Paper Citation Record · LEDGER

Analyzing the Performance of Large Language Models on Code Summarization

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 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 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:26:56.189277Z

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 106ef279-2395-4f9a-9fe6-3fd45a1debc4 · inbound

Resource-Efficient & Effective Code Summarization cites this paper.

Resource-Efficient & Effective Code Summarization Analyzing the Performance of Large Language Models on Code Summarization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T04:23:34.592437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af300a8b-44df-4a8c-ae40-78f779960adf · inbound

A Structured Literature Review on Traditional Approaches in Current Natural Language Processing cites this paper.

A Structured Literature Review on Traditional Approaches in Current Natural Language Processing Analyzing the Performance of Large Language Models on Code Summarization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:56.189277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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 2026-08-18T06:34:40.430872+00:00.