MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection | Paper Citation Record

Observation e65cc139-483a-47a1-aefc-c7102deb3404 · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly detection and localization,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Cutpaste: Self-supervised learning for anomaly detection and localization,

Reference 1

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Observation 769e376f-14e1-4445-b198-4bc7ee5507a9 · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Anomaly detection via reverse distillation from one-class embedding,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.589946Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5548c839-790c-4696-aa00-240cbbf24577 · outbound

This paper cites Rethinking reverse distillation for multi-modal anomaly detection,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Rethinking reverse distillation for multi-modal anomaly detection,

Reference 3

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Observation cdd45c14-5540-4fc3-98ea-93ab69b4661e · outbound

This paper cites Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection

Reference 4

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Observation 9e6f27d0-2daa-4b81-a30d-0155ad0f89fb · outbound

This paper cites AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.568567Z

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Observation 2ac5746a-0fc3-45fd-9b69-87ebebf54811 · outbound

This paper cites Unsupervised surface anomaly detection with diffusion probabilistic model,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Unsupervised surface anomaly detection with diffusion probabilistic model,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.556546Z

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Observation f090464e-5b71-4e22-99bd-1f5bea658fff · outbound

This paper cites Anomaly Detection with Conditioned Denoising Diffusion Models.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 7

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Observation e1bf7a14-93f9-493e-acf7-6c5b28b376af · outbound

This paper cites DRÆM: A discriminatively trained reconstruction embedding for surface anomaly detection,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection DRÆM: A discriminatively trained reconstruction embedding for surface anomaly detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.541281Z

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Observation 2ad15073-9c3c-46f5-90e1-14f41523a58b · outbound

This paper cites Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.529121Z

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Observation 66db3ac6-5989-4f44-b11b-e1af13954468 · outbound

This paper cites Natural synthetic anomalies for self-supervised anomaly detection and localization,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Natural synthetic anomalies for self-supervised anomaly detection and localization,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.517398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bce5f4e0-0b27-4739-a8f4-8ef09e4e6938 · outbound

This paper cites GANomaly: Semi-supervised anomaly detection via adversarial training,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection GANomaly: Semi-supervised anomaly detection via adversarial training,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.506222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 71b56bc0-2f33-4057-bc55-df2642412796 · outbound

This paper cites f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.494479Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e256c9ab-9f08-4b6b-9c75-307b64767568 · outbound

This paper cites OCGAN: One-class novelty detection using gans with constrained latent representations,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection OCGAN: One-class novelty detection using gans with constrained latent representations,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.480790Z

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Observation e1f71017-be42-473e-809a-8f820185cc18 · outbound

This paper cites Deep learning for anomaly detection: A review,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Deep learning for anomaly detection: A review,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.467818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e01ad992-8c56-4057-89fb-9c7568c9ea4a · outbound

This paper cites Anomalydiffusion: Few-shot anomaly image generation with diffusion model,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Anomalydiffusion: Few-shot anomaly image generation with diffusion model,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.454566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0e089792-534c-4b8a-ab2e-fad2713ae5d8 · outbound

This paper cites Transfusion – a transparency-based diffusion model for anomaly detection,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Transfusion – a transparency-based diffusion model for anomaly detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.440862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fd36a2b3-664c-4da8-aa82-f0313cc6fa1f · outbound

This paper cites Enhancing anomaly detection via generating diversified and hard-to-distinguish synthetic anomalies,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Enhancing anomaly detection via generating diversified and hard-to-distinguish synthetic anomalies,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.425852Z

Source-reported events for the cited work

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Observation b25d6730-737b-43dc-aac2-c06f0c611394 · outbound

This paper cites Few-shot anomaly-driven generation for anomaly classifica- tion and segmentation,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Few-shot anomaly-driven generation for anomaly classifica- tion and segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.411896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 05830f84-a39e-4b31-af2c-9d960777af93 · outbound

This paper cites VOS: Learning what you don’t know by virtual outlier synthesis,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection VOS: Learning what you don’t know by virtual outlier synthesis,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.398893Z

Source-reported events for the cited work

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Observation a1a6acdb-decb-40e6-a906-e804839b17e8 · outbound

This paper cites Segment Anything.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Segment Anything

Reference 20

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Observation 0096ad01-f33a-4eab-a44e-fd8189de5d21 · outbound

This paper cites Multiquadric equations of topography and other irregular surfaces,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Multiquadric equations of topography and other irregular surfaces,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T12:23:55.736220Z

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Observation 21ffc526-406b-450e-9d7f-eb61a83bf149 · outbound

This paper cites A microscopic theory for antiphase boundary motion and its application to antiphase domain coarsening,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection A microscopic theory for antiphase boundary motion and its application to antiphase domain coarsening,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.386533Z

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Observation cabd3143-8e60-413d-b258-bd481c81839a · outbound

This paper cites Self-adaptive loss balanced physics-informed neural networks,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Self-adaptive loss balanced physics-informed neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.373834Z

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Observation 08a90872-da60-4ba9-ae0f-7387a16b6856 · outbound

This paper cites Theory of edge detection,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Theory of edge detection,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.361200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a02c3ae9-fdf8-4de5-8191-b2fc7fe4030f · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T12:23:55.751797Z

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Observation 8098d1df-2867-4568-8a82-37e151cf1e82 · outbound

This paper cites Spot-the-difference self-supervised pre-training for anomaly detection and segmentation,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Spot-the-difference self-supervised pre-training for anomaly detection and segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.340662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aee61a79-2901-4d72-bf96-3ba5210c1788 · outbound

This paper cites Vt-adl: A vision transformer network for image anomaly detection and localization,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Vt-adl: A vision transformer network for image anomaly detection and localization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.328991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 03547a71-6ca3-4416-9dd0-a4c8fac2f6ae · outbound

This paper cites Dsr – a dual subspace re-projection network for surface anomaly detection,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Dsr – a dual subspace re-projection network for surface anomaly detection,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.316541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d1f6c718-4642-4d88-bff9-6c0a9ef52258 · outbound

This paper cites Revisiting reverse distillation for anomaly detection,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Revisiting reverse distillation for anomaly detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.305201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 400cad0b-9b42-41cc-abd4-f80eb0a11a53 · outbound

This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T12:23:55.770015Z

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Observation 0f16e917-61a0-4e8f-b507-8037247db7ed · outbound

This paper cites SimpleNet: A Simple Network for Image Anomaly Detection and Localization.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection SimpleNet: A Simple Network for Image Anomaly Detection and Localization

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-16T12:23:55.998934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c3e1d0ef-3b6e-4708-b0b7-dcbef20efa8a · outbound

This paper cites Towards total recall in industrial anomaly detection: Feature memory readers,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Towards total recall in industrial anomaly detection: Feature memory readers,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.292684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 99dd22f3-d4c5-4912-a896-36e4df1749b6 · outbound

This paper cites Optimising area under the roc curve using gradient descent,.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Optimising area under the roc curve using gradient descent,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T12:23:55.781915Z

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Observation 01bd8cbe-d4af-4955-a407-bfeaf1b81cfd · outbound

This paper cites Meta-Learning with Implicit Gradients.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Meta-Learning with Implicit Gradients

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T12:23:55.785936Z

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Observation 45fcb24a-7323-4a97-b48d-3ee159fd6198 · outbound

This paper cites Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T12:23:55.790292Z

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Observation 966ca071-18f9-4311-afe3-46d980eb64e2 · outbound

This paper cites an unresolved cited work.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Unresolved cited work

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 418b2505-93ea-45a6-b465-574acc7d0dc1 · outbound

This paper cites an unresolved cited work.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:23:56.266628Z

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Observation 8f8c96ff-2810-44b0-8075-4184226ec209 · outbound

This paper cites Additional Regularization.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Additional Regularization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.254084Z

Source-reported events for the cited work

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Observation 4636dca3-1acf-46d1-bd4c-1a27c813cc35 · outbound

This paper cites an unresolved cited work.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:23:56.241882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b6ba8da7-eb2e-40b5-8af4-290838eec157 · outbound

This paper cites normal” and “anomaly.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection normal” and “anomaly

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.229877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4b98f9d7-e33a-492c-bc52-bd743ac0feb2 · outbound

This paper cites • Assign each point an initial direction θ, from which we derive dy = sinθ, dx = cosθ.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection • Assign each point an initial direction θ, from which we derive dy = sinθ, dx = cosθ

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.217367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1ec358e2-8b60-4510-a8ed-34e5926c56c7 · outbound

This paper cites • With a small probability, branch_prob, generate a branch by slightly shifting dy, dx.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection • With a small probability, branch_prob, generate a branch by slightly shifting dy, dx

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.205857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ffd8a180-8edd-4c1a-a001-d0282f8099ff · outbound

This paper cites eaten-away.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection eaten-away

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.192949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 98d934cb-7294-4882-90cc-e04ff82e94f0 · outbound

This paper cites • Generate polygons with deformed edges and fill them into a temporary mask.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection • Generate polygons with deformed edges and fill them into a temporary mask

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.176864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e7f63af3-a009-4a58-ab26-ca349d0d9761 · outbound

This paper cites an unresolved cited work.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:23:56.165085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8b3e9339-fc79-4ef6-96a3-8207363f824d · outbound

This paper cites stretched.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection stretched

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.151656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 66d5e855-3fda-44c0-bc93-2ac22a6d3a80 · outbound

This paper cites an unresolved cited work.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:23:56.136877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 17c07c66-0148-47ba-a36c-47b21314a001 · outbound

This paper cites • Else, compare to a constant color cref.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection • Else, compare to a constant color cref

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.122572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7b2a0fc6-bf72-42ce-bdcb-737a023257f1 · outbound

This paper cites an unresolved cited work.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:23:56.108739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d93f66b1-1ce0-47c1-8c10-fdf20ebae1f8 · outbound

This paper cites an unresolved cited work.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:23:56.096426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2166b444-49aa-4cdd-bbe0-437fa326b828 · outbound

This paper cites rust-like.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection rust-like

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:23:56.083067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers