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Paper Citation Record · LEDGER

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

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 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 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:20:01.611489Z

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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  • verified fuzzy0
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  • malformed identifier0
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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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-23T22:09:19.917854Z digest=sha256:b3fb8bb6f7c5d633ca86c9b641137cda65fee1d738b32cf380a5d63ca5bc0840

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

Resolution
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=sha256:2ec9f168b4d8f2228d23e27ac8edba7162336f5e6d19ec2c949eafce7b94f0a4

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

Resolution
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:d8e8adaa386d0bd89695fc79d9f6bc5fd5d11da44689690a9662a2189173d826

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

Resolution
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:eda1e33b81728e772b276185b2491a9f61eadc3112c2e86b876cb290544de2f3

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:a90d541b85774ea5033fa074ad28f65ca667b96f9f4548800baf0ad4c66aa8bc

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=sha256:1c02f014710869b69ff9e4e48e174f7b261f9fe894aaf36fd9619ab4469c933f

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:a300d045c24611762572042d40a6ab5e2756c18a2d3fe348380ce3d8da8443ba

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:c529cac8b6fa8895934bd0562324eb91c509a1f2acdf5ff35d5b3f26ba224db0

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=sha256:89943fab9455b64bef933f8447b89f538b815f6ff753e116a5894c2df06c1ea6

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=sha256:99d0826f2fe2c07e53da68c06735013ba8736472064297b81de2c8861aaddb2d

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=sha256:09d7c53b39f5d5d95e904ebfcdc1ca2f3e70cdd0a2b208e945c38c82c0959c1b

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 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-25T21:18:05.054587Z digest=sha256:0beccb7c3e4b04533a7a9db596606428d0cca58f4a99dda77316831cf54a4136

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-29T04:05:22.253489Z digest=sha256:8b6455c865c5bb661caa40b908423cf4d96b513d64d9a9900eeec3fbce4f6632