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Paper Citation Record · LEDGER

LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.10455.

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

pith.paper-citation-record.v1
2411.10455 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:47:11.187114Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T20:42:58.352519Z

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 8a8523d4-8838-455f-a214-563360c8d8fe · inbound

Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education cites this paper.

Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T14:47:11.187114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:47:11.187114Z digest=sha256:c9b96926c89f43ae5d2b59355268e5c851243fdb833681e46afb0d222d1f498c

Observation f1cfb13e-7979-45ce-8c1d-b19302ea8d0f · inbound

Fine-Tuning Models for Automated Code Review Feedback cites this paper.

Fine-Tuning Models for Automated Code Review Feedback LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-14T20:42:58.355595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-14T20:39:36.114056Z digest=sha256:11f747546405e6445542afbcdf578d486a206b3fab4fb2764228d641849e065f