Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:16:59.215569Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2506.11253.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:16:59.215569Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-10T15:38:58.361411Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T15:47:23.228937Z
52 of 52 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation c490bfde-19b5-41cf-a976-970fc9648ca9 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models GPT-4 Technical Report
Reference 1
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Observation d7ceea3b-f7b0-49e1-9f61-e3d07cae6de8 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Related W ork T ask Unlearned Model/T arget Golatkar et al
Reference 7
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Observation 75931b67-728b-4783-b5de-2d0eb9c841c4 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
Reference 8
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Observation 007309cc-74d7-4621-aa4c-a77654ce4fcb · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Simplicity prevails: Rethinking negative preference optimization for llm unlearning.arXiv preprint arXiv:2410.07163,
Reference 9
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Observation 4f245604-fae7-474c-af7d-95d115213e9c · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models The importance of forgetting.Nature, 571(July):S12–S14,
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Observation 4973d31a-74b5-4a98-8a1e-b401339cde04 · outbound
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Observation a56f72a9-7798-42c3-bc65-3fbfa7ee0543 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
Reference 17
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Observation f6852d84-a3f9-458d-8d7d-613a824a1f5b · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Reference 18
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Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models LLaVA-OneVision: Easy Visual Task Transfer
Reference 19
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Observation 535be7b8-3d09-4be7-9965-7f2252555238 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Rethinking Machine Unlearning for Large Language Models
Reference 20
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Observation cdd913a3-95d7-46ad-a4e1-85a1525769a1 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models TOFU: A Task of Fictitious Unlearning for LLMs
Reference 21
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Observation e58cea4e-b143-449c-8fc0-1ea088d448ca · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Fast Model Editing at Scale
Reference 22
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Observation 274b3b34-4868-4762-b2eb-77209c76f17e · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models GPT-4o System Card
Reference 23
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Observation 5465c0b5-06cd-4b7b-9fa8-6546f635fc50 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Direct Unlearning Optimization for Robust and Safe Text-to-Image Models
Reference 24
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Observation 93ee4420-2133-4e9a-a712-ce7dbf5443b4 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Safe-clip: Removing nsfw concepts from vision-and-language models
Reference 26
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Observation 64c8302c-e02c-4e6a-a036-87bfa10f9b4a · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models How Data Inter-connectivity Shapes LLMs Unlearning: A Structural Unlearning Perspective
Reference 27
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Observation e7c168d0-8417-41f3-a2dd-cb629ef0ff70 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Regulation (eu) 2016/679 of the european parliament and of the council.Regulation (eu), 679: 2016,
Reference 28
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Observation e2d4e09b-22ed-4b89-9cc6-e72f5ad8c016 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models MUSE: Machine Unlearning Six-Way Evaluation for Language Models
Reference 29
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Observation 90fd0c64-f36f-408c-94d2-a7125409c06a · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Position: LLM Unlearning Benchmarks are Weak Measures of Progress
Reference 30
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Observation 09e36e8b-b332-45dc-9c1a-a89d8eeef962 · outbound
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Reference 31
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Observation 0933fb63-482f-41dd-928c-8c92c120dcd0 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models LLaMA: Open and Efficient Foundation Language Models
Reference 32
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Observation 40ed7878-ec87-4a9d-bc7d-07cd5e6faf96 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition
Reference 33
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Observation 44f2363b-1af1-4026-adb0-4c781af1eced · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Reference 34
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Observation 173da392-4a5a-4887-9423-ddb7b129939b · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
Reference 35
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Observation c59366e9-0365-47a5-bfe3-423e19ff0cfc · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP
Reference 36
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Observation ecf141a7-2300-4847-ba99-d043a8f50227 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Machine Unlearning of Pre-trained Large Language Models
Reference 37
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Observation bbc83ff9-b702-4e5f-a336-df8a0bb7fe04 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Large Language Model Unlearning
Reference 38
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Observation 5aceda00-4049-4784-b77b-801c094bf065 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Unlearning bias in language models by partitioning gradients
Reference 39
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Observation 28a29f8b-6728-4cff-a65d-e9704c1e0701 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models A Closer Look at Machine Unlearning for Large Language Models
Reference 40
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Observation ab5c8f5c-bc6d-4d91-a19a-2c1b6a5ba49b · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency
Reference 41
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Observation 54a55582-cf63-440d-85cd-08ffc6999436 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models What makes unlearning hard and what to do about it
Reference 42
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Observation 27b04d05-3bff-4801-976b-1f315f431fc0 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Fortuitous Forgetting in Connectionist Networks
Reference 43
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Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models We systematically categorize unlearning tasks, models, and targets of related papers in Table
Reference 44
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Reference 46
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Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Unresolved cited work
Reference 47
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Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Unresolved cited work
Reference 48
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Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models According to the results shown 20 Published in Transactions on Machine Learning Research (May/2026) Table 10: Prompts of CompCars-S and ImgnetDogs dataset
Reference 50
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Observation c9e1a491-a0a6-4409-96c3-91e05903e3ec · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Additionally, relabeling- based methods fail to achieve effective unlearning, similar to their performance on the ImgnetDogs dataset
Reference 51
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Observation d15c1bde-18a0-4b5b-9fb4-06bf0ca69e87 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Dataset Food101 Flower102 Caltech101 OxfordPet Cifar100 Avg↑ Origin CLIP (Radford et al.,
Reference 52
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Observation 9f9f822c-f12f-4abc-a476-d36abfa31992 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Reference 1998
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Observation 2a873164-27e0-4932-9b91-e1a2168124e7 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Who's Harry Potter? Approximate Unlearning in LLMs
Reference 2009
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Observation 6569e482-0535-4487-850e-fad30ca062d1 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models In-Context Unlearning: Language Models as Few Shot Unlearners
Reference 2012
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Observation 420a2376-ca45-4692-b973-a13f33a65aa1 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Efficient repair of polluted machine learning systems via causal unlearning
Reference 2015
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Observation cbac58b9-5639-45c6-b510-164b33f4e541 · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models On the Opportunities and Risks of Foundation Models
Reference 2019
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Observation 039d944a-0e88-4175-a9f4-dd7522ad6c7d · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models The optimization objective for relabeling is as follows: LRelabel = ∑ (xi,.)∈Df [−log(yrand|xi,θ)],(8) wherey rand is randomly chosen from the label set andyrand̸=y f
Reference 2020
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Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,
Reference 2021
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Reference 2022
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Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Microsoft COCO Captions: Data Collection and Evaluation Server
Reference 2023
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Observation 0f26619f-a3be-4b77-a9e6-91693dd8827f · outbound
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark
Reference 2024
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Observation 1de5dcf1-9dc5-4024-b11b-a803ebcb148f · outbound
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Reference 2025
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Observation 433a215d-df29-4d63-b34d-6cbbf17cf988 · inbound
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models
Reference 113
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