Paper Citation Record · LEDGER

Analysing the Robustness of Vision-Language-Models to Common Corruptions

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 6 inbound Pith citation observations for arXiv:2504.13690.

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

pith.paper-citation-record.v1
2504.13690 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:04:22.020535Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:22:04.563578Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T21:00:09.515721Z

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy23
unresolved15
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 53884e7d-663e-4487-a5fd-816e3a257597 · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Benchmarking neural network robustness to common corruptions and perturbations,

Reference 1

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Observation 399d1dee-1320-4e63-86e3-39dddaea976e · outbound

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

Analysing the Robustness of Vision-Language-Models to Common Corruptions Learning transferable visual models from natural language supervision,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.326320Z

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Observation 32a891c9-1b94-40c6-884c-c061bb702925 · outbound

This paper cites LXMERT: Learning Cross-Modality Encoder Representations from Transformers.

Analysing the Robustness of Vision-Language-Models to Common Corruptions LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Reference 3

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Observation 45cc0189-7c75-41d6-bbd0-1e5fa7e5c829 · outbound

This paper cites Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey

Reference 4

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Observation ee3353c6-803c-414a-86a5-d9e3416dfbd5 · outbound

This paper cites Uninet: Unified architecture search with convolution, transformer, and mlp,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Uninet: Unified architecture search with convolution, transformer, and mlp,

Reference 5

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Observation 18a1b0be-1307-48c2-bd66-66ba9d872568 · outbound

This paper cites Image difference captioning with pre-training and contrastive learning,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Image difference captioning with pre-training and contrastive learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.310092Z

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Observation f6bae221-e36b-4347-8fd8-0e428cfa997d · outbound

This paper cites MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER.

Analysing the Robustness of Vision-Language-Models to Common Corruptions MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

Reference 7

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Observation 2a6e0582-47b5-401e-bf6d-ed99641cd119 · outbound

This paper cites Point-bert: Pre-training 3d point cloud transformers with masked point modeling,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Point-bert: Pre-training 3d point cloud transformers with masked point modeling,

Reference 8

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Observation 8f64a85f-ce5a-4de0-acd1-d4b4aaf8d553 · outbound

This paper cites Ensemble deep learning for automated visual classification using eeg signals,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Ensemble deep learning for automated visual classification using eeg signals,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.293121Z

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Observation 0076507b-c38e-4b3c-aa84-db30bbd2ae78 · outbound

This paper cites Lit: Zero-shot transfer with locked-image text tuning,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Lit: Zero-shot transfer with locked-image text tuning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.284442Z

Source-reported events for the cited work

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Observation ab689c34-d652-48d4-9715-886058522ab3 · outbound

This paper cites Multimodal few-shot learning with frozen language models,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Multimodal few-shot learning with frozen language models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.276437Z

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Observation eb69c2e4-bcb1-4ed2-976a-f12524b3f9cd · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen im- age encoders and large language models,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Blip-2: Bootstrapping language-image pre-training with frozen im- age encoders and large language models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.268406Z

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Observation a1a7ba43-3ad2-4508-ab76-8847896dcf46 · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Bert: Pre-training of deep bidirectional trans- formers for language understanding,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.260232Z

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Observation 4079734a-cbdd-45b4-b4bd-fa8edfda220d · outbound

This paper cites Language models are unsu- pervised multitask learners,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Language models are unsu- pervised multitask learners,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.251988Z

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Observation f2b6acca-92b0-4111-84d4-61c0af404d2a · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 15

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Observation b2e42023-4e5e-4cf8-8d5d-15d74de31d44 · outbound

This paper cites Language models are few- shot learners,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Language models are few- shot learners,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.239455Z

Source-reported events for the cited work

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Observation 51881d07-de52-433b-a8ff-ef828f30af07 · outbound

This paper cites Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 17

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Observation 4f9f011b-090f-4e85-b5e0-3c786ed8ccaa · outbound

This paper cites Training Compute-Optimal Large Language Models.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Training Compute-Optimal Large Language Models

Reference 18

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This paper cites Palm: Scaling language modeling with pathways,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Palm: Scaling language modeling with pathways,

Reference 19

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Observation 776989c8-a9f6-495a-9768-627b35e3b5b8 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Analysing the Robustness of Vision-Language-Models to Common Corruptions OPT: Open Pre-trained Transformer Language Models

Reference 20

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Observation 0ffee348-12cd-4681-9bec-c6d5de938996 · outbound

This paper cites BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

Analysing the Robustness of Vision-Language-Models to Common Corruptions BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T12:04:21.968804Z

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Observation bbcbfd4b-f48c-4b70-b85e-5a1e7c990ae3 · outbound

This paper cites Emergent Abilities of Large Language Models.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Emergent Abilities of Large Language Models

Reference 22

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Observation 537a4ab1-48df-4047-9519-cbb2d8a8898f · outbound

This paper cites Training language models to follow instructions with human feedback,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Training language models to follow instructions with human feedback,

Reference 23

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Observation a568efa8-c177-499a-b7f9-4468ad956572 · outbound

This paper cites Openai: Introducing chatgpt.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Openai: Introducing chatgpt

Reference 24

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Observation 5f4c8e32-4805-41b4-bd50-c840b803d9a8 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Analysing the Robustness of Vision-Language-Models to Common Corruptions LLaMA: Open and Efficient Foundation Language Models

Reference 25

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Observation bd0cbe93-fbd7-45b1-bb7e-b830e121f4f1 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,

Reference 26

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Observation b368c416-0f33-4116-9304-d6fe109ed538 · outbound

This paper cites Visual instruction tuning,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Visual instruction tuning,

Reference 27

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Observation c467ae4d-f04c-4027-95c6-e6f94bbb06d1 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Analysing the Robustness of Vision-Language-Models to Common Corruptions MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 28

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Observation 673a64dc-4b4f-4b92-8f62-5464b8fd7d89 · outbound

This paper cites Visualgpt: Data-efficient adaptation of pretrained language models for image captioning,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Visualgpt: Data-efficient adaptation of pretrained language models for image captioning,

Reference 29

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Observation 3933642d-9dd2-4752-b265-6342d48170f2 · outbound

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

Analysing the Robustness of Vision-Language-Models to Common Corruptions Flamingo: a visual language model for few-shot learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.189694Z

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This paper cites PaLM-E: An Embodied Multimodal Language Model.

Analysing the Robustness of Vision-Language-Models to Common Corruptions PaLM-E: An Embodied Multimodal Language Model

Reference 31

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This paper cites GPT-4 Technical Report.

Analysing the Robustness of Vision-Language-Models to Common Corruptions GPT-4 Technical Report

Reference 32

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Observation bdae2b8a-04c6-4e5a-8836-8069d7f44d71 · outbound

This paper cites One pixel attack for fooling deep neural networks,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions One pixel attack for fooling deep neural networks,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.180578Z

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Observation 660d8dff-3c83-430c-ad11-5056a24ef551 · outbound

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

Analysing the Robustness of Vision-Language-Models to Common Corruptions Deepfool: a simple and accurate method to fool deep neural networks,

Reference 34

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Observation 032b371c-ba94-4148-9615-522198b807d9 · outbound

This paper cites Tracing the origin of adversar- ial attack for forensic investigation and deterrence,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Tracing the origin of adversar- ial attack for forensic investigation and deterrence,

Reference 35

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Source-reported events for the cited work

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Observation 8c42b068-ce59-4e1c-9ff1-e9a73ab92709 · outbound

This paper cites The best defense is a good offense: Adversarial augmentation against adversar- ial attacks,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions The best defense is a good offense: Adversarial augmentation against adversar- ial attacks,

Reference 36

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Observation 29fb3b02-01a2-42f7-b213-0f497fc8e75a · outbound

This paper cites Defending against patch-based backdoor attacks on self-supervised learning,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Defending against patch-based backdoor attacks on self-supervised learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:22.147067Z

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Observation 6a96b7d6-5343-4760-ae65-b0fea024f273 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Explaining and Harnessing Adversarial Examples

Reference 38

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Observation fcf6f922-93ae-48b1-ace1-edda92fa1674 · outbound

This paper cites Sibling-attack: Rethinking transferable adversarial attacks against face recognition,.

Analysing the Robustness of Vision-Language-Models to Common Corruptions Sibling-attack: Rethinking transferable adversarial attacks against face recognition,

Reference 39

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Pith citing papers

Observation 6347e853-a133-481b-b9c4-788953edc8de · inbound

Diagnosing Corruption-Induced Reliability Failures in Vision-Language Models cites this paper.

Diagnosing Corruption-Induced Reliability Failures in Vision-Language Models Analysing the Robustness of Vision-Language-Models to Common Corruptions

Reference 37

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Observation e1071650-d258-4884-8db0-38d8086cdca8 · inbound

RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation cites this paper.

RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation Analysing the Robustness of Vision-Language-Models to Common Corruptions

Reference 50

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Observation 3ba9a9cf-42bc-4929-a585-7291c0d87be9 · inbound

DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning cites this paper.

DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning Analysing the Robustness of Vision-Language-Models to Common Corruptions

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:10:09.800498Z

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Observation 3b84f120-34da-4392-9819-25a891a79204 · inbound

Are Reasoning Vision-Language Models Robust to Semantic Visual Distractions? cites this paper.

Are Reasoning Vision-Language Models Robust to Semantic Visual Distractions? Analysing the Robustness of Vision-Language-Models to Common Corruptions

Reference 32

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Observation dd6525cc-7749-400e-82ec-f32ee749cc0e · inbound

How Robust is OCR-Reasoning? Evaluating OCR-Reasoning Robustness of Vision-Language Models under Visual Perturbations cites this paper.

How Robust is OCR-Reasoning? Evaluating OCR-Reasoning Robustness of Vision-Language Models under Visual Perturbations Analysing the Robustness of Vision-Language-Models to Common Corruptions

Reference 14

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Observation 0a276d57-6442-4410-84f5-be29e78194bc · inbound

BRUCE: Benchmarking Robustness Under Corruption Escalation for Scientific Vision-Language Reasoning cites this paper.

BRUCE: Benchmarking Robustness Under Corruption Escalation for Scientific Vision-Language Reasoning Analysing the Robustness of Vision-Language-Models to Common Corruptions

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:04.563578Z

Source-reported events for the cited work

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