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Paper Citation Record · LEDGER

Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2302.07248.

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

pith.paper-citation-record.v1
2302.07248 v3

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 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:29:59.985522Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T15:34:48.540049Z

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 633e500e-50ef-41b4-8a3c-3f462db08cd6 · inbound

Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions cites this paper.

Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-06T15:29:59.985522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:29:59.985522Z digest=sha256:2b4aadd545e2b1f790a81ac28e8131aaeb09721ca535bfc593532a8a03370589

Observation 5b6ab556-2c15-47a1-b66b-73fe2868c46d · inbound

From Noise to Knowledge: Interactive Summaries for Developer Alerts cites this paper.

From Noise to Knowledge: Interactive Summaries for Developer Alerts Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-05T22:22:15.501597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:22:15.501597Z digest=sha256:c325c0638d69586bc743ec3ff9212e792c0ba9bb742b23e091c82c5514fd656d

Observation 76a97947-3cc5-42a4-8fb4-8fc6aa9526e5 · inbound

Hint-Writing with Deferred AI Assistance: Fostering Critical Engagement in Data Science Education cites this paper.

Hint-Writing with Deferred AI Assistance: Fostering Critical Engagement in Data Science Education Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:41:03.422133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T01:17:45.434410Z digest=sha256:e08a611c9ca347b25bbc0a0b53a48023c6518cde3ca60984cb274c23b8b45e26

Observation 02dbab6b-3941-4d93-9026-87efb275d9fc · inbound

Uncertainty Quantification for LLM-based Code Generation cites this paper.

Uncertainty Quantification for LLM-based Code Generation Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-13T04:02:13.071588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-13T04:01:31.679818Z digest=sha256:4f2204d7bb8b729071b44cdffa511a07300ad13a29107d147d92a710b40b0b5f

Observation 39427816-77ec-47c3-b9e2-1dda64fd7eae · inbound

An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods cites this paper.

An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:34:48.541321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-30T15:17:26.306332Z digest=sha256:86aa937cc0a993242d872fe498e59c85e359a90efbc0b3b6648c44945d6821b8