Paper Citation Record · LEDGER

Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2308.07610.

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

pith.paper-citation-record.v1
2308.07610 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:32:14.663343Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T07:00:43.044725Z

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 55740275-5071-4f6e-8c75-0e3d3e047936 · inbound

SHIELD: APT Detection and Intelligent Explanation Using LLM cites this paper.

SHIELD: APT Detection and Intelligent Explanation Using LLM Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T12:32:14.663343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1b2dbf4-30ba-4f81-8b00-6fa3332ecaae · inbound

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data cites this paper.

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies

Reference 134

Resolution: unresolved; no resolver link, observed 2026-08-03T08:15:24.140770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24b5d77d-a862-45b9-8b40-86e91489496a · inbound

KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction cites this paper.

KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:00:43.047167Z

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 e2b665bd-281a-4dcb-bcec-5c2175366f02 · inbound

LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnostics cites this paper.

LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnostics Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:01:03.815364Z

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 5c750549-8c70-424f-b687-e35c5f387957 · inbound

A pragmatic approach to regulating AI agents cites this paper.

A pragmatic approach to regulating AI agents Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T10:04:06.870715Z

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 0bfcae61-a0f1-48be-934d-c106e3d8b4d3 · inbound

AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey cites this paper.

AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies

Reference 161

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:51:14.714983Z

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.