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Paper Citation Record · LEDGER

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios

As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.18289.

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

pith.paper-citation-record.v1
2607.18289 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:22:44.632168Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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  • malformed identifier0
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External citation measurements

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Outbound references

Observation ac081c6c-0b65-461f-a671-996903b0df1a · outbound

This paper cites Advances in Neural Information Processing Systems , note =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Advances in Neural Information Processing Systems , note =

Reference 1

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source=arxiv_source observed=2026-08-02T09:22:41.103888Z digest=sha256:bf18f79b1f5810321fc3cb27f06ff968d5c9a774f5158dbb44eb278ef79cba57

Observation 325c1f19-64c3-48f3-864c-ef93fee28a19 · outbound

This paper cites Advances in Neural Information Processing Systems , note =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Advances in Neural Information Processing Systems , note =

Reference 2

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source=arxiv_source observed=2026-08-02T09:22:41.185359Z digest=sha256:9b01f8b409337546b94fc79eeb3dc31827420f082355a34e09eeb5047986c350

Observation 2d5516af-143b-42b5-8dd5-84bf37f6022f · outbound

This paper cites IEEE transactions on pattern analysis and machine intelligence , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios IEEE transactions on pattern analysis and machine intelligence , volume=

Reference 3

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source=arxiv_source observed=2026-08-02T09:22:41.243039Z digest=sha256:211561eff9f768a813be781efbe221e7226e7739de5ab4b531623e3e3ec1e5c0

Observation 6c7d1f15-6c94-4cf7-a77f-0d8a7e18793f · outbound

This paper cites ACM computing surveys (CSUR) , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios ACM computing surveys (CSUR) , volume=

Reference 4

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source=arxiv_source observed=2026-08-02T09:22:41.355125Z digest=sha256:4d30480a087af754679690cb33bc7f833733a1962f1fbf7a08fb8d04864f43d5

Observation 74b370af-2612-4592-bdd9-4b1652f8d984 · outbound

This paper cites SOUL: A Semi-supervised Open-world continUal Learning method for Network Intrusion Detection.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios SOUL: A Semi-supervised Open-world continUal Learning method for Network Intrusion Detection

Reference 5

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source=arxiv_source observed=2026-08-02T09:22:41.430051Z digest=sha256:95e13dce16def2a297082a23b233e1e8aa599c2de9ca494fdd22bd799154e305

Observation c48ceb5c-80e9-446a-99be-4899ad8dd301 · outbound

This paper cites and Finn, Chelsea , title =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios and Finn, Chelsea , title =

Reference 6

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source=arxiv_source observed=2026-08-02T09:22:41.515159Z digest=sha256:bf8d34cb8f4eb91e6dc41765ace9b34509413a588c0a38d7236e349f7ee88b50

Observation 4d7f71b6-4603-455d-a8a9-47b56139c430 · outbound

This paper cites Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , year =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , year =

Reference 7

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source=arxiv_source observed=2026-08-02T09:22:41.570212Z digest=sha256:534773e5fb974db688e27eec40daa802e6b2b0cde7fe6320f7fff39357a93fa7

Observation deda8bba-d95d-400e-b201-532d0857f03f · outbound

This paper cites Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , year =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , year =

Reference 8

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source=arxiv_source observed=2026-08-02T09:22:41.639936Z digest=sha256:ec344363bc0644aa9315e16ce9896c8798d0e3fc585fdf09038d5672d9b55c70

Observation d87a4e2e-dcc9-427c-bfc0-85d1e17b468f · outbound

This paper cites Advances in Neural Information Processing Systems , note =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Advances in Neural Information Processing Systems , note =

Reference 9

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source=arxiv_source observed=2026-08-02T09:22:41.724960Z digest=sha256:b9c7741197497422774d1e91f57296b75aae19ef524af5cbdf1a837c6812c726

Observation 754ec031-fc63-4dbc-8f79-a7b1db0275b1 · outbound

This paper cites Advances in Neural Information Processing Systems , note =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Advances in Neural Information Processing Systems , note =

Reference 10

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source=arxiv_source observed=2026-08-02T09:22:41.807528Z digest=sha256:aa4962ebaa05c55c9cb21e8eda35249505bcbeb2fef56ba4d59044f521756d5a

Observation e0dda7a0-d7b6-45c3-a688-245e9153a355 · outbound

This paper cites , title =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios , title =

Reference 11

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source=arxiv_source observed=2026-08-02T09:22:41.889993Z digest=sha256:e0a7ddc23d491c7247dff2693e28236280094bbcc9ae75f8f62657c6f187cb95

Observation 402a2f95-abf9-478c-9c27-029f9c5f0f8c · outbound

This paper cites IEEE Access , volume =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios IEEE Access , volume =

Reference 12

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source=arxiv_source observed=2026-08-02T09:22:41.954148Z digest=sha256:2ea7b516527ef2f529f2d11672c94061c03d1b8415c3bf67a5be8c1015b33a45

Observation 34a31c95-d9e6-474e-949c-96c623c1d0e8 · outbound

This paper cites Neural Networks , volume =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Neural Networks , volume =

Reference 13

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source=arxiv_source observed=2026-08-02T09:22:42.053934Z digest=sha256:4244bcae07c60aa39aabcaa7b1d8f112fc5dc647e242ea01dc01692f564186a3

Observation 360c88fb-5199-4b9a-900a-5b5c44340d3c · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Proceedings of the AAAI conference on artificial intelligence , volume =

Reference 14

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Observation cbdac547-3402-4241-b7cc-390a30d823c4 · outbound

This paper cites IEEE Transactions on Industrial Informatics , year =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios IEEE Transactions on Industrial Informatics , year =

Reference 15

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source=arxiv_source observed=2026-08-02T09:22:42.196100Z digest=sha256:8032de3d100d9078bc54bfe2081f6556b68eea4c4ac8f16ed8e6c0888e984520

Observation 2bbf15c3-0614-4caa-8997-58d4091d8fad · outbound

This paper cites Proceedings of the 30th ACM International Conference on Multimedia , pages =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Proceedings of the 30th ACM International Conference on Multimedia , pages =

Reference 16

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source=arxiv_source observed=2026-08-02T09:22:42.224946Z digest=sha256:c7fa0a9d806d3b9e08eecf7fed651be814641e17005a4c46c7d677d4c8428d01

Observation 17911549-be00-4060-b01b-68cc4c6f83eb · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 17

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source=arxiv_source observed=2026-08-02T09:22:42.268446Z digest=sha256:7d491b663c1793069cc6e401bb84e2d01bbf680d956fe9840bbdcd38cd554aac

Observation e455d483-9f85-4ef0-979d-a9ee529214de · outbound

This paper cites Neural networks , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Neural networks , volume=

Reference 18

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source=arxiv_source observed=2026-08-02T09:22:42.318123Z digest=sha256:10308adec846e920c6a8147caa1233e72a2d6ee360603cba6c2524bf0fa6dd5a

Observation 260feff0-0ba7-4d62-9df6-1cfdb2ece4d9 · outbound

This paper cites ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection

Reference 19

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source=arxiv_source observed=2026-08-02T09:22:42.372529Z digest=sha256:ddd375a36e05609e893d3b4ade5239fea07cf5179e3b3ddc13c552156a344efe

Observation 95f9d35c-5ade-4708-985f-82d840edec91 · outbound

This paper cites arXiv preprint arXiv:2506.00956 , year=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios arXiv preprint arXiv:2506.00956 , year=

Reference 20

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source=arxiv_source observed=2026-08-02T09:22:42.468625Z digest=sha256:4b7d6aa0fc07f7e64081457aab4157ba7bb06b7eeecfb70dbb38f2ba6ffd6785

Observation 675feb37-78bc-460f-8182-f803279f7ec1 · outbound

This paper cites Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection , url =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection , url =

Reference 21

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source=arxiv_source observed=2026-08-02T09:22:42.551438Z digest=sha256:b1212fda2333e16fac47e4a76a94c8c602de09b792465ed229f5fed2f80b01b8

Observation e74afafe-6acb-4c77-802c-8b080eea0ffd · outbound

This paper cites IEEE INFOCOM 2024-IEEE Conference on Computer Communications , pages=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios IEEE INFOCOM 2024-IEEE Conference on Computer Communications , pages=

Reference 22

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source=arxiv_source observed=2026-08-02T09:22:42.735413Z digest=sha256:5bd59afff4073bf4c006ffd2f9acd88915ed13c500f52d8bea3ff529b0d01e4b

Observation a61596a9-75be-409a-8f47-9cc91905a948 · outbound

This paper cites 2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) , pages =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios 2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) , pages =

Reference 23

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source=arxiv_source observed=2026-08-02T09:22:42.901753Z digest=sha256:2dd586dd1cbe352c11ace73c80d1a77f2f1d4f3f144a4c5dd4bfd666361ad281

Observation 844cb7a0-887e-446b-8e02-41b7c80deda1 · outbound

This paper cites 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP) , pages =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP) , pages =

Reference 24

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Observation b7f96485-0d26-4668-a737-a4bc8145b0a7 · outbound

This paper cites SoftwareX , volume =.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios SoftwareX , volume =

Reference 25

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source=arxiv_source observed=2026-08-02T09:22:43.172165Z digest=sha256:d3a6317715cae12f154c8f7efa79307eff7b1b2ffbbca2947416624e5efdeebf

Observation 0d7c53fa-a763-40bc-9460-3c7dd4244813 · outbound

This paper cites Machine Learning and Knowledge Extraction , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Machine Learning and Knowledge Extraction , volume=

Reference 26

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source=arxiv_source observed=2026-08-02T09:22:43.316253Z digest=sha256:45a8e2e3caf57f75ca33a7fc47233d9c19799f15488bc9d070d7c26fb9dd3556

Observation 4023b63a-4b91-4c71-99ff-27ca00549e17 · outbound

This paper cites ICLR , year=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios ICLR , year=

Reference 27

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Observation 6bb3638a-072a-4652-b4ef-ad94570fbfba · outbound

This paper cites TMLR , year=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios TMLR , year=

Reference 28

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Observation a17c7cad-7f93-4d89-ab68-4c80e6a05a1a · outbound

This paper cites Applied Sciences , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Applied Sciences , volume=

Reference 29

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Observation 009782ee-11c5-4a53-ad7a-841cf834fc0a · outbound

This paper cites Neural Networks , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Neural Networks , volume=

Reference 30

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source=arxiv_source observed=2026-08-02T09:22:43.883595Z digest=sha256:c66e180402970fdbc6a71eba8441e114e21963f604dade364b9c2c4342df1a5f

Observation 9ad2ade5-1c03-47f9-89b6-e79f0426dcc6 · outbound

This paper cites Transactions on Machine Learning Research , year=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Transactions on Machine Learning Research , year=

Reference 31

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Observation 54c5985d-26ae-434b-89de-bcde4ce5cc0d · outbound

This paper cites Machine Learning , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Machine Learning , volume=

Reference 32

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source=arxiv_source observed=2026-08-02T09:22:44.020120Z digest=sha256:088eac18a6d2fdca56920e1b49f026024e86b671382daeda9d2758a6c1f190cd

Observation fe884847-5abc-4740-bc6b-d96a61365862 · outbound

This paper cites Applied Soft Computing , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Applied Soft Computing , volume=

Reference 33

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Observation 77fc82d2-1dbe-43bd-8416-aa723ef7be56 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 34

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Observation 14d7c3d5-1844-4c19-b8b5-6a8f52ee11fc · outbound

This paper cites Biometrika , volume=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Biometrika , volume=

Reference 35

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source=arxiv_source observed=2026-08-02T09:22:44.244509Z digest=sha256:566a08fa661c267d30e6d07554f00d53b4d7e3c332273752994ab8d7153ea6dd

Observation 6a9b5e7d-8678-4fba-8803-2c2ac97fb729 · outbound

This paper cites Proceedings of the 4th International Conference on Information Systems Security and Privacy , year=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Proceedings of the 4th International Conference on Information Systems Security and Privacy , year=

Reference 36

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Observation 693412e7-81d2-41b5-a0c9-45d047e1945e · outbound

This paper cites UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (

Reference 37

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source=arxiv_source observed=2026-08-02T09:22:44.399608Z digest=sha256:507103e76985a41d16c0e7c15ea5fc999dd635eeb4f44ae48bcf0b52fbd95fa2

Observation 1deecdf2-af1e-41c9-b63a-0a5686b92674 · outbound

This paper cites Public program analysis: A new categorical data approach , pages=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Public program analysis: A new categorical data approach , pages=

Reference 38

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unresolved
no resolver link, observed 2026-08-02T09:22:44.448766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:22:44.448766Z digest=sha256:eb32ea802f8cca59a33658b3181f81d9c917aae7d72820727d5e30b2990245eb

Observation 6092d71d-681b-4b59-a984-862825bae720 · outbound

This paper cites Encyclopedia of statistics in behavioral science , year=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios Encyclopedia of statistics in behavioral science , year=

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-02T09:22:44.536185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:22:44.536185Z digest=sha256:c36b082860c2a88e4781c1b21bcfcc1974485bde9e1a7c2445edd8289da3b42f

Observation f58a47f2-64a9-4cd1-b9a0-b7d8c8d632fc · outbound

This paper cites 2024 21st Annual international conference on privacy, security and trust (PST) , pages=.

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios 2024 21st Annual international conference on privacy, security and trust (PST) , pages=

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-02T09:22:44.632168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:22:44.632168Z digest=sha256:216906a0860a742dc8d6af7a2edff5e4c562e87cb9b6c63615e3ec8e36e34641

Pith citing papers

No inbound Pith citation observations are available.