Paper Citation Record · LEDGER

RUB: Evaluating Residual Knowledge in Unlearned Models

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2504.14798.

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

pith.paper-citation-record.v1
2504.14798 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:45:33.057465Z

measured 45 of 45 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T09:35:00.520860Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T09:37:14.104421Z

Reference resolution

44 of 44 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85f85cf5-1247-4ff6-99b5-87f3585e8b19 · outbound

This paper cites write newline.

RUB: Evaluating Residual Knowledge in Unlearned Models write newline

Reference 1

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Observation c9ca0411-bde9-43dd-8a01-6093b7a5af3a · outbound

This paper cites A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N.

RUB: Evaluating Residual Knowledge in Unlearned Models A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N

Reference 2

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Observation a46cd76f-4716-4465-befb-d726f9c7264d · outbound

This paper cites and Yang, J.

RUB: Evaluating Residual Knowledge in Unlearned Models and Yang, J

Reference 3

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Observation 96480987-9e6f-4100-8fa2-e785b5d143b5 · outbound

This paper cites Membership inference attacks from first principles.

RUB: Evaluating Residual Knowledge in Unlearned Models Membership inference attacks from first principles

Reference 4

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Observation 211d2a5a-43ed-4a4a-ba6e-f01da8e38e5d · outbound

This paper cites Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation.

RUB: Evaluating Residual Knowledge in Unlearned Models Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation

Reference 5

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Observation d678d1c5-abdd-4fc4-8b3f-dc2c3f42d07a · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures.

RUB: Evaluating Residual Knowledge in Unlearned Models Model inversion attacks that exploit confidence information and basic countermeasures

Reference 6

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Observation 847ad7e0-8c3f-4189-8918-efca03214b7c · outbound

This paper cites Verifi: Towards verifiable federated unlearning.

RUB: Evaluating Residual Knowledge in Unlearned Models Verifi: Towards verifiable federated unlearning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:33.495446Z

Source-reported events for the cited work

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Observation 23486c0b-b713-4580-a1f2-e0127d23e554 · outbound

This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks.

RUB: Evaluating Residual Knowledge in Unlearned Models Eternal sunshine of the spotless net: Selective forgetting in deep networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:33.485234Z

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Observation fab86652-1346-4811-8d26-858df91e3a24 · outbound

This paper cites Amnesiac machine learning.

RUB: Evaluating Residual Knowledge in Unlearned Models Amnesiac machine learning

Reference 9

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Observation 3f2f5bb6-32c2-4ead-8cac-2cfc239857bd · outbound

This paper cites Verifying in the dark: Verifiable machine unlearning by using invisible backdoor triggers.

RUB: Evaluating Residual Knowledge in Unlearned Models Verifying in the dark: Verifiable machine unlearning by using invisible backdoor triggers

Reference 10

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Observation 2f78061f-6d6d-4cb7-8f56-7a323d13fdf0 · outbound

This paper cites Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective.

RUB: Evaluating Residual Knowledge in Unlearned Models Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective

Reference 11

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Observation 3bb26f4d-690b-40e2-8076-3d63297dca42 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

RUB: Evaluating Residual Knowledge in Unlearned Models Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 12

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Observation 542865a9-7351-4b87-94ec-795bba6755a4 · outbound

This paper cites A., Chaudhuri, K., and Zou, J.

RUB: Evaluating Residual Knowledge in Unlearned Models A., Chaudhuri, K., and Zou, J

Reference 13

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Observation c4beb0e4-de2f-40aa-b568-65a74141cafd · outbound

This paper cites A., Dullerud, N., Thudi, A., Chandrasekaran, V., and Papernot, N.

RUB: Evaluating Residual Knowledge in Unlearned Models A., Dullerud, N., Thudi, A., Chandrasekaran, V., and Papernot, N

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:33.456514Z

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Observation 5900a856-57a8-49fd-83a1-7e3619bc3067 · outbound

This paper cites Model sparsity can simplify machine unlearning.

RUB: Evaluating Residual Knowledge in Unlearned Models Model sparsity can simplify machine unlearning

Reference 15

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RUB: Evaluating Residual Knowledge in Unlearned Models Unresolved cited work

Reference 16

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Source-reported events for the cited work

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Observation c9406344-d4c1-42eb-af24-1ef47138bf23 · outbound

This paper cites Towards unbounded machine unlearning.

RUB: Evaluating Residual Knowledge in Unlearned Models Towards unbounded machine unlearning

Reference 17

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Observation 9e8419d2-b8b5-488f-8fcd-492a92ba4237 · outbound

This paper cites Machine unlearning for image-to-image generative models.

RUB: Evaluating Residual Knowledge in Unlearned Models Machine unlearning for image-to-image generative models

Reference 18

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Observation 1c9a0478-ac4b-4f42-b987-08a018e4dd79 · outbound

This paper cites The right to be forgotten in federated learning: An efficient realization with rapid retraining.

RUB: Evaluating Residual Knowledge in Unlearned Models The right to be forgotten in federated learning: An efficient realization with rapid retraining

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:33.418056Z

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Observation 5fa601d0-89df-4ba8-add7-3cc29f394417 · outbound

This paper cites Backdoor attacks via machine unlearning.

RUB: Evaluating Residual Knowledge in Unlearned Models Backdoor attacks via machine unlearning

Reference 20

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Observation c74fc1f8-432b-4d18-ac63-b10a2c1a592b · outbound

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

RUB: Evaluating Residual Knowledge in Unlearned Models Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 21

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RUB: Evaluating Residual Knowledge in Unlearned Models Unresolved cited work

Reference 22

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RUB: Evaluating Residual Knowledge in Unlearned Models Unresolved cited work

Reference 23

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Observation 6cd953dc-e06d-411c-b9af-cf68d360bee6 · outbound

This paper cites O., Cohen, N., Mittal, G., and Hegde, C.

RUB: Evaluating Residual Knowledge in Unlearned Models O., Cohen, N., Mittal, G., and Hegde, C

Reference 24

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Observation 192c72bd-e0c0-4334-a60d-217125606be2 · outbound

This paper cites Towards understanding and enhancing robustness of deep learning models against malicious unlearning attacks.

RUB: Evaluating Residual Knowledge in Unlearned Models Towards understanding and enhancing robustness of deep learning models against malicious unlearning attacks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:33.370943Z

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Observation 887e48e7-adbb-4573-943b-4fb93ee4d936 · outbound

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RUB: Evaluating Residual Knowledge in Unlearned Models Unresolved cited work

Reference 26

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Observation 124493a6-9ae7-43e8-8718-d2298c8e85e6 · outbound

This paper cites Improved techniques for training gans.

RUB: Evaluating Residual Knowledge in Unlearned Models Improved techniques for training gans

Reference 27

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Observation 9b5da929-cd10-45d4-ac7b-e46b0fb501f7 · outbound

This paper cites Membership inference attacks against machine learning models.

RUB: Evaluating Residual Knowledge in Unlearned Models Membership inference attacks against machine learning models

Reference 28

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RUB: Evaluating Residual Knowledge in Unlearned Models A., and Anderson, R

Reference 29

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Observation e390539e-191e-46a2-a3e4-5b794d72689d · outbound

This paper cites UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI.

RUB: Evaluating Residual Knowledge in Unlearned Models UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI

Reference 30

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Observation f789b445-eea4-4c11-9302-fd7b4671602b · outbound

This paper cites Towards Probabilistic Verification of Machine Unlearning.

RUB: Evaluating Residual Knowledge in Unlearned Models Towards Probabilistic Verification of Machine Unlearning

Reference 31

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Observation 483ecbd8-07f5-44a5-a685-050fb006ebe8 · outbound

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RUB: Evaluating Residual Knowledge in Unlearned Models K., Chundawat, V

Reference 32

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Observation 5ac44f80-4301-4647-a3eb-a7ed83b5367a · outbound

This paper cites On the necessity of auditable algorithmic definitions for machine unlearning.

RUB: Evaluating Residual Knowledge in Unlearned Models On the necessity of auditable algorithmic definitions for machine unlearning

Reference 33

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This paper cites Ring-a-bell! how reliable are concept removal methods for diffusion models? In International Conference on Learning Representations, 2024.

RUB: Evaluating Residual Knowledge in Unlearned Models Ring-a-bell! how reliable are concept removal methods for diffusion models? In International Conference on Learning Representations, 2024

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:33.313553Z

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Observation 0c3f2853-da77-408a-8365-500b059eb11d · outbound

This paper cites Machine Unlearning of Features and Labels.

RUB: Evaluating Residual Knowledge in Unlearned Models Machine Unlearning of Features and Labels

Reference 35

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Observation 59c2d1f6-4141-43a8-9dfc-b98a49348a78 · outbound

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RUB: Evaluating Residual Knowledge in Unlearned Models Large Language Model Unlearning

Reference 36

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Observation 35ceaee6-4854-4961-9760-35da580299ae · outbound

This paper cites Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models.

RUB: Evaluating Residual Knowledge in Unlearned Models Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models

Reference 37

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Observation 69fda85d-e26a-401c-a425-5f0f5b565c22 · outbound

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RUB: Evaluating Residual Knowledge in Unlearned Models Verification of machine unlearning is fragile

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:33.302636Z

Source-reported events for the cited work

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Observation 4188d738-c782-4613-a039-ee26f759785c · outbound

This paper cites To generate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images.

RUB: Evaluating Residual Knowledge in Unlearned Models To generate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images

Reference 39

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Observation 08ce975e-3474-40d1-80fa-fbdcda8271b4 · outbound

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RUB: Evaluating Residual Knowledge in Unlearned Models write newline

Reference 40

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Observation 66515fb1-1af5-467f-a456-9bfeb390f87f · outbound

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RUB: Evaluating Residual Knowledge in Unlearned Models write newline

Reference 41

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Observation 6ee6b9ac-f2fa-4bf6-93ac-f694b16bbc44 · outbound

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RUB: Evaluating Residual Knowledge in Unlearned Models @esa (Ref

Reference 42

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Observation 966b26c3-3ecb-4e91-815f-79d0bf627b4c · outbound

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RUB: Evaluating Residual Knowledge in Unlearned Models Unresolved cited work

Reference 43

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Observation 7f0fd93d-3c26-4dda-849f-2aa03756c791 · outbound

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RUB: Evaluating Residual Knowledge in Unlearned Models Unresolved cited work

Reference 44

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Pith citing papers

Observation d2b1b202-adb7-4075-8c20-85670f6f21f2 · inbound

Towards Reliable Forgetting: A Survey on Machine Unlearning Verification cites this paper.

Towards Reliable Forgetting: A Survey on Machine Unlearning Verification RUB: Evaluating Residual Knowledge in Unlearned Models

Reference 117

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