Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.11050.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T15:04:47.756227Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T11:26:03.680629Z
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 dd5d5fc4-c2ec-4204-a370-7cb3ce6689a7 · inbound
CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation cites this paper.
CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T11:26:03.683597Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 15bd62c8-78b3-4372-9bda-af244d0625f2 · inbound
LM2Alloy: Investigating LLM-Generated Formal Specifications for Automated Test Derivation in Production Software cites this paper.
LM2Alloy: Investigating LLM-Generated Formal Specifications for Automated Test Derivation in Production Software An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T15:04:47.756227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.