Paper Citation Record · LEDGER

Segment Any Anomaly without Training via Hybrid Prompt Regularization

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2305.10724.

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

pith.paper-citation-record.v1
2305.10724 v1

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 2026-08-12T12:29:19.429823Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T19:43:23.424586Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 6345bb97-637b-4dba-aaa2-12e76de80476 · inbound

On Efficient Variants of Segment Anything Model: A Survey cites this paper.

On Efficient Variants of Segment Anything Model: A Survey Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:43:23.427692Z

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.

Observation c41e0887-b6c6-40e2-8cfb-edaac03d7db8 · inbound

Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning cites this paper.

Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T12:26:30.578037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52a95491-c158-4d6e-a5b4-df770b86a88f · inbound

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models cites this paper.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T12:29:19.429823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cc674c7-5225-4d4e-b9d0-77ecf5a43ac2 · inbound

ONER: Online Experience Replay for Incremental Anomaly Detection cites this paper.

ONER: Online Experience Replay for Incremental Anomaly Detection Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T22:03:23.793173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2627b121-1dfc-46e9-b2da-d99912abc3e8 · inbound

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

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:57.206576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7fed61d-21a5-43b6-a2ca-1e98c712a0e5 · inbound

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding cites this paper.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:10.657850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 114501ed-7062-4436-9b55-7d5d58f6b26f · inbound

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain cites this paper.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:33.651441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fb5c67c-4239-422b-9ea9-4cc960841783 · inbound

Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling cites this paper.

Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:25.970590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9145053-1984-4fb8-9cf4-693e1e2b2947 · inbound

StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection cites this paper.

StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T21:43:26.817027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afd10c94-7f5b-4916-9bbf-1cebbcd3b111 · inbound

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts cites this paper.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.386401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75466d63-1c17-42cd-a491-c56ded15098d · inbound

SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM cites this paper.

SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T20:27:24.760227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6917ce3d-6cb6-4f74-ad47-bbe75c2e1f0a · inbound

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors cites this paper.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-16T10:47:45.444905Z

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.

Observation e06e62e5-fa2e-4b60-97e3-e2b687f1e00e · inbound

Closed form perturbative relativistic modifications to wave-packet dynamics in the quantum harmonic oscillator cites this paper.

Closed form perturbative relativistic modifications to wave-packet dynamics in the quantum harmonic oscillator Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-15T12:56:18.518533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.