Paper Citation Record · LEDGER

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers

As of 9 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2502.04679.

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

pith.paper-citation-record.v1
2502.04679 v1

Coverage vector

measured 56 of 56 reference resolution

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One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:32:42.835672Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T12:46:18.612622Z

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation f9a91be9-9744-42c5-8655-b34d5ddf1427 · outbound

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

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 1

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This paper cites Attention Is All You Need.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Attention Is All You Need

Reference 2

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This paper cites Imagenet classification with deep convolutional neural networks,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Imagenet classification with deep convolutional neural networks,

Reference 3

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This paper cites Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,

Reference 4

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This paper cites End-to-end object detection with transformers,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers End-to-end object detection with transformers,

Reference 5

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This paper cites Learning transferable visual models from natural language supervision,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Learning transferable visual models from natural language supervision,

Reference 6

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Observation a51bcb5d-6f3b-4089-ba7b-867a6b8adc6c · outbound

This paper cites Multimodal learning with transform- ers: A survey,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Multimodal learning with transform- ers: A survey,

Reference 7

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Observation dbfc1ad7-256f-4222-b440-73d090c7ddb8 · outbound

This paper cites On the Adversarial Robustness of Vision Transformers.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers On the Adversarial Robustness of Vision Transformers

Reference 8

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This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 9

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This paper cites Improving adversarial robustness requires revisiting misclassified examples,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Improving adversarial robustness requires revisiting misclassified examples,

Reference 10

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This paper cites Analyzing Transformers in Embedding Space.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Analyzing Transformers in Embedding Space

Reference 11

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This paper cites Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space

Reference 12

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This paper cites Do vision transformers see like convolutional neural networks?.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Do vision transformers see like convolutional neural networks?

Reference 13

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This paper cites Understanding and defending patched-based adversarial attacks for vision transformer,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Understanding and defending patched-based adversarial attacks for vision transformer,

Reference 14

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This paper cites Analyzing vision trans- formers for image classification in class embedding space,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Analyzing vision trans- formers for image classification in class embedding space,

Reference 15

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Observation a9b02c2c-f59e-4706-ba71-aabb534d0a05 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 16

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This paper cites Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition,

Reference 17

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This paper cites Geometric analysis and metric learning of instruction embeddings,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Geometric analysis and metric learning of instruction embeddings,

Reference 18

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Observation 18f1bd2b-c66e-4532-bad0-797c894675ac · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Representation Engineering: A Top-Down Approach to AI Transparency

Reference 19

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models

Reference 20

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This paper cites Intriguing Equivalence Structures of the Embedding Space of Vision Transformers.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Intriguing Equivalence Structures of the Embedding Space of Vision Transformers

Reference 21

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Towards evaluating the robustness of neural networks,

Reference 22

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Adversarial examples are not easily detected: Bypassing ten detection methods,

Reference 23

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Deepfool: a simple and accurate method to fool deep neural networks,

Reference 24

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Explaining and Harnessing Adversarial Examples

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Adversarial examples in the physical world,

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers The limitations of deep learning in adversarial settings,

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Transferability in machine learning: From phenomena to black-box attacks using adver- sarial samples,

Reference 28

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Exploring adversarial robustness of vision transformers in the spectral perspective,

Reference 29

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Understanding adversarial robustness of vision transformers via cauchy problem,

Reference 30

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Malicious Path Manipulations via Exploitation of Representation Vulnerabilities of Vision-Language Navigation Systems

Reference 31

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Towards transferable adversarial attacks on vision transformers,

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Dual stage black-box adversarial attack against vision transformer,

Reference 33

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers QuantAttack: Exploiting Dynamic Quantization to Attack Vision Transformers

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Transferable adversarial attacks on vision transformers with token gradient regularization,

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Universal Adversarial Triggers for Attacking and Analyzing NLP

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Adversarial Attacks on Large Language Models Using Regularized Relaxation

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Trade-off between robustness and accuracy of vision transformers,

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers When adversarial training meets vision transformers: Recipes from training to architec- ture,

Reference 40

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Harnessing edge information for improved ro- bustness in vision transformers,

Reference 42

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Random entangled tokens for adversarially robust vision transformer,

Reference 43

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Certified adversarial robustness via randomized smoothing,

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers {PatchGuard}: A provably robust defense against adversarial patches via small receptive fields and masking,

Reference 45

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Steering Language Models With Activation Engineering

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Pytorch image models,

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Imagenet: A large-scale hierarchical image database,

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Layer Normalization

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Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Deep residual learning for image recognition,

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This paper cites imagenette,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers imagenette,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:55:57.581998Z

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Observation 5d4093ba-ed64-42c2-a15f-6bb94355a49e · outbound

This paper cites Cifar-10 (canadian institute for advanced research),.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Cifar-10 (canadian institute for advanced research),

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:55:57.566160Z

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Observation c09b11c5-f1ae-411b-936a-bec7d105287e · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers Training data-efficient image transformers & distillation through attention,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T21:55:56.916516Z

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Observation 4aea6147-5d6e-49b5-bf36-8eb50b47c248 · outbound

This paper cites How deep learning sees the world: A survey on adversarial attacks & defenses,.

Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers How deep learning sees the world: A survey on adversarial attacks & defenses,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:55:57.540141Z

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Pith citing papers

Observation ba7d8b8f-5ee3-406a-9c61-a6431f2ded4b · inbound

DeepSeek on a Trip: Inducing Targeted Visual Hallucinations via Representation Vulnerabilities cites this paper.

DeepSeek on a Trip: Inducing Targeted Visual Hallucinations via Representation Vulnerabilities Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T11:32:42.835672Z

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Observation bf2af1ce-abfa-42f3-bc81-8f02eb3079d0 · inbound

Benign Overfitting in Adversarial Training for Vision Transformers cites this paper.

Benign Overfitting in Adversarial Training for Vision Transformers Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:46:18.695775Z

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