Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.17545.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T18:11:46.421669Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T07:00:43.019928Z
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 5e0b3112-7946-4186-9c22-cf285c89e289 · inbound
LLM-based event log analysis techniques: A survey cites this paper.
LLM-based event log analysis techniques: A survey Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T18:11:46.421669Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 96f78ac4-85f0-4c05-a694-d60701196257 · inbound
KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction cites this paper.
KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T07:00:43.022777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.