Paper Citation Record · LEDGER

Coordinated Robustness Evaluation Framework for Vision-Language Models

As of 7 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2506.05429.

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

pith.paper-citation-record.v1
2506.05429 v1

Coverage vector

measured 50 of 50 reference resolution

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measured 51 of 51 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:28:23.793963Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:28:25.701065Z

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation 5b569dc3-8a41-48d0-8a53-d040cac356dc · outbound

This paper cites Advances in adversarial attacks and defenses in com- puter vision: A survey.

Coordinated Robustness Evaluation Framework for Vision-Language Models Advances in adversarial attacks and defenses in com- puter vision: A survey

Reference 1

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Observation d5e36efc-678e-4039-ab39-d2f006be3073 · outbound

This paper cites Square attack: a query-efficient black-box adversarial attack via random search.

Coordinated Robustness Evaluation Framework for Vision-Language Models Square attack: a query-efficient black-box adversarial attack via random search

Reference 2

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Observation 7e22fc29-1fd9-40da-9946-71b0a6b19b94 · outbound

This paper cites A survey on adversarial attacks and defences.

Coordinated Robustness Evaluation Framework for Vision-Language Models A survey on adversarial attacks and defences

Reference 3

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Observation 99310f2d-5c31-4867-91d4-c709f0079e0b · outbound

This paper cites Textguise: Adaptive adversarial example attacks on text classification model.

Coordinated Robustness Evaluation Framework for Vision-Language Models Textguise: Adaptive adversarial example attacks on text classification model

Reference 4

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Observation e3384d94-2c18-4a75-846b-3e951464fb29 · outbound

This paper cites Instructblip: Towards general-purpose vision-language models with instruction tuning.

Coordinated Robustness Evaluation Framework for Vision-Language Models Instructblip: Towards general-purpose vision-language models with instruction tuning

Reference 5

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Observation 8a77fab5-a699-41ed-965e-18c4d01e4265 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Coordinated Robustness Evaluation Framework for Vision-Language Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

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Observation 443e5018-ddd0-4d5b-a0ea-30c25dc409c3 · outbound

This paper cites Fda: Feature disruptive attack.

Coordinated Robustness Evaluation Framework for Vision-Language Models Fda: Feature disruptive attack

Reference 7

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Observation 7813e8be-082b-4816-b1a6-f0afd2f5bdf7 · outbound

This paper cites BAE: BERT-based Adversarial Examples for Text Classification.

Coordinated Robustness Evaluation Framework for Vision-Language Models BAE: BERT-based Adversarial Examples for Text Classification

Reference 8

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Observation 1574f963-aa63-485f-86d3-978f90b78b8f · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answering.

Coordinated Robustness Evaluation Framework for Vision-Language Models Making the v in vqa matter: Elevating the role of image understanding in visual question answering

Reference 9

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Observation 5d8fc5e9-149b-498d-b633-bf5933166c36 · outbound

This paper cites Adversarial Attack and Defense of Structured Prediction Models.

Coordinated Robustness Evaluation Framework for Vision-Language Models Adversarial Attack and Defense of Structured Prediction Models

Reference 10

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Observation dabdfb06-0705-4516-9683-3d0d7b3895ae · outbound

This paper cites Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!.

Coordinated Robustness Evaluation Framework for Vision-Language Models Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!

Reference 11

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Observation 28df7976-4bfe-4047-8a3a-38930fb45386 · outbound

This paper cites Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs.

Coordinated Robustness Evaluation Framework for Vision-Language Models Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs

Reference 12

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Observation 702ffb37-ebe4-4cc0-94cd-57510549c0ff · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Coordinated Robustness Evaluation Framework for Vision-Language Models Categorical Reparameterization with Gumbel-Softmax

Reference 13

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This paper cites Is bert really robust? a strong baseline for natural language attack on text classification and entailment.

Coordinated Robustness Evaluation Framework for Vision-Language Models Is bert really robust? a strong baseline for natural language attack on text classification and entailment

Reference 14

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Observation dcb91e86-a761-41d0-91d6-b325ed690eb4 · outbound

This paper cites Adversarial deep learning: A survey on adversarial attacks and defense mech- anisms on image classification.

Coordinated Robustness Evaluation Framework for Vision-Language Models Adversarial deep learning: A survey on adversarial attacks and defense mech- anisms on image classification

Reference 15

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Observation 67f5a72e-63a8-4ecf-b471-ac9528398ef2 · outbound

This paper cites Vilt: Vision-and- language transformer without convolution or region supervi- sion.

Coordinated Robustness Evaluation Framework for Vision-Language Models Vilt: Vision-and- language transformer without convolution or region supervi- sion

Reference 16

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Observation c4a4c9fe-1914-41ae-ad4a-78b1e5c97543 · outbound

This paper cites Contextualized Perturbation for Textual Adversarial Attack.

Coordinated Robustness Evaluation Framework for Vision-Language Models Contextualized Perturbation for Textual Adversarial Attack

Reference 17

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Observation 61645836-3be7-432c-a1d3-b5d61603a0d0 · outbound

This paper cites BERT-ATTACK: Adversarial Attack Against BERT Using BERT.

Coordinated Robustness Evaluation Framework for Vision-Language Models BERT-ATTACK: Adversarial Attack Against BERT Using BERT

Reference 18

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This paper cites Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models.

Coordinated Robustness Evaluation Framework for Vision-Language Models Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models

Reference 19

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This paper cites Improved Baselines with Visual Instruction Tuning.

Coordinated Robustness Evaluation Framework for Vision-Language Models Improved Baselines with Visual Instruction Tuning

Reference 20

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This paper cites Llava-next: Improved reason- ing, ocr, and world knowledge, 2024.

Coordinated Robustness Evaluation Framework for Vision-Language Models Llava-next: Improved reason- ing, ocr, and world knowledge, 2024

Reference 21

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This paper cites Deepfool: a simple and accurate method to fool deep neural networks.

Coordinated Robustness Evaluation Framework for Vision-Language Models Deepfool: a simple and accurate method to fool deep neural networks

Reference 22

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This paper cites A self-supervised approach for adversarial robustness.

Coordinated Robustness Evaluation Framework for Vision-Language Models A self-supervised approach for adversarial robustness

Reference 23

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Coordinated Robustness Evaluation Framework for Vision-Language Models Crafting adversarial input sequences for recurrent neural net- works

Reference 24

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Coordinated Robustness Evaluation Framework for Vision-Language Models Measuring robustness with black-box adversarial attack using reinforcement learning

Reference 25

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Coordinated Robustness Evaluation Framework for Vision-Language Models Rl-cam: Visual explana- tions for convolutional networks using reinforcement learning

Reference 26

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Coordinated Robustness Evaluation Framework for Vision-Language Models Robustness with query- efficient adversarial attack using reinforcement learning

Reference 27

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Coordinated Robustness Evaluation Framework for Vision-Language Models Reinforcement learning based black-box adversarial attack for robustness improvement

Reference 28

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Coordinated Robustness Evaluation Framework for Vision-Language Models Robustness with black-box adversarial attack using reinforce- ment learning

Reference 29

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This paper cites Benchmark generation framework with customizable distortions for image classifier robustness.

Coordinated Robustness Evaluation Framework for Vision-Language Models Benchmark generation framework with customizable distortions for image classifier robustness

Reference 30

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This paper cites Robustness and visual explanation for black box image, video, and ecg signal classification with reinforcement learning.

Coordinated Robustness Evaluation Framework for Vision-Language Models Robustness and visual explanation for black box image, video, and ecg signal classification with reinforcement learning

Reference 31

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Coordinated Robustness Evaluation Framework for Vision-Language Models Reinforcement learning platform for adversarial black- box attacks with custom distortion filters

Reference 32

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This paper cites A Corpus for Reasoning About Natural Language Grounded in Photographs.

Coordinated Robustness Evaluation Framework for Vision-Language Models A Corpus for Reasoning About Natural Language Grounded in Photographs

Reference 33

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Coordinated Robustness Evaluation Framework for Vision-Language Models Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT

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Coordinated Robustness Evaluation Framework for Vision-Language Models Intriguing properties of neural networks

Reference 35

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Coordinated Robustness Evaluation Framework for Vision-Language Models Autoattacker: A reinforcement learning approach for black- box adversarial attacks

Reference 36

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Observation ce50ea18-4424-4be7-b824-0fd9ed4e1f58 · outbound

This paper cites GIT: A Generative Image-to-text Transformer for Vision and Language.

Coordinated Robustness Evaluation Framework for Vision-Language Models GIT: A Generative Image-to-text Transformer for Vision and Language

Reference 37

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This paper cites CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation.

Coordinated Robustness Evaluation Framework for Vision-Language Models CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation

Reference 38

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This paper cites Towards a Robust Deep Neural Network in Texts: A Survey.

Coordinated Robustness Evaluation Framework for Vision-Language Models Towards a Robust Deep Neural Network in Texts: A Survey

Reference 39

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Coordinated Robustness Evaluation Framework for Vision-Language Models Measure and Improve Robustness in NLP Models: A Survey

Reference 40

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Coordinated Robustness Evaluation Framework for Vision-Language Models Adversarial attacks and defenses in images, graphs and text: A review

Reference 41

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This paper cites R&R: Metric-guided Adversarial Sentence Generation.

Coordinated Robustness Evaluation Framework for Vision-Language Models R&R: Metric-guided Adversarial Sentence Generation

Reference 42

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Coordinated Robustness Evaluation Framework for Vision-Language Models Natural attack for pre-trained models of code

Reference 43

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This paper cites Texthoaxer: Budgeted hard-label adversarial attacks on text.

Coordinated Robustness Evaluation Framework for Vision-Language Models Texthoaxer: Budgeted hard-label adversarial attacks on text

Reference 44

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This paper cites VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models.

Coordinated Robustness Evaluation Framework for Vision-Language Models VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

Reference 45

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Coordinated Robustness Evaluation Framework for Vision-Language Models Towards adversarial at- tack on vision-language pre-training models

Reference 46

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This paper cites BERTScore: Evaluating Text Generation with BERT.

Coordinated Robustness Evaluation Framework for Vision-Language Models BERTScore: Evaluating Text Generation with BERT

Reference 47

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This paper cites Adversarial attacks on deep-learning models in nat- ural language processing: A survey.

Coordinated Robustness Evaluation Framework for Vision-Language Models Adversarial attacks on deep-learning models in nat- ural language processing: A survey

Reference 48

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Observation 9b2af941-c6c7-4cc0-9de3-0f2e91ebb556 · outbound

This paper cites On evaluating adversarial robustness of large vision-language models.

Coordinated Robustness Evaluation Framework for Vision-Language Models On evaluating adversarial robustness of large vision-language models

Reference 49

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Observation 48fb0b46-4211-47e3-ade7-8dedcfe9dfa8 · outbound

This paper cites Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective.

Coordinated Robustness Evaluation Framework for Vision-Language Models Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective

Reference 50

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Pith citing papers

Observation ffbaa1ea-698a-4b8d-9411-1e26d89989ee · inbound

Evaluating the Diagnostic Robustness of Vision-Language Models Under Visual and Textual Perturbations cites this paper.

Evaluating the Diagnostic Robustness of Vision-Language Models Under Visual and Textual Perturbations Coordinated Robustness Evaluation Framework for Vision-Language Models

Reference 12

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