Paper Citation Record · LEDGER

Cross-System Software Log-based Anomaly Detection Using Meta-Learning

As of 15 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2412.15445.

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

pith.paper-citation-record.v1
2412.15445 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:30:14.849353Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:50.767574Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:35:51.657885Z

Reference resolution

31 of 31 outbound references displayed

verified exact1
verified fuzzy25
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d3951fa6-dfae-4f24-bcef-ddd317352c60 · outbound

This paper cites Robust log-based anomaly detection on unstable log data,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Robust log-based anomaly detection on unstable log data,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.261957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 114aeeec-ecf4-49e9-b8c3-fceb83105b63 · outbound

This paper cites Metalog: Generalizable cross-system anomaly detection from logs with meta-learning,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Metalog: Generalizable cross-system anomaly detection from logs with meta-learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.248998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 05cd9553-5701-40a7-904f-839543aa170a · outbound

This paper cites Experience report: System log analysis for anomaly detection,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Experience report: System log analysis for anomaly detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.234434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2fb51e8d-85f6-497d-b8db-2dad3fc262f5 · outbound

This paper cites Log clustering based problem identification for online service systems,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Log clustering based problem identification for online service systems,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.220370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 37702de5-d558-41ee-a9f7-6b0a428fcd6e · outbound

This paper cites Log-based anomaly detection with deep learning: How far are we?.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Log-based anomaly detection with deep learning: How far are we?

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.207458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f3991bdc-1338-47c8-b849-c4d633641c6d · outbound

This paper cites Loglab: attention- based labeling of log data anomalies via weak supervision,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Loglab: attention- based labeling of log data anomalies via weak supervision,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.194373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a4bc83cc-a99a-42a4-99fc-0bb199ad59f7 · outbound

This paper cites Semi-supervised log-based anomaly detection via probabilistic label estimation,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Semi-supervised log-based anomaly detection via probabilistic label estimation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.182234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d16f5d0b-6e06-4df0-ace4-ec7cda87a594 · outbound

This paper cites Execution anomaly detection in distributed systems through unstructured log analysis,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Execution anomaly detection in distributed systems through unstructured log analysis,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.170503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 36043937-2658-4216-a0d4-e259601c4aed · outbound

This paper cites Fault analysis and debugging of microservice systems: Industrial survey, benchmark system, and empirical study,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Fault analysis and debugging of microservice systems: Industrial survey, benchmark system, and empirical study,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T11:30:14.761490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c1d2de6-88cb-4eb9-bc7d-3e819b362cb0 · outbound

This paper cites On understanding laws, evolution, and conservation in the large-program life cycle,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning On understanding laws, evolution, and conservation in the large-program life cycle,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.150668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d5f65118-1294-4cd6-9150-9939f22be096 · outbound

This paper cites Examining the stability of logging statements,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Examining the stability of logging statements,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.138938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8df76cae-035d-4688-858f-706351ff144c · outbound

This paper cites Swisslog: Robust and unified deep learning based log anomaly detection for diverse faults,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Swisslog: Robust and unified deep learning based log anomaly detection for diverse faults,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.125291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6f5bddb8-23b1-43cb-9c1a-0b5fc3fda3a5 · outbound

This paper cites AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T11:30:14.777057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58e0e9a6-2894-4ae9-82c4-d4deff372713 · outbound

This paper cites Logtransfer: Cross-system log anomaly detection for software systems with transfer learning,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Logtransfer: Cross-system log anomaly detection for software systems with transfer learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.112016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a7fff681-e35a-44b9-a274-1bf2450fee5c · outbound

This paper cites Logsd: Detecting anomalies from system logs through self-supervised learning and frequency-based masking,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Logsd: Detecting anomalies from system logs through self-supervised learning and frequency-based masking,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.097954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 65c14abf-93bb-4a0e-881c-925ae01cf274 · outbound

This paper cites Unsupervised cross-system log anomaly detection via domain adaptation,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Unsupervised cross-system log anomaly detection via domain adaptation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.083897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9574767f-d5e6-4da8-80ac-e0049414834a · outbound

This paper cites Onelog: towards end-to-end software log anomaly detection,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Onelog: towards end-to-end software log anomaly detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.071226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 921ea2e4-2e3d-44ca-8df4-6be35f4fe66b · outbound

This paper cites Log-based anomaly detection without log pars- ing,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Log-based anomaly detection without log pars- ing,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.058522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 694cec94-4857-4999-81d0-7c749a789f75 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T11:30:14.801620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3909978a-125a-421e-b6e4-1b1e2571a362 · outbound

This paper cites Loghub: A large collection of system log datasets for ai-driven log analytics,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Loghub: A large collection of system log datasets for ai-driven log analytics,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.032902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d2290497-14c0-46d9-b69f-858116f3e8a3 · outbound

This paper cites What supercomputers say: A study of five system logs,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning What supercomputers say: A study of five system logs,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.019406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b012cf3-9d11-4250-a058-17b1b2e19ced · outbound

This paper cites A critical review of common log data sets used for evaluation of sequence-based anomaly detection techniques,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning A critical review of common log data sets used for evaluation of sequence-based anomaly detection techniques,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:15.006460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f4baae98-7c7f-4508-a926-5e14727f805a · outbound

This paper cites Ro- bust and transferable anomaly detection in log data using pre-trained language models,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Ro- bust and transferable anomaly detection in log data using pre-trained language models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:14.992598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 685659ed-b989-466f-b9a4-ad4851d92d34 · outbound

This paper cites Drain: An online log parsing approach with fixed depth tree,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Drain: An online log parsing approach with fixed depth tree,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:14.980313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 96ea4693-1ccd-4507-b9a6-4fd000b5d237 · outbound

This paper cites Loglead-fast and integrated log loader, enhancer, and anomaly detector,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Loglead-fast and integrated log loader, enhancer, and anomaly detector,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:14.967375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9780189c-9ecf-488c-95ac-990caff79f38 · outbound

This paper cites System log parsing: A survey,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning System log parsing: A survey,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:14.955368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation befce74e-7ddd-4bad-ad99-826cdb15cff7 · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T11:30:14.833369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29f7322d-c332-4447-a7ca-59a4d54eb62b · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transform- ers for Language Understanding,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning BERT: Pre-training of Deep Bidirectional Transform- ers for Language Understanding,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:14.943800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8962af0e-151e-4644-bdfb-e9472f76c02f · outbound

This paper cites Long short-term memory,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Long short-term memory,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T11:30:14.841536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1d4c6f2-4fa9-4b2b-b64e-a87c8ea8e7ec · outbound

This paper cites How to train your maml to excel in few-shot classification,.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning How to train your maml to excel in few-shot classification,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:30:14.924467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f43761f1-2361-4a54-a671-c33eba10a36e · outbound

This paper cites Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning.

Cross-System Software Log-based Anomaly Detection Using Meta-Learning Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-11T11:30:14.888955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 62a309b3-7308-48a7-a086-c8d807847a2a · inbound

Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs cites this paper.

Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs Cross-System Software Log-based Anomaly Detection Using Meta-Learning

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T13:35:51.707354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.