Paper Citation Record · LEDGER

Existing Large Language Model Unlearning Evaluations Are Inconclusive

As of 17 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2506.00688.

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

pith.paper-citation-record.v1
2506.00688 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

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measured 60 of 60 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:59:54.681611Z

Reference resolution

59 of 59 outbound references displayed

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External citation measurements

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Outbound references

Observation ff707d62-44fc-480d-800a-0dc80713fde2 · outbound

This paper cites Are aligned neural networks adversarially aligned?Advances in Neural Information Processing Systems, 36, 2024.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Are aligned neural networks adversarially aligned?Advances in Neural Information Processing Systems, 36, 2024

Reference 1

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Observation f98b6eca-868f-4d71-aee7-50f5265849f6 · outbound

This paper cites Rethinking machine unlearning for large language models.Nature Machine Intelligence, pages 1–14, 2025.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Rethinking machine unlearning for large language models.Nature Machine Intelligence, pages 1–14, 2025

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:06:00.384033Z

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Observation 704d0e38-1681-42cb-90bf-082a31d18d0d · outbound

This paper cites The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning.

Existing Large Language Model Unlearning Evaluations Are Inconclusive The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

Reference 3

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Observation b40dc7ac-ded5-4a71-9527-9005cb32af62 · outbound

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

Existing Large Language Model Unlearning Evaluations Are Inconclusive Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning

Reference 4

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Observation ed586202-c046-4069-a8b9-3cad2ae29c10 · outbound

This paper cites Model manipulation attacks enable more rigorous evaluations of llm capabilities.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Model manipulation attacks enable more rigorous evaluations of llm capabilities

Reference 5

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Observation 519bc41e-0dcb-4c33-8e15-b3338189d42c · outbound

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

Existing Large Language Model Unlearning Evaluations Are Inconclusive An Adversarial Perspective on Machine Unlearning for AI Safety

Reference 6

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Observation 6e88bdd6-2a6c-4927-89cf-0ce6d10d836f · outbound

This paper cites Rethinking LLM Memorization through the Lens of Adversarial Compression.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Rethinking LLM Memorization through the Lens of Adversarial Compression

Reference 7

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Observation 8a71b73c-bb07-48a4-9b9d-98a0cfc6a2b0 · outbound

This paper cites Machine unlearning: Solutions and challenges.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Machine unlearning: Solutions and challenges

Reference 8

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Observation 8df5076b-e95c-4178-b086-4714ef621573 · outbound

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

Existing Large Language Model Unlearning Evaluations Are Inconclusive On the necessity of auditable algorithmic definitions for machine unlearning

Reference 9

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Observation d63ed356-3913-4242-962b-683dce0f9f0f · outbound

This paper cites Arcane: An efficient architecture for exact machine unlearning.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Arcane: An efficient architecture for exact machine unlearning

Reference 10

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Observation f80afe54-171c-43b7-80ce-8e8ba7e26c2a · outbound

This paper cites Certified Data Removal from Machine Learning Models.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Certified Data Removal from Machine Learning Models

Reference 11

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Observation e9204cbc-2532-4d49-9e4e-e266e13ef43b · outbound

This paper cites Amnesiac machine learning.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Amnesiac machine learning

Reference 12

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Observation 0147fd8c-4e39-4fd1-a844-f63bfb74ece0 · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022

Reference 13

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Observation 53739170-9478-45d9-ab81-0fe1a5c102e9 · outbound

This paper cites Position: LLM Unlearning Benchmarks are Weak Measures of Progress.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Position: LLM Unlearning Benchmarks are Weak Measures of Progress

Reference 14

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Observation d9c58f53-d6d7-4332-8e4c-5de37f7d0bdd · outbound

This paper cites A Probabilistic Perspective on Unlearning and Alignment for Large Language Models.

Existing Large Language Model Unlearning Evaluations Are Inconclusive A Probabilistic Perspective on Unlearning and Alignment for Large Language Models

Reference 15

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Observation ecd16d61-77a1-42e0-876a-809b36c4e673 · outbound

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

Existing Large Language Model Unlearning Evaluations Are Inconclusive Regulation (eu) 2016/679 of the european parliament and of the council

Reference 16

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Observation bbad220d-79b2-4982-b824-b40a48065d49 · outbound

This paper cites UK General Data Protection Regulation (UK GDPR), 2021.

Existing Large Language Model Unlearning Evaluations Are Inconclusive UK General Data Protection Regulation (UK GDPR), 2021

Reference 17

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Observation d188f6ad-6f9e-4d2f-8da3-3478197b30bd · outbound

This paper cites Ccpa regulations: Final regulation text.Office of the Attorney General, California Department of Justice, 2021.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Ccpa regulations: Final regulation text.Office of the Attorney General, California Department of Justice, 2021

Reference 18

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Observation 5f14058e-993b-4af9-94a4-fe348c6c5c51 · outbound

This paper cites Bill C-27: Digital Charter Implementation Act, 2022 – Consumer Privacy Protection Act (CPPA), 2022.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Bill C-27: Digital Charter Implementation Act, 2022 – Consumer Privacy Protection Act (CPPA), 2022

Reference 19

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Observation ba6e05a5-65e4-4879-b2cc-3a16b5747e45 · outbound

This paper cites Machine unlearning via algorithmic stability.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Machine unlearning via algorithmic stability

Reference 20

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Machine unlearning

Reference 21

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Observation 6e098255-a136-44a3-b54c-9085396652b5 · outbound

This paper cites Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations

Reference 22

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Observation 896cf14d-407c-441a-b87b-541bf7c292d0 · outbound

This paper cites Making ai forget you: Data deletion in machine learning.Advances in neural information processing systems, 32, 2019.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Making ai forget you: Data deletion in machine learning.Advances in neural information processing systems, 32, 2019

Reference 23

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Observation 127de00a-e182-44b7-a0f8-9b8e544c3df7 · outbound

This paper cites The algorithmic foundations of differential privacy.Founda- tions and Trends® in Theoretical Computer Science, 9(3–4):211–407, 2014.

Existing Large Language Model Unlearning Evaluations Are Inconclusive The algorithmic foundations of differential privacy.Founda- tions and Trends® in Theoretical Computer Science, 9(3–4):211–407, 2014

Reference 24

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Approximate data deletion from machine learning models

Reference 25

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Observation b9b3f8db-d4a4-444f-bf52-1fc94e50c7b8 · outbound

This paper cites Remember what you want to forget: Algorithms for machine unlearning.Advances in Neural Information Processing Systems, 34:18075–18086, 2021.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Remember what you want to forget: Algorithms for machine unlearning.Advances in Neural Information Processing Systems, 34:18075–18086, 2021

Reference 26

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Who's Harry Potter? Approximate Unlearning in LLMs

Reference 27

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Existing Large Language Model Unlearning Evaluations Are Inconclusive TOFU: A Task of Fictitious Unlearning for LLMs

Reference 28

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset

Reference 29

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Existing Large Language Model Unlearning Evaluations Are Inconclusive MUSE: Machine Unlearning Six-Way Evaluation for Language Models

Reference 30

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Existing Large Language Model Unlearning Evaluations Are Inconclusive RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models

Reference 31

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Open Problems in Machine Unlearning for AI Safety

Reference 32

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Existing Large Language Model Unlearning Evaluations Are Inconclusive JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

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This paper cites Simplicity prevails: Rethinking negative preference optimization for llm unlearning.arXiv preprint arXiv:2410.07163, 2024.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Simplicity prevails: Rethinking negative preference optimization for llm unlearning.arXiv preprint arXiv:2410.07163, 2024

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Improving alignment and robustness with circuit breakers

Reference 35

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This paper cites Fast yet effective machine unlearning.IEEE Transactions on Neural Networks and Learning Systems, 2023.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Fast yet effective machine unlearning.IEEE Transactions on Neural Networks and Learning Systems, 2023

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This paper cites Self- destructing models: Increasing the costs of harmful dual uses of foundation models.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Self- destructing models: Increasing the costs of harmful dual uses of foundation models

Reference 37

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Tamper-Resistant Safeguards for Open-Weight LLMs

Reference 38

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Do Unlearning Methods Remove Information from Language Model Weights?

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Eight Methods to Evaluate Robust Unlearning in LLMs

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Existing Large Language Model Unlearning Evaluations Are Inconclusive LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet

Reference 41

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Scalable Extraction of Training Data from (Production) Language Models

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Preventing generation of verba- tim memorization in language models gives a false sense of privacy

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:58.146074Z

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This paper cites Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning

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Observation fece8aae-3c51-4d92-bf7a-a10a4ac90f65 · outbound

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Jailbreaking Black Box Large Language Models in Twenty Queries

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Universal and Transferable Adversarial Attacks on Aligned Language Models

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Jailbreaking LLM-Controlled Robots

Reference 47

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Existing Large Language Model Unlearning Evaluations Are Inconclusive FLRT: Fluent Student-Teacher Redteaming

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Rush, and Thomas Wolf

Reference 49

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Textbooks Are All You Need II: phi-1.5 technical report

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Existing Large Language Model Unlearning Evaluations Are Inconclusive The Llama 3 Herd of Models

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This paper cites Are Transformers universal approximators of sequence-to-sequence functions?.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Are Transformers universal approximators of sequence-to-sequence functions?

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Inside-Out: Hidden Factual Knowledge in LLMs

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Information-Theoretic Probing for Linguistic Structure

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Existing Large Language Model Unlearning Evaluations Are Inconclusive Quantifying Semantic Emergence in Language Models

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:05:57.239317Z

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Observation 4610c999-c621-45b3-bfd6-2721d8619e66 · outbound

This paper cites Measuring and modifying factual knowledge in large language models.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Measuring and modifying factual knowledge in large language models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:57.956958Z

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Observation b7b88fb4-20ef-490b-a7e5-975aef909afc · outbound

This paper cites Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:56.757190Z

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Observation 9c9e14a4-7774-4374-9178-d1651ba39f18 · outbound

This paper cites Large Language Models Are Not Robust Multiple Choice Selectors.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Large Language Models Are Not Robust Multiple Choice Selectors

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:56.806697Z

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Observation c2dbf4f5-0b16-4863-992b-3b745ee773c4 · outbound

This paper cites Does Prompt Formatting Have Any Impact on LLM Performance?.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Does Prompt Formatting Have Any Impact on LLM Performance?

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:56.921126Z

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Pith citing papers

Observation cacc932b-c9e3-42f4-b8a4-a2b303386651 · inbound

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs cites this paper.

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs Existing Large Language Model Unlearning Evaluations Are Inconclusive

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:59:54.745064Z

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