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Paper Citation Record · LEDGER

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection

As of 20 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2512.22179.

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

pith.paper-citation-record.v1
2512.22179 v3

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:19:08.979847Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

18 of 18 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved16
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 42daf798-13ab-4843-af52-679bd25f2ff0 · outbound

This paper cites Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:07.018781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:07.018781Z digest=sha256:ea199a7102e96c73640edf0017fe076e967ab709cff0f582e773365bb97a68e8

Observation d8ab6937-aad7-4e87-a753-bd7665a97661 · outbound

This paper cites Outside the Closed World: On Using Machine Learning for Network Intrusion Detection.2010 IEEE Symposium on Security and Privacy, pages 305–316, 2010.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Outside the Closed World: On Using Machine Learning for Network Intrusion Detection.2010 IEEE Symposium on Security and Privacy, pages 305–316, 2010

Reference 2

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malformed identifier
no resolver link, observed 2026-08-03T15:19:07.101448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:07.101448Z digest=sha256:771e0a76e5514f9dc6e56de3101a2594e1f325a0ee2e9970e408c758727de85a

Observation 2cf4c905-42de-4a25-9401-b01fc159c2b0 · outbound

This paper cites Deep Learning for Anomaly Detection: A Survey.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Deep Learning for Anomaly Detection: A Survey

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:07.214940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:07.214940Z digest=sha256:eafa8ab3de7174644207339cbf947e48f67cc4c42f9beb06243c5f435d9818a7

Observation e59f3f14-ae1c-489d-bbc6-fe9fcde4a90b · outbound

This paper cites Anomaly-Based Network Intrusion Detection: Techniques, Systems and Challenges.Computers & Security, 28(1-2):18–28, 2009.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Anomaly-Based Network Intrusion Detection: Techniques, Systems and Challenges.Computers & Security, 28(1-2):18–28, 2009

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:07.358299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:07.358299Z digest=sha256:d5983fc8a8d114882fe6bba782090a9d092e774af91b5fece988ee865a7f0592

Observation bce41d7e-e7e2-446e-a2c7-fe51db677c59 · outbound

This paper cites Outlier Detection with Autoencoder Ensembles.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Outlier Detection with Autoencoder Ensembles

Reference 5

Resolution
verified exact
doi, observed 2026-08-03T15:24:00.641232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-03T15:19:07.523795Z digest=sha256:6142c098b039e54a33d24e38a296f37d2208167fcf9407d8a8c66d8dcf1a02a8

Observation 9fb5f08f-993f-4b18-86fd-3790be104b87 · outbound

This paper cites Masked Autoregressive Flow for Density Estimation.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Masked Autoregressive Flow for Density Estimation

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:07.646532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:07.646532Z digest=sha256:6f8e13b439554508e37db34d979850707618eca806126df610fd1ec04644fe8c

Observation 49fc63bd-c5cd-4159-88dd-04fedb571d49 · outbound

This paper cites A Discriminative Feature Learning Approach for Deep Face Recognition.European Conference on Computer Vision (ECCV), pages 499–515.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection A Discriminative Feature Learning Approach for Deep Face Recognition.European Conference on Computer Vision (ECCV), pages 499–515

Reference 7

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unresolved
no resolver link, observed 2026-08-03T15:19:07.773461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:07.773461Z digest=sha256:72231cfc56b3c6a7aee72f2008896225558e446445c008bd37c7e51bcee5fec4

Observation 9a353bae-c1c8-4fa5-8c5f-e2fe247a3f83 · outbound

This paper cites Dimensionality Reduction by Learning an Invariant Mapping.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Dimensionality Reduction by Learning an Invariant Mapping

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:07.935429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:07.935429Z digest=sha256:1a4ac3e475c84d7a29d3d8dde43b021ab938d3e06f50614ddc04e1e004bb85d2

Observation afa34b3f-c5ba-4809-9273-725405d7b8ee · outbound

This paper cites an unresolved cited work.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Unresolved cited work

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:08.049152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.049152Z digest=sha256:1870a33b820c3f91deb89778ef69b674e4d1cf75a18749ac8dee896fe86b8e65

Observation 9c7fbb24-8c9e-4da4-93c8-7b4a871e27aa · outbound

This paper cites an unresolved cited work.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Unresolved cited work

Reference 10

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unresolved
no resolver link, observed 2026-08-03T15:19:08.137052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.137052Z digest=sha256:79667490424ab80377ae54ee955487f823161ed59232bccdde0d3377fc747d73

Observation 1ffdbf6d-1d57-47c1-bff0-d9391b9eb5c2 · outbound

This paper cites LossTransform: Reformulating Contrastive Objectives for Robust Representation Learning.International Conference on Learning Representations (ICLR), 2025.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection LossTransform: Reformulating Contrastive Objectives for Robust Representation Learning.International Conference on Learning Representations (ICLR), 2025

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:08.224736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.224736Z digest=sha256:1298bdcd98d5746ed2dffa8edd55f30fb269c834bc2fdffecddfcd4b06dd5c00

Observation 302bf07c-5df7-42c6-9b16-0a3b9eaeaa5c · outbound

This paper cites Attention Is All You Need.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Attention Is All You Need

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:08.332343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.332343Z digest=sha256:0eeb1592f3abfd4f6320390e55fe846ecaca12728d28c3aebca782e42beee13c

Observation 4c66376b-e12d-4fb1-870e-8b2dd07b7e09 · outbound

This paper cites Representation Learning: A Review and New Perspectives.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Representation Learning: A Review and New Perspectives

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:08.415425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.415425Z digest=sha256:66403f91606b2a6ab855ad776d09d0521cd706e1ca5c6188d6dd4dbf87073c94

Observation ff34232d-a0b7-4049-a768-585942b7e4e2 · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:08.554249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.554249Z digest=sha256:4ef4e8ac7e7e3f23c3c316412fd29a51acf2c193abd92b00d128fe733fc8227c

Observation f994c6cf-c72c-4404-a646-0ece37399dd5 · outbound

This paper cites an unresolved cited work.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Unresolved cited work

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:08.722440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.722440Z digest=sha256:c3d443f3602e78b4272a079345e55675015492a98ebe042d40bbf95db6bf827e

Observation 60fc0b8e-1434-469a-a480-8b2789df5a8d · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection A Simple Framework for Contrastive Learning of Visual Representations

Reference 16

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unresolved
no resolver link, observed 2026-08-03T15:19:08.817961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.817961Z digest=sha256:36e893bc433dbbfe0c29841a671e9fd7b73abebd5473eb39b52523169300d4f5

Observation 7f0a38b2-b40c-484d-a4ae-806cfa061f8e · outbound

This paper cites Decoupled Weight Decay Regularization.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Decoupled Weight Decay Regularization

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:08.914476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.914476Z digest=sha256:e1bd055f836fdde113209d06b6d180f273e2555f5cbc250a43c6e603fe6edb0b

Observation f5a54f53-b867-457e-9114-b51e0c696597 · outbound

This paper cites Data Preprocessing for Supervised Learning.International Journal of Computer Science, 1(2):111–117, 2006.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Data Preprocessing for Supervised Learning.International Journal of Computer Science, 1(2):111–117, 2006

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-03T15:19:08.979847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:19:08.979847Z digest=sha256:dd56da1571b1654b10af86dc9d55acb3a3f488978165fb8c2a4467afc2659871

Pith citing papers

No inbound Pith citation observations are available.