Paper Citation Record · LEDGER

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models

As of 20 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2509.06992.

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

pith.paper-citation-record.v1
2509.06992 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:18:44.087201Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

18 of 18 outbound references displayed

verified exact3
verified fuzzy6
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2cc4a5ae-eec7-4f97-ba92-9f34b899a580 · outbound

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

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Imagenet: A large-scale hierarchical image database

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:42.602970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d376db70-108d-4740-b5c7-2d084d158b06 · outbound

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

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Learning generative visual models from few training examples: An incremen- tal bayesian approach tested on 101 object categories

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:42.719595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22e1d19a-6bfc-45dc-8df2-14f2c4ad474d · outbound

This paper cites Torchattacks: A PyTorch Repository for Adversarial Attacks.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:42.857844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d50d352-b005-4ecc-a194-5c3c3e712338 · outbound

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

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:43.032450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b34c94d2-7a41-4aff-b6d6-3e8ddcd3f559 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Fine-Grained Visual Classification of Aircraft

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:43.093364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7a24f9d-9586-45f8-9cb2-b3d833974d69 · outbound

This paper cites and Zisserman, A.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models and Zisserman, A

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:18:45.511334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1896b4b4-1ccc-4be7-8f27-784984667722 · outbound

This paper cites Learning to Prompt Your Domain for Vision-Language Models.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Learning to Prompt Your Domain for Vision-Language Models

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:18:44.533863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a4efc455-ecd6-49fa-a9ba-f40269931d40 · outbound

This paper cites Few-Shot Adversarial Prompt Learning on Vision-Language Models.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Few-Shot Adversarial Prompt Learning on Vision-Language Models

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:18:44.344042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d785e953-bfe6-46b8-a884-4250f1925217 · outbound

This paper cites M., Vedaldi, A., Zisserman, A., and Jawahar, C.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models M., Vedaldi, A., Zisserman, A., and Jawahar, C

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:18:45.244255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 44a81d2d-a37c-4530-a4a8-38398236c5ff · outbound

This paper cites Intriguing properties of neural networks.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Intriguing properties of neural networks

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:43.595800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1aaca91-7f09-4671-a0b2-004ee3fe81ef · outbound

This paper cites and Wagner, D.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models and Wagner, D

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:18:45.976843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d0c1700-4809-4fb7-8717-5c24d9f0c751 · outbound

This paper cites Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:42.286695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2594e414-0630-4514-945d-5b8e95d43574 · outbound

This paper cites Adversarial training in communication constrained federated learning.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Adversarial training in communication constrained federated learning

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:18:44.797116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b7584ddb-7349-4ea2-af0b-2f793661ce58 · outbound

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

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:43.502536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93bcc730-f04d-4e28-bfe3-0d3897957636 · outbound

This paper cites A., Oliva, A., and Torralba, A.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models A., Oliva, A., and Torralba, A

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:43.840981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1f77d2c-9b71-491c-9f13-1f3175001c58 · outbound

This paper cites Vi- sual prompting for adversarial robustness.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Vi- sual prompting for adversarial robustness

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:18:45.700264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1c36d2c3-4204-48b8-8630-5f58979cffbd · outbound

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

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Food-101– mining discriminative components with random forests

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:18:46.215906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e51f9981-fb73-44aa-9988-31769b4f0403 · outbound

This paper cites Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learning.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learning

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:18:45.003335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.