Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2409.09927.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:45:41.204856Z
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
2
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 78c3158d-32db-4027-9bb4-19bb3fdb2eed · inbound
LLM Performance for Code Generation on Noisy Tasks cites this paper.
LLM Performance for Code Generation on Noisy Tasks Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:45:41.204856Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1645894-ad5c-4459-87c3-38c1f24c1685 · inbound
Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation cites this paper.
Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:35:28.145036Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3a96b30f-98c0-492f-9ca3-a2b6bd1fdd45 · inbound
Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications cites this paper.
Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T17:24:56.595279Z
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.