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Paper Citation Record · LEDGER

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

As of 17 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2506.14003.

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

pith.paper-citation-record.v1
2506.14003 v5

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:29:39.623798Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T06:49:12.070481Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T14:58:33.258552Z

Reference resolution

40 of 40 outbound references displayed

  • verified exact1
  • verified fuzzy9
  • unresolved29
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2e166c7c-cd2f-47c9-8fbf-6251e54a6ddc · outbound

This paper cites Open Problems in Machine Unlearning for AI Safety.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Open Problems in Machine Unlearning for AI Safety

Reference 1

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source=pdf_text observed=2026-08-07T00:29:34.908539Z digest=sha256:18255243bbe2fdde38d20a6220aca1a476202a1f822e808c6321ab20a60c7410

Observation 2c3329a7-35e7-4577-891e-2606deba6fef · outbound

This paper cites forget” prompt drawn from the WMDP evaluation set, which tests the model’s ability to omit specific target knowledge, and (2) a “forget- irrelevant.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs forget” prompt drawn from the WMDP evaluation set, which tests the model’s ability to omit specific target knowledge, and (2) a “forget- irrelevant

Reference 3

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Observation dd3fb1f0-9a06-4a83-8f1b-313a378b6989 · outbound

This paper cites role": "user.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs role": "user

Reference 4

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1989a63b-e8e5-433e-907b-79b61e2d7b8a · outbound

This paper cites forget” prompts, and above 98% on UltraChat for all models. Train- ing on forget-only data (Sf) likewise yields over 97% detection on “forget irrelevant.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs forget” prompts, and above 98% on UltraChat for all models. Train- ing on forget-only data (Sf) likewise yields over 97% detection on “forget irrelevant

Reference 6

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 793011d6-1741-4770-99b1-d7f53cd85e53 · outbound

This paper cites Measuring massive multitask language understanding.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Measuring massive multitask language understanding

Reference 8

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:29:35.721991Z digest=sha256:0ffee98ad3d1f7f0797d20281e41a2e16f50a86ea61dce30b33a728ad1e22b7b

Observation dc44e36b-f286-4506-bc03-3c85c88e5693 · outbound

This paper cites Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Reference 10

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source=pdf_text observed=2026-08-07T00:29:35.986007Z digest=sha256:8a3fe91ce4d80c182271c4b374e2c2f68c2bac2c824e94afbdfb46086fe2ea35

Observation 89066222-4259-45bf-a780-2bd53ea509f4 · outbound

This paper cites Editing Models with Task Arithmetic.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Editing Models with Task Arithmetic

Reference 11

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source=pdf_text observed=2026-08-07T00:29:36.151843Z digest=sha256:c9958b344e745efac18ba0396c3f5e98a71f6e88bd1b25c7ac8c1a7b72ade634

Observation 184ca9d9-ee8e-451e-9709-a550cc453ae2 · outbound

This paper cites SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning

Reference 13

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source=pdf_text observed=2026-08-07T00:29:36.377364Z digest=sha256:51bba659e4fd65e7e3579a3caf57cfef2207785be83700cc8c73ccb05468a55d

Observation 227d9c44-a5c2-40ad-9239-335234cbb5fc · outbound

This paper cites An Adversarial Perspective on Machine Unlearning for AI Safety.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs An Adversarial Perspective on Machine Unlearning for AI Safety

Reference 14

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source=pdf_text observed=2026-08-07T00:29:36.509811Z digest=sha256:5bfb1be2267ebb45b9f4529d8d9a1a4553b203e03e56d84c6f8437821c34c462

Observation d5307c7e-ff97-4704-8c77-78e97c1551e8 · outbound

This paper cites Eight Methods to Evaluate Robust Unlearning in LLMs.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Eight Methods to Evaluate Robust Unlearning in LLMs

Reference 15

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source=pdf_text observed=2026-08-07T00:29:36.619568Z digest=sha256:3f0a854bdd0e33a2d9da6f1b26bed25d18cd7799157d03b801eebd18907f6c7d

Observation 460f7658-d403-4463-8ad1-9c56b48f15c3 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 16

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source=pdf_text observed=2026-08-07T00:29:36.744747Z digest=sha256:5054d6e0b4a594457efbe3469b3e3778249ef48e3b0eab26ad411ac60b3dc228

Observation ee6b2ef6-51bd-4f47-b7f5-282c25a242f8 · outbound

This paper cites CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization

Reference 17

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source=pdf_text observed=2026-08-07T00:29:36.902929Z digest=sha256:0504168b868309fed17af8ce5c4043352f31f20dbf4010244744102111187094

Observation ecf25c02-d2ed-4477-9458-caec2a189a76 · outbound

This paper cites A Survey of Machine Unlearning.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs A Survey of Machine Unlearning

Reference 18

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source=pdf_text observed=2026-08-07T00:29:37.032784Z digest=sha256:076a230fff8bcd1bd036592f151e6ec74ea215d3750c537bbcabc966ee6293ed

Observation b61e6a1c-0077-476d-bce7-59d4c0fce241 · outbound

This paper cites Model provenance testing for large language models.arXiv preprint arXiv:2502.00706,.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Model provenance testing for large language models.arXiv preprint arXiv:2502.00706,

Reference 19

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source=pdf_text observed=2026-08-07T00:29:37.134690Z digest=sha256:0dbdb6ae6ad10b58972edded06db8afd6b2aee989909f5b0029a48335fdc86c5

Observation 76f97e4d-ae80-4bf4-8bf3-10acbdb965c3 · outbound

This paper cites In-Context Unlearning: Language Models as Few Shot Unlearners.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs In-Context Unlearning: Language Models as Few Shot Unlearners

Reference 20

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source=pdf_text observed=2026-08-07T00:29:37.230777Z digest=sha256:6f954d91940e22c24c90cb0508684a740632fea3a58bd63b658a0898867871d6

Observation 62674cbd-d081-4dbe-a09c-6a3eb37f1cc3 · outbound

This paper cites The Frontier of Data Erasure: Machine Unlearning for Large Language Models.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs The Frontier of Data Erasure: Machine Unlearning for Large Language Models

Reference 21

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source=pdf_text observed=2026-08-07T00:29:37.309383Z digest=sha256:447b647358792901fe3976e40f65da815da8ba30e14a44ab506a391350d8ebdc

Observation bfda0d67-4b66-49d4-b861-afc01e3400c4 · outbound

This paper cites MUSE: Machine Unlearning Six-Way Evaluation for Language Models.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs MUSE: Machine Unlearning Six-Way Evaluation for Language Models

Reference 24

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source=pdf_text observed=2026-08-07T00:29:37.694372Z digest=sha256:e11567ccfeeac3e4bff10f0c2d4c0dafc105833b50b6a76ea3458fd3edc92ce2

Observation 3226a965-a5fc-43dc-855c-d6711c3185b1 · outbound

This paper cites Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges

Reference 25

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source=pdf_text observed=2026-08-07T00:29:37.808176Z digest=sha256:147af3e770c052fc84d68c2df78d2abf1f82f6d31ea0a879105705b3981e317f

Observation 1d8eb44b-b832-49ee-bc2e-2abce6cb8c85 · outbound

This paper cites Idiosyncrasies in Large Language Models.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Idiosyncrasies in Large Language Models

Reference 26

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source=pdf_text observed=2026-08-07T00:29:37.961233Z digest=sha256:ae97295bfe08618866cdc15e4288e51d0ee3e886af968bb005a73b2b3672a0bf

Observation 4a980508-a89a-4402-8d49-0425f275fd78 · outbound

This paper cites Guardrail Baselines for Unlearning in LLMs.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Guardrail Baselines for Unlearning in LLMs

Reference 27

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source=pdf_text observed=2026-08-07T00:29:38.082423Z digest=sha256:b4cbe56ad49c01fb560eef0e6de13cde38eaed6db54560eecd82eda4542eb290

Observation ca03ba4b-f4a9-4beb-878b-9f21d076d794 · outbound

This paper cites Unrolling sgd: Under- standing factors influencing machine unlearning.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Unrolling sgd: Under- standing factors influencing machine unlearning

Reference 28

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:29:38.219481Z digest=sha256:310977bbb95370a4133f5a43f894d76de9d05e87b3237a38d7f7170d2779da9c

Observation b8f92201-9df0-41e5-b5c7-682ff5e2ce5a · outbound

This paper cites DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models

Reference 29

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source=pdf_text observed=2026-08-07T00:29:38.330222Z digest=sha256:e512ce9fbf5476a7f0c46d77b8a23cd7c8fb5617fdff29406d138e2e9f319f57

Observation d69a16c0-9918-470e-9886-5a606ecd8bb0 · outbound

This paper cites A fingerprint for large language models.arXiv preprint arXiv:2407.01235,.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs A fingerprint for large language models.arXiv preprint arXiv:2407.01235,

Reference 30

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source=pdf_text observed=2026-08-07T00:29:38.434169Z digest=sha256:bf7982f1467a824f92c40450f94dd3ebb7c2b4baa50a6d76644dd288e2a3e40c

Observation 2c16152a-b312-42e4-8753-b8f9fd2674e6 · outbound

This paper cites Large Language Model Unlearning.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Large Language Model Unlearning

Reference 31

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source=pdf_text observed=2026-08-07T00:29:38.583685Z digest=sha256:9ba3f1417296666810fc316564fc37358efb59686578b98c0417798222fe04ba

Observation f92521d0-e0ff-4d44-a7d4-d11acdee325a · outbound

This paper cites Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning

Reference 32

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source=pdf_text observed=2026-08-07T00:29:38.744476Z digest=sha256:7c93dc90c2c40bcdce125e1d1eaffd93af0c30961b76fefed22cc91055e6e102

Observation 16809980-21cd-4dd3-9019-f24c9e0465d7 · outbound

This paper cites UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models

Reference 33

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source=pdf_text observed=2026-08-07T00:29:38.874013Z digest=sha256:e64fc8a021fdbfa86b305c5752e8df0393cf98c7af5cd839cab13ba0bd196479

Observation 29553f39-d40b-40ab-af98-ef6484dea201 · outbound

This paper cites Tamm: Triadapter multi-modal learning for 3d shape understanding.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Tamm: Triadapter multi-modal learning for 3d shape understanding

Reference 34

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source=pdf_text observed=2026-08-07T00:29:38.990301Z digest=sha256:4a5362e1e941c200b41fb3844dd27e650f7658e0cd02d5b1ad6ec3d24e95635a

Observation 02f12c69-a25c-4bd4-b596-31d97e29e1de · outbound

This paper cites Independence Tests for Language Models.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Independence Tests for Language Models

Reference 35

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Observation b8b2b163-85ed-4645-b42d-4a6d30712853 · outbound

This paper cites 5, we present the supervised UMAP projections of the final activations from different models inFig.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs 5, we present the supervised UMAP projections of the final activations from different models inFig

Reference 39

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation bd601793-c55b-4756-bc88-b516081fcb56 · outbound

This paper cites Both evaluations report the accuracy on four-choice question answering.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Both evaluations report the accuracy on four-choice question answering

Reference 2003

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:29:39.204815Z digest=sha256:2b83bf44d47d08d700578398e5e42486c5e6e9b7eac9f8ce4fc0963ee1830473

Observation 4ab2cb67-5ceb-4a61-b78a-855c2da85004 · outbound

This paper cites Unlearn What You Want to Forget: Efficient Unlearning for LLMs.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Reference 2015

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source=pdf_text observed=2026-08-07T00:29:35.001839Z digest=sha256:b6d6a2f2da7afedf5f4281d8569bec8c893e83029a73a940249b9f7775c3358d

Observation 97e4c419-62ee-44b0-8b9d-d8bd9b56035e · outbound

This paper cites An Approach to Technical AGI Safety and Security.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs An Approach to Technical AGI Safety and Security

Reference 2016

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source=pdf_text observed=2026-08-07T00:29:37.550916Z digest=sha256:d837909b2d457bc1229087e2efec6d4606ec363a1c99175667878f22f57d5c47

Observation 01e30e16-c906-4588-a0c1-b81ac63f55c6 · outbound

This paper cites Knowledge Unlearning for Mitigating Privacy Risks in Language Models.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Knowledge Unlearning for Mitigating Privacy Risks in Language Models

Reference 2018

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source=pdf_text observed=2026-08-07T00:29:36.258693Z digest=sha256:dcc2987b32d94dc10ea493cf0cb5083e242174e302dc34e4c133886eb1e104f1

Observation 781ad25d-09ba-40a5-bdc5-5cd665d73c2d · outbound

This paper cites Jogging the memory of unlearned models through targeted relearning attacks.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Jogging the memory of unlearned models through targeted relearning attacks

Reference 2019

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raw_fallback, observed 2026-08-07T00:29:42.589838Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:29:35.857573Z digest=sha256:b45a7e02c978de33d9a8261497aecf7203863a5f5a95ebe6c9d4012104ed89b3

Observation c7711d74-f07c-4d67-bf4b-5ea719eee5f4 · outbound

This paper cites Regulation (eu) 2016/679 of the european parliament and of the council.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Regulation (eu) 2016/679 of the european parliament and of the council

Reference 2020

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raw_fallback, observed 2026-08-07T00:29:42.316310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:29:37.443872Z digest=sha256:becbfed3c0fcf909d6e62c6d5305cd90c0c792451c5948d434f9c2c21bfad6fb

Observation ce3262ff-1568-4f5b-a3fd-c9a5b5b2350a · outbound

This paper cites Simplicity prevails: Rethinking negative preference optimization for llm unlearning.arXiv preprint arXiv:2410.07163,.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Simplicity prevails: Rethinking negative preference optimization for llm unlearning.arXiv preprint arXiv:2410.07163,

Reference 2021

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unresolved
no resolver link, observed 2026-08-07T00:29:35.574913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:29:35.574913Z digest=sha256:09bb36a1ea6a8c9d2a2f50d7f822a4352915de4cefeb5ec9510845f6fe8be1b0

Observation b4d24ac3-df92-400a-94eb-00c1b892ea1d · outbound

This paper cites Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-07T00:29:35.246287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:29:35.246287Z digest=sha256:f85045aa6e52cd5a614713bb2248ffa4024fa6003acb338df5c819bc577f1eb8

Observation b2982730-17ba-4a00-8b4f-223e125321b1 · outbound

This paper cites When machine unlearning jeopardizes privacy.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs When machine unlearning jeopardizes privacy

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:29:42.812670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:29:35.112978Z digest=sha256:ae26ceba381c1324e42f4af3b4085542b18662558f7624ba27d36e1b01bbb5d3

Observation 59a716f1-489a-4f4e-beec-8765cfeb6d3f · outbound

This paper cites Do Unlearning Methods Remove Information from Language Model Weights?.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Do Unlearning Methods Remove Information from Language Model Weights?

Reference 2024

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unresolved
no resolver link, observed 2026-08-07T00:29:35.464938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:29:35.464938Z digest=sha256:f945c83c963671cea1d46faa327dc693553a8728b86e26142544aec5d62555ff

Observation ee45d036-973b-475c-969f-30a03eb3a631 · outbound

This paper cites Machine unlearning doesn’t do what you think: Lessons for generative ai policy, research, and practice.arXiv preprint arXiv:2412.06966,.

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs Machine unlearning doesn’t do what you think: Lessons for generative ai policy, research, and practice.arXiv preprint arXiv:2412.06966,

Reference 2025

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unresolved
no resolver link, observed 2026-08-07T00:29:35.364619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:29:35.364619Z digest=sha256:ad97b698a26eeeedd07dfad61c03ab490c2a89ec6d1ea01b6d5ac9f2e5d33cd4

Pith citing papers

Observation c903e360-09e4-4201-a40d-c44c1eb6dbf9 · inbound

SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents cites this paper.

SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

Reference 40

Resolution
metadata mismatch
local_arxiv, observed 2026-07-03T14:58:33.259815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-27T06:49:12.070481Z digest=sha256:0ebf17324ca253f3a7c8d80fe19bfe51a83046489dfffda9d4dd8a6fcca9d8f9