Paper Citation Record · LEDGER

Unifying Adversarially Robust Model Experts in Vision-Language Models

As of 22 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2607.27897.

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pith.paper-citation-record.v1
2607.27897 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:31:51.830778Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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Outbound references

Observation bf45c194-9d57-4460-9aa2-5b56008eeef0 · outbound

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

Unifying Adversarially Robust Model Experts in Vision-Language Models Learning transferable visual models from natural language supervision,

Reference 1

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Observation fa8698d5-9f40-4378-be31-41093d69e6d6 · outbound

This paper cites On the adversarial robustness of multi- modal foundation models,.

Unifying Adversarially Robust Model Experts in Vision-Language Models On the adversarial robustness of multi- modal foundation models,

Reference 2

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Observation dc2b84ac-b237-4149-8aaa-88010b3a9ac0 · outbound

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

Unifying Adversarially Robust Model Experts in Vision-Language Models Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 3

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Observation b142ab8b-c190-4360-8b88-88f4d9791cd6 · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Theoretically principled trade-off between robustness and accuracy,

Reference 4

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

This paper cites Understanding Zero-Shot Adversarial Robustness for Large-Scale Models.

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

Reference 5

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Observation 8bbfbfea-6272-485c-92f9-0341c6359525 · outbound

This paper cites Text-guided attention is all you need for zero-shot robustness in vision-language models,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Text-guided attention is all you need for zero-shot robustness in vision-language models,

Reference 6

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Observation c69bc4c5-5da8-4ecc-960a-91b6b658c52d · outbound

This paper cites Generalist: Decoupling natural and robust gen- eralization,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Generalist: Decoupling natural and robust gen- eralization,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T23:31:48.045782Z

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Observation 7dca82ae-6007-4baa-8beb-6b7ee38a5710 · outbound

This paper cites Adversarially robust few-shot learning via parameter co-distillation of similarity and class concept learners,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Adversarially robust few-shot learning via parameter co-distillation of similarity and class concept learners,

Reference 8

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Observation 23c76e7d-b28d-4352-8554-e6cac7618b02 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 9

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Observation 8328cc14-6121-4743-b00d-d900d443491f · outbound

This paper cites Align before fuse: Vision and language representation learning with momentum distillation,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Align before fuse: Vision and language representation learning with momentum distillation,

Reference 10

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Observation 930400d6-7775-4bdd-9a5e-40669587a5a6 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Flamingo: a visual language model for few-shot learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T23:31:48.337097Z

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Observation e140a7c5-cf8f-4944-9770-730abd74c9a0 · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Unifying Adversarially Robust Model Experts in Vision-Language Models OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 12

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Observation 3c9bbc45-f05f-4c49-b8d4-f9dbf96cfdf0 · outbound

This paper cites Visual instruction tuning,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Visual instruction tuning,

Reference 13

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Observation 01cd3881-2913-40f6-9d2a-b266b17b7a22 · outbound

This paper cites Visual adversarial examples jailbreak large language models,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Visual adversarial examples jailbreak large language models,

Reference 14

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Observation b590d260-02a2-4efb-b141-04f4d658bbc9 · outbound

This paper cites Are aligned neural networks adversarially aligned?.

Unifying Adversarially Robust Model Experts in Vision-Language Models Are aligned neural networks adversarially aligned?

Reference 15

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Observation c26265c5-3f85-4a07-aff8-78c8f4f2ae1b · outbound

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

Unifying Adversarially Robust Model Experts in Vision-Language Models On evaluating adversarial robustness of large vision-language models,

Reference 16

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Observation 23df2e04-fbc3-44fa-973d-aadfbfe0538e · outbound

This paper cites Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast.

Unifying Adversarially Robust Model Experts in Vision-Language Models Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast

Reference 17

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Observation 2e6a60e7-9860-40e7-a013-d1d8feeb40c8 · outbound

This paper cites Pre-trained model guided fine-tuning for zero-shot adversarial robustness,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Pre-trained model guided fine-tuning for zero-shot adversarial robustness,

Reference 18

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

This paper cites Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models.

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

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Observation 294de650-8b27-4e78-bc99-87290e569616 · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time,

Reference 20

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Observation b7fd4dff-496a-40dd-bd17-f72a722fe4bf · outbound

This paper cites Editing Models with Task Arithmetic.

Unifying Adversarially Robust Model Experts in Vision-Language Models Editing Models with Task Arithmetic

Reference 21

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This paper cites Task arithmetic in the tangent space: Improved editing of pre-trained models,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Task arithmetic in the tangent space: Improved editing of pre-trained models,

Reference 22

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Observation f115bc2f-27dd-490f-a304-2cb538d67da8 · outbound

This paper cites Univer- sal adversarial perturbations,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Univer- sal adversarial perturbations,

Reference 23

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Observation 30ddb735-2032-410e-b37c-79e31f490014 · outbound

This paper cites Universal adversarial training,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Universal adversarial training,

Reference 24

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Observation 398336b2-a552-4372-9aca-f196740988a7 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Imagenet: A large-scale hierarchical image database,

Reference 25

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Observation 48819f16-29fc-4aff-a840-10c6dce7ed71 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 26

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Observation 6273e9e1-242d-49d7-aa3d-abbfaf67fa65 · outbound

This paper cites Decoupled weight decay regularization,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Decoupled weight decay regularization,

Reference 27

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Observation ea40a64b-5de2-4d99-a9f2-5d2b83512cf9 · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning,.

Unifying Adversarially Robust Model Experts in Vision-Language Models An analysis of single-layer networks in unsupervised feature learning,

Reference 28

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Observation cb37eafb-d292-4818-a706-8bbb8a043fea · outbound

This paper cites Learning multiple layers of features from tiny images,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Learning multiple layers of features from tiny images,

Reference 29

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Observation b129fb9d-04f4-44eb-b471-a85daafb6305 · outbound

This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,

Reference 30

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Unifying Adversarially Robust Model Experts in Vision-Language Models 3d object representations for fine-grained categorization,

Reference 31

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Observation c7016b0e-95ba-4d8d-ab86-0e0f3693777d · outbound

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Unifying Adversarially Robust Model Experts in Vision-Language Models Cats and dogs,

Reference 32

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Observation 6940ef55-4729-48be-90ce-3b99be28da29 · outbound

This paper cites Automated flower classification over a large number of classes,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Automated flower classification over a large number of classes,

Reference 33

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Observation 43a8e4b0-28ff-4a01-b084-d9c2d97bfc3c · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Unifying Adversarially Robust Model Experts in Vision-Language Models Fine-Grained Visual Classification of Aircraft

Reference 34

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Unifying Adversarially Robust Model Experts in Vision-Language Models Describing textures in the wild,

Reference 35

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This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classi- fication,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classi- fication,

Reference 36

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This paper cites Rotation equivariant cnns for digital pathology,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Rotation equivariant cnns for digital pathology,

Reference 37

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This paper cites The many faces of robustness: A critical analysis of out- of-distribution generalization,.

Unifying Adversarially Robust Model Experts in Vision-Language Models The many faces of robustness: A critical analysis of out- of-distribution generalization,

Reference 38

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Unifying Adversarially Robust Model Experts in Vision-Language Models Learning robust global representations by penalizing local predictive power,

Reference 39

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Unifying Adversarially Robust Model Experts in Vision-Language Models Microsoft coco: Common objects in context,

Reference 40

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This paper cites Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,

Reference 41

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This paper cites Towards vqa models that can read,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Towards vqa models that can read,

Reference 42

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Unifying Adversarially Robust Model Experts in Vision-Language Models Making the v in vqa matter: Elevating the role of image understanding in visual question answering,

Reference 43

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This paper cites Cider: Consensus- based image description evaluation,.

Unifying Adversarially Robust Model Experts in Vision-Language Models Cider: Consensus- based image description evaluation,

Reference 44

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Unifying Adversarially Robust Model Experts in Vision-Language Models Vqa: Visual question answering,

Reference 45

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