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Paper Citation Record · LEDGER

Analyzing the Performance of Large Language Models on Code Summarization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.08018.

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

pith.paper-citation-record.v1
2404.08018 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:26:04.588284Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T04:05:55.537734Z

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 4339f942-a926-408f-85f6-97e235e4def0 · inbound

Large Language Models for Fault Localization: An Empirical Study cites this paper.

Large Language Models for Fault Localization: An Empirical Study Analyzing the Performance of Large Language Models on Code Summarization

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-04T08:26:04.588284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T08:26:04.588284Z digest=sha256:5e975262652302ab9dda162acb05e02ed11d3ef17e40792da13010f9fd12f241

Observation 5fd4e263-55b6-4b40-b9da-58db98057205 · inbound

Boosting Automatic Java-to-Cangjie Translation with Multi-Stage LLM Training and Error Repair cites this paper.

Boosting Automatic Java-to-Cangjie Translation with Multi-Stage LLM Training and Error Repair Analyzing the Performance of Large Language Models on Code Summarization

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:05:55.542543Z

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=2026-05-11T01:59:53.134278Z digest=sha256:515c8f99813a5b9ec1ad2d04a79f2c6c96a63e54d69a347548c05ed10a0e9b72