Paper Citation Record · LEDGER

Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2402.12336.

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

pith.paper-citation-record.v1
2402.12336 v2

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:38:17.480616Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:59:07.063832Z

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External citation measurements

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Outbound references

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Pith citing papers

Observation d2f5efb4-3dbf-442c-b739-0e378715e711 · inbound

When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks for VLMs cites this paper.

When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks for VLMs Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 107

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Observation 0b0d825d-4486-4c91-b147-d572b13c2511 · inbound

A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations cites this paper.

A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 148

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Observation 60a5a2dd-ed63-4bae-94e8-2a741ef83965 · inbound

Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment cites this paper.

Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 25

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Observation ebf584df-d99f-4edd-a28a-dc9ee716d36c · 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 Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 69

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Observation 7bf6b250-1b9c-40ed-92e4-0b3daabdcbca · inbound

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

Diffusion-based Cumulative Adversarial Purification for Vision Language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 48

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Observation 0f6951c7-d207-492e-a4ee-d99974500a89 · inbound

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents cites this paper.

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 154

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Observation 40c52470-0a62-4685-8bf2-293a3fe76fa5 · inbound

Quality Text, Robust Vision: The Role of Language in Enhancing Visual Robustness of Vision-Language Models cites this paper.

Quality Text, Robust Vision: The Role of Language in Enhancing Visual Robustness of Vision-Language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 2024

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Observation 44628e35-95c1-46cb-a172-88e77b633620 · inbound

Invisible Injections: Exploiting Vision-Language Models Through Steganographic Prompt Embedding cites this paper.

Invisible Injections: Exploiting Vision-Language Models Through Steganographic Prompt Embedding Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 54

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Observation 42f5acdd-0d3a-4206-9f1f-8a3e676722b5 · inbound

Beyond the Textual: Generating Coherent Visual Options for MCQs cites this paper.

Beyond the Textual: Generating Coherent Visual Options for MCQs Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 48

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Observation 9d741b0b-7ea8-47c8-b0a1-15ab48bef964 · inbound

ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models cites this paper.

ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.866893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1f6c570e-8d54-4f01-bab1-e703eb2c4c22 · inbound

ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models cites this paper.

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:30:39.404154Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 79c270ba-1df5-424b-9319-3061f87632e6 · 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 Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 41

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Observation 07b7e299-dd12-43fa-9994-0c7e0a87f8b5 · inbound

Breaking the Illusion: Consensus-Based Generative Mitigation of Adversarial Illusions in Multi-Modal Embeddings cites this paper.

Breaking the Illusion: Consensus-Based Generative Mitigation of Adversarial Illusions in Multi-Modal Embeddings Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 23

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Observation 47352615-a4e4-4a93-941d-2173c1eb5178 · 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 Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T00:33:44.684493Z

Source-reported events for the cited work

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Observation 18c41af6-dc16-4dfd-9daf-0e3ec428f094 · inbound

Hierarchically Robust Zero-shot Vision-language Models cites this paper.

Hierarchically Robust Zero-shot Vision-language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 43

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2de6a4d2-d14f-4c8e-a78b-2f7dec54061f · inbound

VisInject: Disruption != Injection -- A Dual-Dimension Evaluation of Universal Adversarial Attacks on Vision-Language Models cites this paper.

VisInject: Disruption != Injection -- A Dual-Dimension Evaluation of Universal Adversarial Attacks on Vision-Language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:56:08.096327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bf82e486-a7ba-410e-829d-9b0784ee6381 · inbound

TARO: Temporal Adversarial Rectification Optimization Using Diffusion Models as Purifiers cites this paper.

TARO: Temporal Adversarial Rectification Optimization Using Diffusion Models as Purifiers Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:26:30.625340Z

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Observation 8fad4a87-bd7c-45d5-90b0-8083bf4bd21b · 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 Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T19:38:56.014305Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e2c317cd-cc9f-4d88-a85a-0070af298475 · 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 Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T22:44:01.424531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88d00781-61dc-4169-be5e-9483b0047586 · inbound

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

Investigating Adversarial Robustness of Multi-modal Large Language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:06:27.643485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8c1a1912-7c5a-4c20-9366-be2a7de70aca · 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 Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:16:26.844167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 15bcbbc8-c1d1-4e41-b3bb-1cf236b83bef · inbound

Exploring Adversarial Robustness and Safety Alignment in Multilingual Multi-Modal Large Language Models cites this paper.

Exploring Adversarial Robustness and Safety Alignment in Multilingual Multi-Modal Large Language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T03:16:34.811575Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 89952cc8-2263-4c1c-b85d-c46a9375ba8c · inbound

Semantic Robustness Certification for Vision-Language Models cites this paper.

Semantic Robustness Certification for Vision-Language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T23:59:07.065828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 538d2546-01bd-4239-a8ac-34713d4da38b · inbound

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

Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 43

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Observation 4ac2d30b-6440-46da-a1cd-4dd36c0c6ef1 · inbound

A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning cites this paper.

A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T01:16:40.492300Z

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Observation 45b3fb0f-1089-4e95-9caf-ba4113ac01b8 · inbound

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

Unifying Adversarially Robust Model Experts in Vision-Language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-31T23:31:48.797271Z

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Observation 8da3932e-745e-4220-96c4-9f02a0a37b5f · 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 Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T13:51:18.042445Z

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