Paper Citation Record · LEDGER

Evaluating AIGC Detectors on Code Content

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2304.05193.

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

pith.paper-citation-record.v1
2304.05193 v1

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 2026-08-22T06:32:14.747728+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:29:38.992515Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T22:07:37.482373Z

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 ed1ff3a8-a32d-416c-a17b-4c9f8a1583b3 · inbound

AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection cites this paper.

AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection Evaluating AIGC Detectors on Code Content

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T10:29:38.992515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f88dbcd-8655-4188-a550-c4223e59fc4e · inbound

CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps and Vision Models cites this paper.

CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps and Vision Models Evaluating AIGC Detectors on Code Content

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:07:37.487632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4167f825-4da5-4141-bf19-149db6b1a853 · inbound

Understanding the Ability of LLMs to Handle Character-Level Perturbation cites this paper.

Understanding the Ability of LLMs to Handle Character-Level Perturbation Evaluating AIGC Detectors on Code Content

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T09:39:02.521828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 501e53aa-d4f2-413e-be45-fd07222b77a1 · inbound

MAGA-Bench: Machine-Augment-Generated Text via Alignment Detection Benchmark cites this paper.

MAGA-Bench: Machine-Augment-Generated Text via Alignment Detection Benchmark Evaluating AIGC Detectors on Code Content

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-03T12:01:37.188140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.