Paper Citation Record · LEDGER

AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2405.07626.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2405.07626 v2

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 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:26:36.775514Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T20:05:04.418878Z

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 5e8af14d-ae62-4b5e-be72-4542ff9d5525 · inbound

A Survey of AIOps in the Era of Large Language Models cites this paper.

A Survey of AIOps in the Era of Large Language Models AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:36.775514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6d12069-524b-4a7c-b8cf-88aec7f4fc0f · inbound

Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought cites this paper.

Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:28.372375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 908a880e-1361-488d-ba26-234f4bc51775 · inbound

E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning cites this paper.

E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:25:59.315711Z

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.

Observation 5350de15-3980-4dae-85ac-a80fd067747d · inbound

Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought cites this paper.

Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-06-30T20:05:04.420289Z

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.