Paper Citation Record · LEDGER

GLAD: Generalizable Tuning for Vision-Language Models

As of 13 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 3 inbound Pith citation observations for arXiv:2507.13089.

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

pith.paper-citation-record.v1
2507.13089 v1

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

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measured 72 of 72 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:24:53.437554Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

69 of 69 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 8c631083-f522-4e07-a307-b4355579d76a · outbound

This paper cites Food-101–mining discriminative components with random forests.

GLAD: Generalizable Tuning for Vision-Language Models Food-101–mining discriminative components with random forests

Reference 1

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Observation 18c46557-4c87-4e5d-9b2b-23ae3baf775b · outbound

This paper cites Domain prompt learning with quaternion networks.

GLAD: Generalizable Tuning for Vision-Language Models Domain prompt learning with quaternion networks

Reference 2

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Observation f5650e68-a17e-43f8-802b-123d6870fa79 · outbound

This paper cites Tokenmixup: Efficient attention-guided token-level data augmentation for transformers.

GLAD: Generalizable Tuning for Vision-Language Models Tokenmixup: Efficient attention-guided token-level data augmentation for transformers

Reference 3

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Observation 7ef1785a-36f0-40fd-9ea4-f5bb33dd768d · outbound

This paper cites Describing textures in the wild.

GLAD: Generalizable Tuning for Vision-Language Models Describing textures in the wild

Reference 4

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Observation 0ad76c32-0c90-486f-b8e4-3ea618b7fccf · outbound

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

GLAD: Generalizable Tuning for Vision-Language Models Imagenet: A large-scale hierarchical image database

Reference 5

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Observation 92e68a92-edaa-4485-8bfe-43b4805b65ee · outbound

This paper cites Learning to prompt for open-vocabulary ob- ject detection with vision-language model.

GLAD: Generalizable Tuning for Vision-Language Models Learning to prompt for open-vocabulary ob- ject detection with vision-language model

Reference 6

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Observation 219ff661-2bad-498b-8dc5-b478deef58ff · outbound

This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

GLAD: Generalizable Tuning for Vision-Language Models Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 7

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Observation 1cdbdb46-701f-4237-be09-1ffb4e5a7ad3 · outbound

This paper cites Prompt- det: Towards open-vocabulary detection using uncurated im- ages.

GLAD: Generalizable Tuning for Vision-Language Models Prompt- det: Towards open-vocabulary detection using uncurated im- ages

Reference 8

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Observation 3123dc9a-92b4-4b59-a7cf-d4ce526c1dec · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

GLAD: Generalizable Tuning for Vision-Language Models Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 9

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Observation 062cd90d-afa2-40bf-b4ed-a5ecf1e1fef6 · outbound

This paper cites CLIP-Adapter: Better Vision-Language Models with Feature Adapters.

GLAD: Generalizable Tuning for Vision-Language Models CLIP-Adapter: Better Vision-Language Models with Feature Adapters

Reference 10

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Observation c8cdf682-f7dd-47c4-8bd7-fa1559d24ea6 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

GLAD: Generalizable Tuning for Vision-Language Models Explaining and Harnessing Adversarial Examples

Reference 11

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Observation c557da71-b43d-4aa4-ad2c-953260e28b21 · outbound

This paper cites Open-vocabulary object detection via vision and language knowledge distillation.

GLAD: Generalizable Tuning for Vision-Language Models Open-vocabulary object detection via vision and language knowledge distillation

Reference 12

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Observation 6d2940be-bf72-4e9f-98a7-d6b6929d039f · outbound

This paper cites Eurosat: A novel dataset and deep learn- ing benchmark for land use and land cover classification.

GLAD: Generalizable Tuning for Vision-Language Models Eurosat: A novel dataset and deep learn- ing benchmark for land use and land cover classification

Reference 13

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Observation f44a092f-0253-48bb-a48b-226d4d514e5d · outbound

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

GLAD: Generalizable Tuning for Vision-Language Models The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 14

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Observation 89a001d4-2771-45ac-b562-633982e981f0 · outbound

This paper cites Natural adversarial examples.

GLAD: Generalizable Tuning for Vision-Language Models Natural adversarial examples

Reference 15

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Observation 6b185be9-37b5-46cb-a022-c7019faad37d · outbound

This paper cites Lora: Low-rank adaptation of large language models.

GLAD: Generalizable Tuning for Vision-Language Models Lora: Low-rank adaptation of large language models

Reference 16

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Observation 00d50d34-2784-4f23-aa36-adebb894f1a2 · outbound

This paper cites Learning a Better Initialization for Soft Prompts via Meta-Learning.

GLAD: Generalizable Tuning for Vision-Language Models Learning a Better Initialization for Soft Prompts via Meta-Learning

Reference 17

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Observation 7f0920ef-a73e-4379-8f36-d74481668e8a · outbound

This paper cites Patching open-vocabulary models by interpolating weights.

GLAD: Generalizable Tuning for Vision-Language Models Patching open-vocabulary models by interpolating weights

Reference 18

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Observation 5ff13c00-186d-45e9-97e9-fff6c9f2e3fe · outbound

This paper cites Vi- sual prompt tuning.

GLAD: Generalizable Tuning for Vision-Language Models Vi- sual prompt tuning

Reference 19

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Observation adf384f3-9ef9-412d-9a1c-73dffa930e01 · outbound

This paper cites Maple: Multi-modal prompt learning.

GLAD: Generalizable Tuning for Vision-Language Models Maple: Multi-modal prompt learning

Reference 20

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Observation d58f73ca-6225-4079-8f74-8bc9337af873 · outbound

This paper cites Self-regulating prompts: Foundational model adaptation without forgetting.

GLAD: Generalizable Tuning for Vision-Language Models Self-regulating prompts: Foundational model adaptation without forgetting

Reference 21

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Observation ec6763f4-2486-43f2-8d8a-cdec6d4795fd · outbound

This paper cites Co-mixup: Saliency guided joint mixup with super- modular diversity.

GLAD: Generalizable Tuning for Vision-Language Models Co-mixup: Saliency guided joint mixup with super- modular diversity

Reference 22

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Observation 5b63c5e1-dc1a-4444-a1e4-dd0c43ae81e3 · outbound

This paper cites 3d object representations for fine-grained categorization.

GLAD: Generalizable Tuning for Vision-Language Models 3d object representations for fine-grained categorization

Reference 23

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This paper cites Read-only prompt op- timization for vision-language few-shot learning.

GLAD: Generalizable Tuning for Vision-Language Models Read-only prompt op- timization for vision-language few-shot learning

Reference 24

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Observation 8db438d4-ce38-4fc4-a3f7-f481a3744da0 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

GLAD: Generalizable Tuning for Vision-Language Models The power of scale for parameter-efficient prompt tuning

Reference 25

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

GLAD: Generalizable Tuning for Vision-Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 26

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This paper cites Promptkd: Unsupervised prompt distillation for vision-language models.

GLAD: Generalizable Tuning for Vision-Language Models Promptkd: Unsupervised prompt distillation for vision-language models

Reference 27

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This paper cites Visual instruction tuning.

GLAD: Generalizable Tuning for Vision-Language Models Visual instruction tuning

Reference 28

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This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in nat- ural language processing.

GLAD: Generalizable Tuning for Vision-Language Models Pre-train, prompt, and predict: A systematic survey of prompting methods in nat- ural language processing

Reference 29

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This paper cites Decoupled weight decay regularization.

GLAD: Generalizable Tuning for Vision-Language Models Decoupled weight decay regularization

Reference 30

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Observation ba63cdc1-477a-44e0-b77a-aef4a4a45e5f · outbound

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GLAD: Generalizable Tuning for Vision-Language Models Prompt distribution learning

Reference 31

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This paper cites Image segmentation using text and image prompts.

GLAD: Generalizable Tuning for Vision-Language Models Image segmentation using text and image prompts

Reference 32

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This paper cites Fine-Grained Visual Classification of Aircraft.

GLAD: Generalizable Tuning for Vision-Language Models Fine-Grained Visual Classification of Aircraft

Reference 33

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This paper cites ClipCap: CLIP Prefix for Image Captioning.

GLAD: Generalizable Tuning for Vision-Language Models ClipCap: CLIP Prefix for Image Captioning

Reference 34

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This paper cites Lookbehind-SAM: k steps back, 1 step forward.

GLAD: Generalizable Tuning for Vision-Language Models Lookbehind-SAM: k steps back, 1 step forward

Reference 35

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Observation cea1fbff-c8c4-4478-bdc4-839e46f9c63a · outbound

This paper cites When does label smoothing help? Advances in neural in- formation processing systems, 32, 2019.

GLAD: Generalizable Tuning for Vision-Language Models When does label smoothing help? Advances in neural in- formation processing systems, 32, 2019

Reference 36

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

GLAD: Generalizable Tuning for Vision-Language Models Automated flower classification over a large number of classes

Reference 37

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This paper cites Metropolis-hastings data augmentation for graph neu- ral networks.

GLAD: Generalizable Tuning for Vision-Language Models Metropolis-hastings data augmentation for graph neu- ral networks

Reference 38

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GLAD: Generalizable Tuning for Vision-Language Models Cats and dogs

Reference 39

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This paper cites Learn- ing transferable visual models from natural language super- vision.

GLAD: Generalizable Tuning for Vision-Language Models Learn- ing transferable visual models from natural language super- vision

Reference 40

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This paper cites Do imagenet classifiers generalize to im- agenet? In ICML, pages 5389–5400.

GLAD: Generalizable Tuning for Vision-Language Models Do imagenet classifiers generalize to im- agenet? In ICML, pages 5389–5400

Reference 41

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This paper cites Multimodal Instruction Tuning with Conditional Mixture of LoRA.

GLAD: Generalizable Tuning for Vision-Language Models Multimodal Instruction Tuning with Conditional Mixture of LoRA

Reference 42

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This paper cites Flava: A foundational language and vision alignment model.

GLAD: Generalizable Tuning for Vision-Language Models Flava: A foundational language and vision alignment model

Reference 43

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This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

GLAD: Generalizable Tuning for Vision-Language Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 44

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This paper cites Dropout: a simple way to prevent neural networks from overfitting.

GLAD: Generalizable Tuning for Vision-Language Models Dropout: a simple way to prevent neural networks from overfitting

Reference 45

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Observation 64b15ba4-b80f-4605-8475-87b9d93f4409 · outbound

This paper cites Rethinking the inception ar- chitecture for computer vision.

GLAD: Generalizable Tuning for Vision-Language Models Rethinking the inception ar- chitecture for computer vision

Reference 46

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This paper cites Saliencymix: A saliency guided data augmentation strategy for better regularization.

GLAD: Generalizable Tuning for Vision-Language Models Saliencymix: A saliency guided data augmentation strategy for better regularization

Reference 47

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This paper cites Manifold mixup: learning better representations by in- terpolating hidden states.

GLAD: Generalizable Tuning for Vision-Language Models Manifold mixup: learning better representations by in- terpolating hidden states

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:28.113352Z

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This paper cites Tuning multi-mode token- level prompt alignment across modalities.

GLAD: Generalizable Tuning for Vision-Language Models Tuning multi-mode token- level prompt alignment across modalities

Reference 49

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This paper cites Learning robust global representations by penalizing local predictive power.

GLAD: Generalizable Tuning for Vision-Language Models Learning robust global representations by penalizing local predictive power

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:28.066014Z

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This paper cites Sharpness-aware gradient matching for domain generaliza- tion.

GLAD: Generalizable Tuning for Vision-Language Models Sharpness-aware gradient matching for domain generaliza- tion

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:27.896307Z

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This paper cites Cogvlm: Visual expert for pretrained language models.

GLAD: Generalizable Tuning for Vision-Language Models Cogvlm: Visual expert for pretrained language models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:27.723422Z

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Observation d3ea47da-5c28-4003-ba0e-dcabcfd057ed · outbound

This paper cites Robust fine-tuning of zero-shot models.

GLAD: Generalizable Tuning for Vision-Language Models Robust fine-tuning of zero-shot models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:27.583409Z

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Observation 26048ba1-384a-4013-ab3e-dc289bd39204 · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo.

GLAD: Generalizable Tuning for Vision-Language Models Sun database: Large-scale scene recognition from abbey to zoo

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:27.403579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 35131259-a89b-4284-bdad-790893de0a37 · outbound

This paper cites Tcp: Textual- based class-aware prompt tuning for visual-language model.

GLAD: Generalizable Tuning for Vision-Language Models Tcp: Textual- based class-aware prompt tuning for visual-language model

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:27.246670Z

Source-reported events for the cited work

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Observation 3806585f-bbcc-4e70-be8d-2fde26295261 · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

GLAD: Generalizable Tuning for Vision-Language Models Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:27.078177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ebfd872d-fb0f-49e5-88fb-d918115b5910 · outbound

This paper cites Low-rank few-shot adaptation of vision-language models.

GLAD: Generalizable Tuning for Vision-Language Models Low-rank few-shot adaptation of vision-language models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:26.915990Z

Source-reported events for the cited work

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Observation 626eb206-3728-4927-90aa-835e1c098562 · outbound

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

GLAD: Generalizable Tuning for Vision-Language Models Lit: Zero-shot transfer with locked-image text tuning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:26.763594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ed262ff5-852e-41f0-9a30-dbb356a79355 · outbound

This paper cites Three mechanisms of weight decay regularization.

GLAD: Generalizable Tuning for Vision-Language Models Three mechanisms of weight decay regularization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:26.620181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d28e2228-19a9-496c-8ffc-62e3c36aeeb4 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

GLAD: Generalizable Tuning for Vision-Language Models mixup: Beyond Empirical Risk Minimization

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:23.459308Z

Source-reported events for the cited work

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Observation 4834b420-d026-4e38-86e4-76b17aadee68 · outbound

This paper cites Dept: Decoupled prompt tuning.

GLAD: Generalizable Tuning for Vision-Language Models Dept: Decoupled prompt tuning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:26.426058Z

Source-reported events for the cited work

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Observation a21fe1d1-d66b-4447-9f44-667a3af33382 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

GLAD: Generalizable Tuning for Vision-Language Models Adding conditional control to text-to-image diffusion models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:23.593178Z

Source-reported events for the cited work

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Observation b7512f2a-6b89-4be1-b313-2e6847175b69 · outbound

This paper cites Llama-adapter: Efficient fine-tuning of language models with zero-init attention.

GLAD: Generalizable Tuning for Vision-Language Models Llama-adapter: Efficient fine-tuning of language models with zero-init attention

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:26.126021Z

Source-reported events for the cited work

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Observation c04e7633-c62d-43a3-ba52-725bb69e0a6d · outbound

This paper cites SVIT: Scaling up Visual Instruction Tuning.

GLAD: Generalizable Tuning for Vision-Language Models SVIT: Scaling up Visual Instruction Tuning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:23.687949Z

Source-reported events for the cited work

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Observation 2dadc935-ceed-472a-a3cd-822b7c5036d8 · outbound

This paper cites Regionclip: Region-based language-image pretraining.

GLAD: Generalizable Tuning for Vision-Language Models Regionclip: Region-based language-image pretraining

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:25.879634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7d993c5f-112d-4b83-b15e-97df888bc360 · outbound

This paper cites Conditional prompt learning for vision-language models.

GLAD: Generalizable Tuning for Vision-Language Models Conditional prompt learning for vision-language models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:25.618806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2cbf78dd-d784-4289-a2ae-135dcd884f41 · outbound

This paper cites Learning to prompt for vision-language models.

GLAD: Generalizable Tuning for Vision-Language Models Learning to prompt for vision-language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:25.237348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e2a1921c-ccf8-4a8d-a30d-298f18332a4e · outbound

This paper cites Prompt-aligned gradient for prompt tuning.

GLAD: Generalizable Tuning for Vision-Language Models Prompt-aligned gradient for prompt tuning

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:23.993559Z

Source-reported events for the cited work

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Observation e9c866b0-7b5d-4199-be01-49ece54bf2c4 · outbound

This paper cites Surrogate Gap Minimization Improves Sharpness-Aware Training.

GLAD: Generalizable Tuning for Vision-Language Models Surrogate Gap Minimization Improves Sharpness-Aware Training

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:24.063644Z

Source-reported events for the cited work

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Pith citing papers

Observation bd78f140-02f5-4e16-b878-6d00bf653a39 · inbound

TARA: Token-Aware LoRA for Composable Personalization in Diffusion Models cites this paper.

TARA: Token-Aware LoRA for Composable Personalization in Diffusion Models GLAD: Generalizable Tuning for Vision-Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T21:24:53.437554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30255c27-ef3b-4a30-b360-43510c56f66e · inbound

GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models cites this paper.

GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models GLAD: Generalizable Tuning for Vision-Language Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:40:26.018918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 240109b8-82e3-4783-b64a-5230a8efe68a · inbound

GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models cites this paper.

GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models GLAD: Generalizable Tuning for Vision-Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-12T20:25:41.773345Z

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