Paper Citation Record · LEDGER

Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2212.07016.

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

pith.paper-citation-record.v1
2212.07016 v2

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 2026-08-09T06:31:02.800959+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:26:14.191544Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:49:19.205616Z

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation 2b9b5496-ad13-4cc1-b52b-2c82d1c81616 · inbound

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models cites this paper.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T11:26:14.191544Z

Source-reported events for the cited work

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Observation 3c9334f8-ef3e-4a01-87b6-0517fc9ae847 · inbound

Diffusion-based Cumulative Adversarial Purification for Vision Language Models cites this paper.

Diffusion-based Cumulative Adversarial Purification for Vision Language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T10:59:18.856723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50dc4c1a-729e-4e73-b56c-9e13a115b502 · inbound

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers cites this paper.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.653891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 930dcebe-4299-4f62-b9e8-e9d884578b0a · inbound

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design cites this paper.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.952561Z

Source-reported events for the cited work

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Observation c31f7de3-a073-46ce-acec-d97b3c3ad0cf · inbound

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting cites this paper.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T12:41:48.093680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aeb04b1-b889-4573-a9dc-f2ecf94e00c6 · inbound

Pay Less Attention to Function Words for Free Robustness of Vision-Language Models cites this paper.

Pay Less Attention to Function Words for Free Robustness of Vision-Language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:33:44.721089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8ec4b980-6299-4d8f-9f69-0a8337c632f1 · inbound

Visual prompting reimagined: The power of the Activation Prompts cites this paper.

Visual prompting reimagined: The power of the Activation Prompts Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:45:53.965476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 02fc79ee-404a-4e62-9f59-8fdd0fcf2686 · inbound

Challenging Vision-Language Models with Physically Deployable Multimodal Semantic Lighting Attacks cites this paper.

Challenging Vision-Language Models with Physically Deployable Multimodal Semantic Lighting Attacks Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:31:04.973507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a845fa5-c632-48cf-8c88-baac7c80c9a5 · inbound

AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models cites this paper.

AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:38:56.019230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b7446e7a-e17c-47e4-9f59-4dcf993c9120 · inbound

Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models cites this paper.

Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.441778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation acadd145-67db-4195-a999-98b6382ddec5 · inbound

Investigating Adversarial Robustness of Multi-modal Large Language Models cites this paper.

Investigating Adversarial Robustness of Multi-modal Large Language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:06:27.636024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ba846b3a-5d58-49ff-b3e7-acd6b2641dbf · inbound

Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models cites this paper.

Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:16:26.823957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 24da8478-d867-4092-ad40-ec9f08a7b5fd · inbound

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception cites this paper.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.208818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3f627cba-7b9d-49ec-afeb-3fe58ee604b7 · inbound

Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness cites this paper.

Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-12T04:24:39.660564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15c12e7a-ee39-40c6-bcc1-aa48c37a08a1 · inbound

Unifying Adversarially Robust Model Experts in Vision-Language Models cites this paper.

Unifying Adversarially Robust Model Experts in Vision-Language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T23:31:47.764957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f7b1510-b6a1-428d-8c26-69058e3e5ce5 · inbound

Two Sides of the Same Coin: Co-Evolving Search for Cross-Task Attacks on Vision-Language Models cites this paper.

Two Sides of the Same Coin: Co-Evolving Search for Cross-Task Attacks on Vision-Language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:17.974506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.