Paper Citation Record · LEDGER

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning

As of 15 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 4 inbound Pith citation observations for arXiv:2505.15178.

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

pith.paper-citation-record.v1
2505.15178 v1

Coverage vector

measured 80 of 80 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:28:57.498744Z

measured 84 of 84 standing notices

One-hop event checks from named stored sources.

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:58:48.869438Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T07:14:20.727303Z

Reference resolution

80 of 80 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4df6bb2f-2afa-4ae3-869e-f8def933e128 · outbound

This paper cites Scaling Laws for Neural Language Models.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Scaling Laws for Neural Language Models

Reference 1

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Observation ae0859b5-857b-4f57-b7b2-4546dd30a2fa · outbound

This paper cites Continual learning and private unlearning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Continual learning and private unlearning,

Reference 2

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Observation a148c132-cf93-46ca-a80c-a440ec97eaba · outbound

This paper cites A unified framework for continual learning and unlearning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning A unified framework for continual learning and unlearning,

Reference 3

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Observation d8033304-4c19-41fd-939c-0191505ca352 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning A continual learning survey: Defying forgetting in classification tasks,

Reference 4

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Observation bb7f31fd-3562-43e7-9339-dcbc82bcdb01 · outbound

This paper cites A comprehensive survey of continual learning: Theory, method and application,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning A comprehensive survey of continual learning: Theory, method and application,

Reference 5

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Observation aee6a4fd-7441-421d-805a-289293e03f22 · outbound

This paper cites Class-Incremental Learning: A Survey.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Class-Incremental Learning: A Survey

Reference 6

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Observation 8d255614-cfca-401b-af7d-5700e5224db9 · outbound

This paper cites Machine unlearn- ing,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Machine unlearn- ing,

Reference 7

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Observation 346ce5cb-02ea-493e-a677-566ae7382196 · outbound

This paper cites Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy

Reference 8

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This paper cites Machine unlearning: Solutions and challenges,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Machine unlearning: Solutions and challenges,

Reference 9

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Observation 16f01921-2212-4913-9776-e298d1e6f6df · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Overcoming catastrophic forgetting in neural networks,

Reference 10

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This paper cites Online structured laplace approximations for overcoming catastrophic forgetting,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Online structured laplace approximations for overcoming catastrophic forgetting,

Reference 11

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Observation c1cabd9d-7b84-44f1-8070-0b4f124a7a64 · outbound

This paper cites Learning without forgetting,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Learning without forgetting,

Reference 12

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Observation d47837e8-8504-4a8e-8332-2efc7c62ca05 · outbound

This paper cites Learning a unified classifier incrementally via rebalancing,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Learning a unified classifier incrementally via rebalancing,

Reference 13

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Observation b8b87c58-4764-49da-9399-684b721a3546 · outbound

This paper cites On Tiny Episodic Memories in Continual Learning.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning On Tiny Episodic Memories in Continual Learning

Reference 14

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This paper cites Dark experience for general continual learning: a strong, simple baseline,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Dark experience for general continual learning: a strong, simple baseline,

Reference 15

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Observation 001866c1-382f-4c68-a8ad-0dd4ff373a56 · outbound

This paper cites icarl: Incremental classifier and representation learning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning icarl: Incremental classifier and representation learning,

Reference 16

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Observation 5dba99b9-6ad7-4f64-98f0-744156fcb3f9 · outbound

This paper cites Gradient episodic memory for continual learning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Gradient episodic memory for continual learning,

Reference 17

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Observation ec57b75b-9118-4f99-b8de-bf8a531fae34 · outbound

This paper cites Mofo: Momentum-filtered optimizer for mitigating forgetting in llm fine-tuning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Mofo: Momentum-filtered optimizer for mitigating forgetting in llm fine-tuning,

Reference 18

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Observation 346e4c59-f97e-4ada-8e2f-a3c71fe6bee6 · outbound

This paper cites Hft: Half fine-tuning for large language models,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Hft: Half fine-tuning for large language models,

Reference 19

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This paper cites Low dimensional trajectory hypothesis is true: Dnns can be trained in tiny subspaces,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Low dimensional trajectory hypothesis is true: Dnns can be trained in tiny subspaces,

Reference 20

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Lora: Low-rank adaptation of large language models,

Reference 21

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This paper cites Hesscale: Scalable computation of hessian diagonals,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Hesscale: Scalable computation of hessian diagonals,

Reference 22

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This paper cites Unified gradient-based machine unlearning with remain geom- etry enhancement,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Unified gradient-based machine unlearning with remain geom- etry enhancement,

Reference 23

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This paper cites Catastrophic interference in con- nectionist networks: The sequential learning problem,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Catastrophic interference in con- nectionist networks: The sequential learning problem,

Reference 24

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This paper cites Connectionist models of recognition memory: con- straints imposed by learning and forgetting functions.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Connectionist models of recognition memory: con- straints imposed by learning and forgetting functions

Reference 25

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This paper cites Continual learning through synaptic intelligence,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Continual learning through synaptic intelligence,

Reference 26

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This paper cites Descent-to-delete: Gradient-based methods for machine unlearning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Descent-to-delete: Gradient-based methods for machine unlearning,

Reference 27

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This paper cites Remember what you want to forget: Algorithms for machine unlearning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Remember what you want to forget: Algorithms for machine unlearning,

Reference 28

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Making ai forget you: Data deletion in machine learning,

Reference 29

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Cer- tified data removal from machine learning models,

Reference 30

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Understanding black-box predictions via influence functions,

Reference 31

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

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Eternal sunshine of the spotless net: Selective forgetting in deep networks,

Reference 32

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Machine unlearning of features and labels,

Reference 33

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Model sparsity can simplify machine unlearning,

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Amnesiac machine learn- ing,

Reference 35

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Un- rolling sgd: Understanding factors influencing machine unlearn- ing,

Reference 36

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This paper cites Can bad teaching induce forgetting? unlearning in deep net- works using an incompetent teacher,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Can bad teaching induce forgetting? unlearning in deep net- works using an incompetent teacher,

Reference 37

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This paper cites Natural gradient works efficiently in learning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Natural gradient works efficiently in learning,

Reference 38

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning New insights and perspectives on the natural gradient method,

Reference 39

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This paper cites Natural gradient methods: Perspectives, efficient- scalable approximations, and analysis,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Natural gradient methods: Perspectives, efficient- scalable approximations, and analysis,

Reference 40

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Learning with selective forgetting,

Reference 41

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This paper cites Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines

Reference 42

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This paper cites Three scenarios for continual learning.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Three scenarios for continual learning

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Approximate Data Deletion from Machine Learning Models

Reference 44

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This paper cites The elements of statistical learning: Data mining, inference, and prediction,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning The elements of statistical learning: Data mining, inference, and prediction,

Reference 45

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This paper cites The loss surfaces of multilayer networks,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning The loss surfaces of multilayer networks,

Reference 46

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Stochastic gradient descent as approximate bayesian inference,

Reference 47

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This paper cites A Unified and General Framework for Continual Learning.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning A Unified and General Framework for Continual Learning

Reference 48

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A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Steepest descent algorithms for optimization under unitary matrix constraint,

Reference 49

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This paper cites Fisher sam: Information geometry and sharpness aware minimisation,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Fisher sam: Information geometry and sharpness aware minimisation,

Reference 50

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This paper cites Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates

Reference 51

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This paper cites Geometric modeling in probability and statistics,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Geometric modeling in probability and statistics,

Reference 52

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Observation b258b2a8-7dee-4a56-95c2-add363d4d662 · outbound

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

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Salun: Em- powering machine unlearning via gradient-based weight saliency in both image classification and generation,

Reference 53

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This paper cites Learn to unlearn for deep neural networks: Minimizing unlearning interference with gradient projection,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Learn to unlearn for deep neural networks: Minimizing unlearning interference with gradient projection,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:04.041514Z

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This paper cites Machine un- learning in learned databases: An experimental analysis,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Machine un- learning in learned databases: An experimental analysis,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:03.780483Z

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This paper cites Towards un- bounded machine unlearning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Towards un- bounded machine unlearning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:03.499717Z

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Observation b29f5bd1-b764-4909-8b14-c239c3d770f4 · outbound

This paper cites Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers

Reference 57

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This paper cites On tiny episodic memories in continual learning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning On tiny episodic memories in continual learning,

Reference 58

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Observation 203fd2f0-6a53-4a80-895c-a5f090b17c88 · outbound

This paper cites Random sampling with a reservoir,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Random sampling with a reservoir,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:03.014321Z

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Observation 74f8e249-7a61-48df-902d-0a98c228d5e1 · outbound

This paper cites Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Reference 60

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Observation 78344da0-2e94-4103-9314-2c1b6a555c29 · outbound

This paper cites Continual learning with deep generative replay,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Continual learning with deep generative replay,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:02.776258Z

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Observation 58bfed0e-c42b-48da-925d-167d1f425032 · outbound

This paper cites Note on the quadratic penalties in elastic weight consolidation,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Note on the quadratic penalties in elastic weight consolidation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:02.521135Z

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Observation 2702df7a-2f12-48a7-949b-63ddbdb1cb38 · outbound

This paper cites Meta continual learning revisited: Implicitly enhancing online hessian approximation via variance reduction,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Meta continual learning revisited: Implicitly enhancing online hessian approximation via variance reduction,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:02.280030Z

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This paper cites Lookahead opti- mizer: k steps forward, 1 step back,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Lookahead opti- mizer: k steps forward, 1 step back,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:02.045437Z

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Observation d93f7019-4a19-4218-8b24-46e923f13d6b · outbound

This paper cites On First-Order Meta-Learning Algorithms.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning On First-Order Meta-Learning Algorithms

Reference 65

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This paper cites Bilevel continual learning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Bilevel continual learning,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:01.737209Z

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Observation 9934ff35-5cc8-4677-8bb2-8ae8c9ed262c · outbound

This paper cites Fast yet effective machine unlearning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Fast yet effective machine unlearning,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:01.470823Z

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This paper cites The shapley value in machine learning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning The shapley value in machine learning,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:01.240410Z

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Observation 50aef3b6-ccc3-44ca-8ad4-2df5516d0ba3 · outbound

This paper cites Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning

Reference 69

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This paper cites Fast machine unlearning without retraining through selective synaptic dampening,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Fast machine unlearning without retraining through selective synaptic dampening,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:01.032962Z

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Observation f164b15d-1d71-4ae8-84d1-5696430f0e09 · outbound

This paper cites Towards adversarial evaluations for inexact machine unlearning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Towards adversarial evaluations for inexact machine unlearning,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:00.785407Z

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Observation 373310e9-4570-4c10-812a-ec1c3affa56e · outbound

This paper cites Systematic evaluation of privacy risks of machine learning models,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Systematic evaluation of privacy risks of machine learning models,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:00.531868Z

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This paper cites Membership inference attacks from first principles,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Membership inference attacks from first principles,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:00.279267Z

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Observation 643a2c1c-7b81-47eb-82c1-a073b9f33a36 · outbound

This paper cites New insights on reducing abrupt representation change in online continual learning,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning New insights on reducing abrupt representation change in online continual learning,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:00.081717Z

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Observation 6ed79d2e-c353-433c-8d91-60ea198fa492 · outbound

This paper cites Learning multiple layers of features from tiny images,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Learning multiple layers of features from tiny images,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:59.771899Z

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Observation a208ecb4-4d7c-48fb-b321-154d23f3b1aa · outbound

This paper cites Deep residual learning for image recognition,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Deep residual learning for image recognition,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:59.539556Z

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Observation ef05fdef-889b-4677-8643-74481aaa2d8a · outbound

This paper cites Tiny imagenet visual recognition challenge,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Tiny imagenet visual recognition challenge,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:59.238245Z

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Observation 402031c7-2b0c-4279-8210-de6106678ebf · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:58.948416Z

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Observation 33b5bfe3-5752-45ae-bf92-ae349f350960 · outbound

This paper cites Decoupled weight decay regulariza- tion,.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Decoupled weight decay regulariza- tion,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:58.617822Z

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Observation 3f7cd9b1-eb46-4152-9f66-011f220c7718 · outbound

This paper cites (A15) By plugging (A15) into (A10), we can get ∇L(θk) =G L(θk) ≈ 1 2 H L k H R ∗ −1 h ∇LL(θk;1−ε L k ) +∇L U (θk;−ε U k ) i.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning (A15) By plugging (A15) into (A10), we can get ∇L(θk) =G L(θk) ≈ 1 2 H L k H R ∗ −1 h ∇LL(θk;1−ε L k ) +∇L U (θk;−ε U k ) i

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:58.281076Z

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Pith citing papers

Observation b71c4b26-3025-431d-9da9-770031f54b99 · inbound

SoK: Machine Unlearning for Large Language Models cites this paper.

SoK: Machine Unlearning for Large Language Models A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T04:58:48.869438Z

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BID-LoRA: A Parameter-Efficient Framework for Continual Learning and Unlearning cites this paper.

BID-LoRA: A Parameter-Efficient Framework for Continual Learning and Unlearning A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:11:02.743387Z

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Observation dbc51e9d-7cce-4781-b01c-1a8d4273ac93 · inbound

Robust Continual Unlearning against Knowledge Erosion and Forgetting Reversal cites this paper.

Robust Continual Unlearning against Knowledge Erosion and Forgetting Reversal A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:03.549183Z

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Observation b941f4f3-376d-49d1-a286-ef97d4637dc2 · inbound

The Forgetting-Retention Dilemma: Certified Unlearning Theory in Continual Learning cites this paper.

The Forgetting-Retention Dilemma: Certified Unlearning Theory in Continual Learning A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:14:20.733059Z

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