Developer Productivity and Happiness,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:44.114845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e892280d-e0be-46e5-8149-0c63cf470c0c · outbound

This paper cites A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges,.

Effective LLM-Driven Code Generation with Pythoness A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:44.093041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 53fd5cac-2fe6-4fbe-b4c8-d30a18f74b8d · outbound

This paper cites Accessed: 2024-11-16.

Effective LLM-Driven Code Generation with Pythoness Accessed: 2024-11-16

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:44.066515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cbc6b32a-4d5e-4acd-9fef-58d3f0f70ef0 · outbound

This paper cites Accessed: 2024-11-16.

Effective LLM-Driven Code Generation with Pythoness Accessed: 2024-11-16

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:44.040419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d543f636-817b-40b7-b89d-9cb91ce098e3 · outbound

This paper cites Parsel: Algorithmic reasoning with language models by composing decompositions,.

Effective LLM-Driven Code Generation with Pythoness Parsel: Algorithmic reasoning with language models by composing decompositions,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:44.015066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f810df2d-7a8b-4a7e-a9b1-d27454a70aa8 · outbound

This paper cites Codet: Code generation with generated tests,.

Effective LLM-Driven Code Generation with Pythoness Codet: Code generation with generated tests,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:43.975888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 810551c5-dbf4-4ba3-867e-3f694e82a6bf · outbound

This paper cites Property-Based Testing: A New Approach to Testing for Assurance,.

Effective LLM-Driven Code Generation with Pythoness Property-Based Testing: A New Approach to Testing for Assurance,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:43.937330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 87b92d67-ef5c-4c34-93d0-876061967837 · outbound

This paper cites Hypothesis: A New Approach to Property-Based Testing,.

Effective LLM-Driven Code Generation with Pythoness Hypothesis: A New Approach to Property-Based Testing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:43.910576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dd4c44b6-8969-4955-886a-ce48cfb9475a · outbound

This paper cites Evaluating Large Language Models Trained on Code,.

Effective LLM-Driven Code Generation with Pythoness Evaluating Large Language Models Trained on Code,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:43.874423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5811a5b-ace6-44c8-a2b9-7eac4a6249c0 · outbound

This paper cites (2024) Hello GPT-4o.

Effective LLM-Driven Code Generation with Pythoness (2024) Hello GPT-4o

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:43.839463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 60cdc3cb-00b6-4fc7-be96-2ab1e6004ab6 · outbound

This paper cites (2024) Models.

Effective LLM-Driven Code Generation with Pythoness (2024) Models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:43.813691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 524be16e-c38e-4552-b8a9-2647f5b21bae · outbound

This paper cites The Sketching Approach to Program Synthesis,.

Effective LLM-Driven Code Generation with Pythoness The Sketching Approach to Program Synthesis,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:43.781712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ade87249-068c-4fa2-b79c-a5c9b1e81e32 · outbound

This paper cites Program- ming by Sketching for Bit-Streaming Programs,.

Effective LLM-Driven Code Generation with Pythoness Program- ming by Sketching for Bit-Streaming Programs,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:43.759671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 83f8fb2c-0651-4cfe-8059-606e314423c6 · outbound

This paper cites Available: http://papers.nips.cc/paper_files/paper/2023/ hash/6445dd88ebb9a6a3afa0b126ad87fe41-Abstract-Conference.html.

Effective LLM-Driven Code Generation with Pythoness Available: http://papers.nips.cc/paper_files/paper/2023/ hash/6445dd88ebb9a6a3afa0b126ad87fe41-Abstract-Conference.html

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:16:43.995642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 178c0938-77d2-4b87-9ac9-4c21097de6d3 · inbound

From Text to DSL: Evaluating Grammar-Based Model Generation Using Open LLMs cites this paper.

From Text to DSL: Evaluating Grammar-Based Model Generation Using Open LLMs Effective LLM-Driven Code Generation with Pythoness

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-20T16:43:34.746522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.