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Paper Citation Record · LEDGER

MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2404.06564.

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

pith.paper-citation-record.v1
2404.06564 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:42:38.052484Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:30:07.511909Z

Reference resolution

0 of 0 outbound references displayed

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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 3fe34fa4-5748-4ce5-98e2-5bcc9d672ed0 · inbound

A Survey of Mamba cites this paper.

A Survey of Mamba MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 68

Resolution
verified exact
arxiv_id, observed 2026-05-23T22:13:31.067621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-23T22:09:19.917854Z digest=sha256:a17bb31e53d61540f0c2785b371cd95c3967cf22e688959ae2f65cf25590bbb5

Observation 61d363e2-8650-4f9c-95e8-a155e8b296c8 · inbound

MambaVLT: Time-Evolving Multimodal State Space Model for Vision-Language Tracking cites this paper.

MambaVLT: Time-Evolving Multimodal State Space Model for Vision-Language Tracking MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 21

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unresolved
no resolver link, observed 2026-08-12T14:20:01.611489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:20:01.611489Z digest=sha256:07d8489fcfe98cb151d902de1dc4792d23593a83abd5d11a808574b29629a060

Observation 22620cb1-8ed5-471d-9110-12afe15a998d · inbound

Fab-ME: A Vision State-Space and Attention-Enhanced Framework for Fabric Defect Detection cites this paper.

Fab-ME: A Vision State-Space and Attention-Enhanced Framework for Fabric Defect Detection MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 24

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unresolved
no resolver link, observed 2026-08-11T22:44:39.887244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:44:39.887244Z digest=sha256:8e87260e7c78317e3f4e6fe71b3d87a283b956311feb9e4cd6676844b6411c5e

Observation 8016844c-8074-43e6-ac6c-97465382bbf7 · inbound

LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity cites this paper.

LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 15

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unresolved
no resolver link, observed 2026-08-11T16:43:08.260634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:43:08.260634Z digest=sha256:993ec267548bda1d91b5512cc200dfbc46cf1817cb6185c5b0b7f9578c0f3798

Observation c41b1e96-4ebd-49ff-84e4-a83ccdbcef07 · inbound

CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection cites this paper.

CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-10T22:57:42.638706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:57:42.638706Z digest=sha256:39c0e9e9d3cbc2ceb09265e5987c77189074ccd1291a5e4a9626b5c5af2f533d

Observation 314d1dac-a74d-40fa-b26c-4208168aeb2f · inbound

UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentation cites this paper.

UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentation MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-09T13:41:40.311208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:41:40.311208Z digest=sha256:a4ff71a09f7d6830409d2505b3b7c6bbe4b2bf726df84b73613cc46eeda91107

Observation bafae42c-eb15-43a7-b62f-0026e9d9b315 · inbound

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

ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-15T22:42:38.052484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:42:38.052484Z digest=sha256:82d4f89072d47957b2eb37f4a2d8433e7e2602734e02c522ddf381d210729d6e

Observation 69b9770b-1e15-474e-826f-9a548fac62f8 · inbound

Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays cites this paper.

Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T14:52:19.184006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:52:19.184006Z digest=sha256:45bfabb9d8e81cf6adca57be9824590afeda268abde225c51c9117e9f2f38aef

Observation 67260346-f999-434b-be5b-6c1d614998be · inbound

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning cites this paper.

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T13:20:59.365566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:20:59.365566Z digest=sha256:90088ca3f6db5be0c5136cfc5778f7fffd0565509b5d7a4c038357a8a88826c4

Observation 767a09a1-6ef6-41ac-9951-1431bc1568aa · inbound

ECP-Mamba: An Efficient Multi-scale Self-supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification cites this paper.

ECP-Mamba: An Efficient Multi-scale Self-supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T11:56:32.743179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:56:32.743179Z digest=sha256:f8d1fe8b451fe0d0750c210d6558045b5d9278856f52cf35eb319d1c8c5bc180

Observation 3239410d-b08d-4134-a824-3cc38cb16f6d · inbound

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection cites this paper.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 11

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unresolved
no resolver link, observed 2026-08-15T20:09:17.241748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:09:17.241748Z digest=sha256:86f693fee82105e2ca76e03fc3ab7c962a04c305501ddcdf726c26c92bd076d3

Observation 4453393b-e76d-40f0-9770-b265ece90248 · inbound

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection cites this paper.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T17:27:15.293118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:27:15.293118Z digest=sha256:8a80e5c2993b27ecbc96c986f361c0ccc25633754696f802f3671c34ff98eaa6

Observation 16a2eb6d-608a-47b3-ab82-fa8ff121441c · inbound

SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection cites this paper.

SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T18:05:35.397980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:05:35.397980Z digest=sha256:44e9d85b1533a034c4df876087ee066cdbc4639f7e4ff27f02c4d9c5c0dbe1f0

Observation 343485b9-2cc9-4e65-a8b8-23f08e762b98 · inbound

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization cites this paper.

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T00:53:23.947150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:53:23.947150Z digest=sha256:2d8c085934453134c85b56a87e54fbfd0d170d4f12f79278d25cc024e271a43f

Observation 479e1a83-c87b-4361-b51b-08bc13750d45 · inbound

State Space Models Meet Remote Sensing: A Survey cites this paper.

State Space Models Meet Remote Sensing: A Survey MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 120

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T19:30:07.513359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-25T21:18:05.054587Z digest=sha256:4008ddafa85cd0f9aeb3632f81956aac01e82d6b7b92917d8780295b4f288e94

Observation 45e4c36c-afe4-4de9-8db4-7d81359ad97f · inbound

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation cites this paper.

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 21

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T17:15:50.938022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-29T04:05:22.253489Z digest=sha256:bc987c828d727715583b8dfc055f3de7f14c9239f386eaab99c8845e376e7f86