Paper Citation Record · LEDGER

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift

As of 20 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.03926.

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

pith.paper-citation-record.v1
2506.03926 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:59:45.965529Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact0
verified fuzzy24
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d9357776-042c-4041-bbd7-45a05a94fa1c · outbound

This paper cites Mixture-based feature space learning for few-shot image classification.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Mixture-based feature space learning for few-shot image classification

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:49.355108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation caa58c42-b9f1-43f6-bc42-e9cd095af1fe · outbound

This paper cites Infinite mixture prototypes for few-shot learning.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Infinite mixture prototypes for few-shot learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:49.207831Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e2103099-598d-4156-b731-980d342ed15f · outbound

This paper cites Weight uncertainty in neural net- work.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Weight uncertainty in neural net- work

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:49.041110Z

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Observation 8a157a3e-fb6f-4e74-83a5-6e80be723309 · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 4

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Source-reported events for the cited work

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Observation 094b0449-3266-411a-a917-acdb3d410edf · outbound

This paper cites Bayesian prompt learning for image-language model generaliza- tion.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Bayesian prompt learning for image-language model generaliza- tion

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:48.857129Z

Source-reported events for the cited work

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Observation ddf8c34e-ed96-46cd-9109-3da9328a6126 · outbound

This paper cites Clip-adapter: Better vision-language models with fea- ture adapters.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Clip-adapter: Better vision-language models with fea- ture adapters

Reference 6

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Source-reported events for the cited work

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Observation c493784f-bdca-4297-9eaa-83598a538adf · outbound

This paper cites A broader study of cross-domain few- shot learning.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift A broader study of cross-domain few- shot learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:48.671631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1f01190f-61a3-4ccb-966b-c8ccc6e37e0d · outbound

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

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Eurosat: A novel dataset and deep learn- ing benchmark for land use and land cover classification

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:48.501037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db39cf65-7ed1-4282-8d79-c3fe9c7cce81 · outbound

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

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Scaling up visual and vision- language representation learning with noisy text super- vision

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:48.397216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e68017e7-6f3c-4289-adad-748c7cac70d4 · outbound

This paper cites S3c: Self-supervised stochastic classifiers for few-shot class-incremental learning.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift S3c: Self-supervised stochastic classifiers for few-shot class-incremental learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:48.321209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3e773e54-a4d3-4db5-830d-c6ae72df6297 · outbound

This paper cites Maple: Multi-modal prompt learning.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Maple: Multi-modal prompt learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:48.167073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d44b838-c81a-4132-89c8-d514480269b6 · outbound

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

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Self-regulating prompts: Founda- tional model adaptation without forgetting

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:48.020381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 953801aa-d141-4ed8-872a-f3d3c6ab5045 · outbound

This paper cites Auto-encoding variational bayes, 2013.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Auto-encoding variational bayes, 2013

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:59:45.062266Z

Source-reported events for the cited work

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Observation 419ba528-777b-4d0c-887e-4c814bc0196a · outbound

This paper cites Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:47.856672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e01889db-7096-4635-aa25-798a85913162 · outbound

This paper cites Prompt distribution learning.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Prompt distribution learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:59:45.185055Z

Source-reported events for the cited work

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Observation 762fd2ec-591c-4f9a-8ba8-48cf69b8a9b1 · outbound

This paper cites Stochastic classifiers for un- supervised domain adaptation.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Stochastic classifiers for un- supervised domain adaptation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:47.681388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25f114cd-1880-42b2-be7a-0ca0dc679c5b · outbound

This paper cites Using deep learning for image-based plant dis- ease detection.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Using deep learning for image-based plant dis- ease detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:47.517750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 461e7888-9164-4da5-b9e1-014ea9f64da8 · outbound

This paper cites Bayesian learning for neural networks, volume 118.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Bayesian learning for neural networks, volume 118

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:47.383077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f1d03cd1-864c-4acc-bb89-ea945ae513b8 · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Learning transferable visual models from natural lan- guage supervision

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:47.258587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a2396192-a1cb-4a2e-afd8-b4911b23d89b · outbound

This paper cites A closer look at the few-shot adaptation of large vision-language models.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift A closer look at the few-shot adaptation of large vision-language models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:47.097396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93d236fd-dcd5-44b1-9356-d6b25de9fe12 · outbound

This paper cites Dynamic mixed- prototype model for incremental deepfake detection.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Dynamic mixed- prototype model for incremental deepfake detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:46.964300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c67a93e5-bc07-4e43-84f7-f729136f8cb5 · outbound

This paper cites Chestx- ray8: Hospital-scale chest x-ray database and bench- marks on weakly-supervised classification and localiza- tion of common thorax diseases.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Chestx- ray8: Hospital-scale chest x-ray database and bench- marks on weakly-supervised classification and localiza- tion of common thorax diseases

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:46.869363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e1b468e1-d2c4-45ee-8aef-76f7961d235b · outbound

This paper cites Semantic- guided robustness tuning for few-shot transfer across ex- treme domain shift.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Semantic- guided robustness tuning for few-shot transfer across ex- treme domain shift

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:46.741619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 22f22b25-50f0-4784-a8fd-bc39a14c237d · outbound

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

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Tcp: Textual-based class-aware prompt tuning for visual- language model

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:46.631870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 521e5d7e-9e9c-44c2-86a6-6643184a4b92 · outbound

This paper cites Task residual for tuning vision-language mod- els.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Task residual for tuning vision-language mod- els

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:46.540655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4bc56fea-dcc8-469d-bdc9-ca12b00b170d · outbound

This paper cites Robust person re- identification by modelling feature uncertainty.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Robust person re- identification by modelling feature uncertainty

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:46.404519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e4bbce11-5dd7-4c0e-9cbd-9881bccc40e6 · outbound

This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:59:45.797373Z

Source-reported events for the cited work

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Observation e6f5881b-bbfb-447b-9362-6a8f4bf1f783 · outbound

This paper cites Semi-supervised domain generalization with stochastic stylematch.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Semi-supervised domain generalization with stochastic stylematch

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:46.256530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 59a17da4-33be-4b3e-9fd3-1ef65e113b26 · outbound

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

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Learning to prompt for vision-language models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:59:46.118954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b3bc7632-b03d-458d-8a77-5cc0995867c2 · outbound

This paper cites Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting.

Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:59:45.965529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.