Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An Empirical Study on Capability of Large Language Models in Understanding Code Semantics
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.03611.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T10:13:07.032527Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 9736c17e-dc10-44df-9497-700ce4f0af9e · inbound
Correctness Assessment of Code Generated by Large Language Models Using Internal Representations cites this paper.
Correctness Assessment of Code Generated by Large Language Models Using Internal Representations An Empirical Study on Capability of Large Language Models in Understanding Code Semantics
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T16:41:30.973378Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 486926ed-8372-4413-a668-56d549c35821 · inbound
Secret Breach Detection in Source Code with Large Language Models cites this paper.
Secret Breach Detection in Source Code with Large Language Models An Empirical Study on Capability of Large Language Models in Understanding Code Semantics
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T10:13:07.032527Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 052638c3-b103-41b9-8c62-019503ea3c25 · inbound
EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention cites this paper.
EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention An Empirical Study on Capability of Large Language Models in Understanding Code Semantics
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T20:56:50.500149Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.