Paper Citation Record · LEDGER

Parameter-Efficient Continual Fine-Tuning: A Survey

As of 18 August 2026, this Paper Citation Record lists 100 of 175 outbound references and 2 inbound Pith citation observations for arXiv:2504.13822.

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

pith.paper-citation-record.v1
2504.13822 v3

Coverage vector

measured 100 of 175 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:01:30.424989Z

measured 102 of 102 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:50:59.444442Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T08:04:28.959690Z

Reference resolution

100 of 175 outbound references displayed

verified exact1
verified fuzzy0
unresolved99
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7bd3882e-2f68-448d-8d2e-37dfbae842c0 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Parameter-Efficient Continual Fine-Tuning: A Survey An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 1

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Observation fb50f802-2c8f-41c1-bb7b-f0da7704ba11 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget.

Parameter-Efficient Continual Fine-Tuning: A Survey Memory aware synapses: Learning what (not) to forget

Reference 2

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Observation 2a192b34-40ac-450c-8207-0276f06ce401 · outbound

This paper cites Expert gate: Lifelong learning with a network of experts.

Parameter-Efficient Continual Fine-Tuning: A Survey Expert gate: Lifelong learning with a network of experts

Reference 3

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Observation b676cd6c-b06b-49bb-b02a-b7ea496284b7 · outbound

This paper cites Task-free con- tinual learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Task-free con- tinual learning

Reference 4

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Observation 618daa37-f563-4f58-828c-f3ead0e8d43b · outbound

This paper cites Beyond Supervised Continual Learning: a Review.

Parameter-Efficient Continual Fine-Tuning: A Survey Beyond Supervised Continual Learning: a Review

Reference 5

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Observation 3cca64fc-b20d-4ddd-8be7-f04f04f5d8b4 · outbound

This paper cites Rainbow memory: Continual learning with a memory of diverse samples, 2021.

Parameter-Efficient Continual Fine-Tuning: A Survey Rainbow memory: Continual learning with a memory of diverse samples, 2021

Reference 6

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Observation d5aaf295-d80d-4377-989b-5462fbc8bd98 · outbound

This paper cites Stanley, Jeff Clune, and Nick Cheney.

Parameter-Efficient Continual Fine-Tuning: A Survey Stanley, Jeff Clune, and Nick Cheney

Reference 7

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Observation 1be1a0a0-b1b6-4f53-b10a-308b39a41470 · outbound

This paper cites Language models are few-shot learners.

Parameter-Efficient Continual Fine-Tuning: A Survey Language models are few-shot learners

Reference 8

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Observation b3b3782d-f2a1-4506-ba28-b1dc27282383 · outbound

This paper cites New insights on reducing abrupt rep- resentation change in online continual learning, 2022.

Parameter-Efficient Continual Fine-Tuning: A Survey New insights on reducing abrupt rep- resentation change in online continual learning, 2022

Reference 9

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Observation 3da96010-cb47-43fe-a5e9-f842adcf08bf · outbound

This paper cites Online fast adaptation and knowl- edge accumulation: a new approach to continual learning, 2021.

Parameter-Efficient Continual Fine-Tuning: A Survey Online fast adaptation and knowl- edge accumulation: a new approach to continual learning, 2021

Reference 10

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This paper cites Riemannian walk for incremental learning: Understanding forgetting and intransigence.

Parameter-Efficient Continual Fine-Tuning: A Survey Riemannian walk for incremental learning: Understanding forgetting and intransigence

Reference 11

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Observation 298cea90-2a46-41c7-99cd-3f84c6e5f254 · outbound

This paper cites Efficient Lifelong Learning with A-GEM.

Parameter-Efficient Continual Fine-Tuning: A Survey Efficient Lifelong Learning with A-GEM

Reference 12

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Observation 73a88a2f-5e89-420f-974d-7c909f7b5cd3 · outbound

This paper cites Efficient lifelong learning with a-gem, 2019.

Parameter-Efficient Continual Fine-Tuning: A Survey Efficient lifelong learning with a-gem, 2019

Reference 13

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Observation 13b2c5ea-f60c-45df-b541-f9568e4d9797 · outbound

This paper cites Dual low-rank adaptation for continual learning with pre-trained models, 2024.

Parameter-Efficient Continual Fine-Tuning: A Survey Dual low-rank adaptation for continual learning with pre-trained models, 2024

Reference 14

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Observation 5dbf9c21-fdce-4b39-8bdc-effd40966c34 · outbound

This paper cites Cat: Continual adapter tuning for aspect sentiment classification.

Parameter-Efficient Continual Fine-Tuning: A Survey Cat: Continual adapter tuning for aspect sentiment classification

Reference 15

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Observation 32ea2656-1ada-4ed2-bf73-1ad6b77b85e4 · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition, 2022.

Parameter-Efficient Continual Fine-Tuning: A Survey Adaptformer: Adapting vision transformers for scalable visual recognition, 2022

Reference 16

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Observation 2781e6ac-5c65-440e-8ab4-4e6e57102cab · outbound

This paper cites Semi-supervised and unsupervised deep visual learning: A survey.

Parameter-Efficient Continual Fine-Tuning: A Survey Semi-supervised and unsupervised deep visual learning: A survey

Reference 17

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Observation c6958315-f06f-4b1b-8800-d4d1fdd7896f · outbound

This paper cites Lifelong machine learning , volume 1.

Parameter-Efficient Continual Fine-Tuning: A Survey Lifelong machine learning , volume 1

Reference 18

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Observation 0ee2d831-dedd-4e29-b539-419f463aa050 · outbound

This paper cites Task arithmetic with loRA for continual learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Task arithmetic with loRA for continual learning

Reference 19

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Observation ba74b198-2a74-48cb-a83e-09532de0df68 · outbound

This paper cites Adaptive LoRA merging for efficient domain incremental learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Adaptive LoRA merging for efficient domain incremental learning

Reference 20

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Observation 7c199123-22ef-47c1-ad61-9ca1972da076 · outbound

This paper cites Flattening sharpness for dynamic gradient projection memory benefits continual learning, 2021.

Parameter-Efficient Continual Fine-Tuning: A Survey Flattening sharpness for dynamic gradient projection memory benefits continual learning, 2021

Reference 21

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Observation 4caa91ad-40e2-4ff5-8dc9-ac6e6ccac0eb · outbound

This paper cites Qlora: Efficient finetuning of quantized llms, 2023.

Parameter-Efficient Continual Fine-Tuning: A Survey Qlora: Efficient finetuning of quantized llms, 2023

Reference 23

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Observation d1d69b2a-70aa-4b1a-b259-e83496c536e6 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language under- standing.

Parameter-Efficient Continual Fine-Tuning: A Survey Bert: Pre-training of deep bidirectional transformers for language under- standing

Reference 24

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Observation 87010c15-3277-437c-87a0-4389b7c7c900 · outbound

This paper cites Learning without memorizing, 2019.

Parameter-Efficient Continual Fine-Tuning: A Survey Learning without memorizing, 2019

Reference 25

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Observation 6258f71b-53bf-4db2-bbc6-a20d92db174e · outbound

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Parameter-Efficient Continual Fine-Tuning: A Survey Learning without memorizing

Reference 26

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Observation a5d385d5-b882-46d3-a9ba-bee681aae4bd · outbound

This paper cites Don't forget, there is more than forgetting: new metrics for Continual Learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Don't forget, there is more than forgetting: new metrics for Continual Learning

Reference 27

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Observation 38877ab3-5966-446c-931b-2fe977e7b0d0 · outbound

This paper cites Neural Logic Machines.

Parameter-Efficient Continual Fine-Tuning: A Survey Neural Logic Machines

Reference 28

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Observation a04f395d-b438-47f8-aeee-69a9d406d4d4 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recog- nition at scale, 2021.

Parameter-Efficient Continual Fine-Tuning: A Survey An image is worth 16x16 words: Transformers for image recog- nition at scale, 2021

Reference 29

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Observation 66ae09ab-62d6-43fa-baec-d4b9c4784df5 · outbound

This paper cites Podnet: Pooled outputs distillation for small-tasks incre- mental learning, 2020.

Parameter-Efficient Continual Fine-Tuning: A Survey Podnet: Pooled outputs distillation for small-tasks incre- mental learning, 2020

Reference 30

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This paper cites Hat-cl: A hard-attention-to-the-task pytorch library for continual learning, 2024.

Parameter-Efficient Continual Fine-Tuning: A Survey Hat-cl: A hard-attention-to-the-task pytorch library for continual learning, 2024

Reference 31

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Parameter-Efficient Continual Fine-Tuning: A Survey Orthogonal gradient descent for continual learning, 2019

Reference 32

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Observation 097ca61c-dbb5-489f-89db-40c39cbf3dbd · outbound

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Parameter-Efficient Continual Fine-Tuning: A Survey PathNet: Evolution Channels Gradient Descent in Super Neural Networks

Reference 33

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Observation c69816cd-d2f0-4a97-aea5-44bedfd4928a · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Parameter-Efficient Continual Fine-Tuning: A Survey The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 34

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Observation 30e73050-be12-4748-ab8f-8fc15e37755d · outbound

This paper cites On the effectiveness of parameter-efficient fine-tuning.

Parameter-Efficient Continual Fine-Tuning: A Survey On the effectiveness of parameter-efficient fine-tuning

Reference 35

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Parameter-Efficient Continual Fine-Tuning: A Survey A unified continual learning framework with general parameter-efficient tuning, 2023

Reference 36

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Parameter-Efficient Continual Fine-Tuning: A Survey Beyond prompt learning: Continual adapter for efficient rehearsal-free continual learning, 2024

Reference 37

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This paper cites Continual learning via neural pruning, 2019.

Parameter-Efficient Continual Fine-Tuning: A Survey Continual learning via neural pruning, 2019

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Parameter-Efficient Continual Fine-Tuning: A Survey PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning

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Parameter-Efficient Continual Fine-Tuning: A Survey On the domain adaptation and generalization of pretrained language models: A survey, 2022

Reference 40

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Parameter-Efficient Continual Fine-Tuning: A Survey La-maml: Look-ahead meta learning for continual learning, 2020

Reference 41

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Parameter-Efficient Continual Fine-Tuning: A Survey Towards a Unified View of Parameter-Efficient Transfer Learning

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Parameter-Efficient Continual Fine-Tuning: A Survey Towards a unified view of parameter-efficient transfer learning

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Parameter-Efficient Continual Fine-Tuning: A Survey Mera: Merging pretrained adapters for few-shot learning, 2023

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Parameter-Efficient Continual Fine-Tuning: A Survey Class- incremental learning with repetition, 2023

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Parameter-Efficient Continual Fine-Tuning: A Survey Distilling the knowledge in a neural network, 2015

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Parameter-Efficient Continual Fine-Tuning: A Survey Learning a unified classifier incrementally via rebalancing

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Parameter-Efficient Continual Fine-Tuning: A Survey Learning a unified classifier incrementally via rebalancing

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Parameter-Efficient Continual Fine-Tuning: A Survey Parameter-efficient transfer learning for nlp, 2019

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Parameter-Efficient Continual Fine-Tuning: A Survey Parameter-efficient transfer learning for NLP

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Parameter-Efficient Continual Fine-Tuning: A Survey Universal language model fine- tuning for text classification, 2018

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Parameter-Efficient Continual Fine-Tuning: A Survey Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 52

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Parameter-Efficient Continual Fine-Tuning: A Survey POP: Prompt Of Prompts for Continual Learning

Reference 53

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Parameter-Efficient Continual Fine-Tuning: A Survey Expand and merge: Continual learning with the guidance of fixed text embedding space

Reference 54

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Parameter-Efficient Continual Fine-Tuning: A Survey Memory population in con- tinual learning via outlier elimination

Reference 55

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This paper cites Optimizing reusable knowledge for continual learning via metalearning, 2021.

Parameter-Efficient Continual Fine-Tuning: A Survey Optimizing reusable knowledge for continual learning via metalearning, 2021

Reference 56

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This paper cites Continual learning for predictive maintenance: Overview and challenges.

Parameter-Efficient Continual Fine-Tuning: A Survey Continual learning for predictive maintenance: Overview and challenges

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Parameter-Efficient Continual Fine-Tuning: A Survey Editing models with task arithmetic, 2023

Reference 58

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Observation be0bef8b-fc22-4d20-a257-af9c71fcf9d3 · outbound

This paper cites Meta-learning representations for continual learning, 2019.

Parameter-Efficient Continual Fine-Tuning: A Survey Meta-learning representations for continual learning, 2019

Reference 59

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This paper cites Helpful or harmful: Inter-task associa- tion in continual learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Helpful or harmful: Inter-task associa- tion in continual learning

Reference 60

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Observation 51ca39db-af53-44bf-a992-018de272628d · outbound

This paper cites Meta-consolidation for con- tinual learning, 2020.

Parameter-Efficient Continual Fine-Tuning: A Survey Meta-consolidation for con- tinual learning, 2020

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Observation 8b224125-021c-4ae6-9097-ed77ea946130 · outbound

This paper cites Con- tinual learning with node-importance based adaptive group sparse regu- larization, 2021.

Parameter-Efficient Continual Fine-Tuning: A Survey Con- tinual learning with node-importance based adaptive group sparse regu- larization, 2021

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Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.252354Z

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Observation d48b7fb9-beb4-418c-958c-eb825063796e · outbound

This paper cites an unresolved cited work.

Parameter-Efficient Continual Fine-Tuning: A Survey Unresolved cited work

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.257089Z

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Observation 450ddc38-e9dd-4b64-b106-f4e81915c6a3 · outbound

This paper cites Class-Incremental Learn- ing by Knowledge Distillation with Adaptive Feature Consolidation.

Parameter-Efficient Continual Fine-Tuning: A Survey Class-Incremental Learn- ing by Knowledge Distillation with Adaptive Feature Consolidation

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Observation 218ae6f9-228e-4aef-be5e-b57de3538a3a · outbound

This paper cites Brown, Ben- jamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei.

Parameter-Efficient Continual Fine-Tuning: A Survey Brown, Ben- jamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.266011Z

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Observation 1e84ceb6-bbcd-4614-937f-3236e452be91 · outbound

This paper cites Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-16T12:01:31.069307Z

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Observation fdcfc724-229f-4149-a010-677adaae3cbd · outbound

This paper cites Introducing language guidance in prompt-based continual learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Introducing language guidance in prompt-based continual learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.275025Z

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Observation 322b0d2f-f675-4b00-b960-0dd79a6f67c4 · outbound

This paper cites On the stability-plasticity dilemma of class-incremental learning, 2023.

Parameter-Efficient Continual Fine-Tuning: A Survey On the stability-plasticity dilemma of class-incremental learning, 2023

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.279382Z

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Observation b720968f-da36-46b2-ac67-70749a781c5a · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

Parameter-Efficient Continual Fine-Tuning: A Survey Overcoming catastrophic forgetting in neural networks

Reference 69

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Observation b6ff5e79-9d9a-4d65-a502-6e089b3e480e · outbound

This paper cites Rusu, Kieran Milan, John Quan, Tiago Ra- malho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell.

Parameter-Efficient Continual Fine-Tuning: A Survey Rusu, Kieran Milan, John Quan, Tiago Ra- malho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.288213Z

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Observation 5bfc9d82-7da7-46d0-b6a7-ae33f2272e31 · outbound

This paper cites Kopiczko, Tijmen Blankevoort, and Yuki M.

Parameter-Efficient Continual Fine-Tuning: A Survey Kopiczko, Tijmen Blankevoort, and Yuki M

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Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.292578Z

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Observation be867aeb-9b4e-4009-93a4-98345837cc34 · outbound

This paper cites Hierarchical mo- tion understanding via motion programs.

Parameter-Efficient Continual Fine-Tuning: A Survey Hierarchical mo- tion understanding via motion programs

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.296883Z

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Observation c650891a-5db9-4afa-872f-11a8f0f56ef2 · outbound

This paper cites Overcoming catas- trophic forgetting with unlabeled data in the wild.

Parameter-Efficient Continual Fine-Tuning: A Survey Overcoming catas- trophic forgetting with unlabeled data in the wild

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.301496Z

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Observation 4c46448c-97b4-4762-840f-b4f81b1e71f6 · outbound

This paper cites Do pre-trained mod- els benefit equally in continual learning? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 6485–6493, 2023.

Parameter-Efficient Continual Fine-Tuning: A Survey Do pre-trained mod- els benefit equally in continual learning? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 6485–6493, 2023

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.306510Z

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Observation 6fb97376-de8a-40c2-9272-7b0900790464 · outbound

This paper cites Overcoming catastrophic forgetting by incremental moment matching.

Parameter-Efficient Continual Fine-Tuning: A Survey Overcoming catastrophic forgetting by incremental moment matching

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.311016Z

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Observation 9bfc115d-4eb2-4ba4-80a6-0886fa68ad97 · outbound

This paper cites Symbolic replay: Scene graph as prompt for continual learning on vqa task.

Parameter-Efficient Continual Fine-Tuning: A Survey Symbolic replay: Scene graph as prompt for continual learning on vqa task

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.315410Z

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Observation de31fddd-2fb7-48e3-9786-51f1aaab4a3d · outbound

This paper cites The power of scale for parameter-efficient prompt tuning, 2021.

Parameter-Efficient Continual Fine-Tuning: A Survey The power of scale for parameter-efficient prompt tuning, 2021

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Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.319903Z

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Observation d1131116-d8e0-4b0f-ac27-3b077ccbb17e · outbound

This paper cites Atlas: Adapter- based multi-modal continual learning with a two-stage learning strategy, 2024.

Parameter-Efficient Continual Fine-Tuning: A Survey Atlas: Adapter- based multi-modal continual learning with a two-stage learning strategy, 2024

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.324524Z

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Observation 9e5c4cc1-5c01-4b3f-ac1e-57184ab99cff · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation, 2021.

Parameter-Efficient Continual Fine-Tuning: A Survey Prefix-tuning: Optimizing continuous prompts for generation, 2021

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.329031Z

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Observation 9deea414-3c8c-4e32-a067-a5677f3fb5cd · outbound

This paper cites Vb-lora: Extreme parameter efficient fine-tuning with vector banks, 2024.

Parameter-Efficient Continual Fine-Tuning: A Survey Vb-lora: Extreme parameter efficient fine-tuning with vector banks, 2024

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.333448Z

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Observation 60ef683e-7a04-4173-bf12-f75f4c2dd604 · outbound

This paper cites Learning without forgetting.

Parameter-Efficient Continual Fine-Tuning: A Survey Learning without forgetting

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.337985Z

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Observation 2eb3fa92-affb-4eee-a136-b51d8943ca6b · outbound

This paper cites Inflora: Interference-free low-rank adap- tation for continual learning, 2024.

Parameter-Efficient Continual Fine-Tuning: A Survey Inflora: Interference-free low-rank adap- tation for continual learning, 2024

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.342302Z

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Observation 4deb9981-bf78-491b-af1d-b11271bff628 · outbound

This paper cites Trgp: Trust region gradient projection for continual learning, 2022.

Parameter-Efficient Continual Fine-Tuning: A Survey Trgp: Trust region gradient projection for continual learning, 2022

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.346746Z

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Observation 39cd3362-2df1-4f12-b746-f9bc52990984 · outbound

This paper cites The clear bench- mark: Continual learning on real-world imagery.

Parameter-Efficient Continual Fine-Tuning: A Survey The clear bench- mark: Continual learning on real-world imagery

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.350874Z

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Observation c60ac435-1361-40b9-b2d4-6ffe8ca550f9 · outbound

This paper cites Lora-based continual learning with constraints on critical parameter changes, 2025.

Parameter-Efficient Continual Fine-Tuning: A Survey Lora-based continual learning with constraints on critical parameter changes, 2025

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.355094Z

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Observation 25e67d42-2578-467a-be8b-c10358446c97 · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.359452Z

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Observation 63414cf8-3618-400b-bd86-966a892d08a1 · outbound

This paper cites Parameter-efficient fine-tuning for continual learn- ing: A neural tangent kernel perspective, 2025.

Parameter-Efficient Continual Fine-Tuning: A Survey Parameter-efficient fine-tuning for continual learn- ing: A neural tangent kernel perspective, 2025

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.363925Z

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Observation 0aedd2b1-b15c-461e-a31c-29aad9508ac9 · outbound

This paper cites Dora: Weight-decomposed low-rank adaptation, 2024.

Parameter-Efficient Continual Fine-Tuning: A Survey Dora: Weight-decomposed low-rank adaptation, 2024

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.368240Z

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Observation 43bd82d6-a3c8-4f38-ba3b-20a05ec0c618 · outbound

This paper cites Rotate your networks: Better weight consolidation and less catastrophic forgetting.

Parameter-Efficient Continual Fine-Tuning: A Survey Rotate your networks: Better weight consolidation and less catastrophic forgetting

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.372942Z

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Observation 1488816f-5a21-40b2-80ce-ce5f08ee6a6f · outbound

This paper cites P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks, 2022.

Parameter-Efficient Continual Fine-Tuning: A Survey P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks, 2022

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.377182Z

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Observation bb139cb7-267f-491b-bafa-3553f514a64f · outbound

This paper cites Gpt understands, too.

Parameter-Efficient Continual Fine-Tuning: A Survey Gpt understands, too

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.381506Z

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Observation 1406dff0-d1ce-4d5c-a043-79a085038c75 · outbound

This paper cites Adaptive aggregation net- works for class-incremental learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Adaptive aggregation net- works for class-incremental learning

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.385517Z

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Observation 218e1d65-d8e2-4aae-8df0-1a14886636a5 · outbound

This paper cites Rmm: Reinforced memory management for class-incremental learning, 2023.

Parameter-Efficient Continual Fine-Tuning: A Survey Rmm: Reinforced memory management for class-incremental learning, 2023

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.389817Z

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Observation 47583abd-7a0a-4040-a9b5-66441a51d0d8 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Parameter-Efficient Continual Fine-Tuning: A Survey RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.394102Z

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Observation dc9cd66c-ec37-4e35-9b74-45a7fb732097 · outbound

This paper cites Learning to describe scenes with programs.

Parameter-Efficient Continual Fine-Tuning: A Survey Learning to describe scenes with programs

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.398442Z

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Observation 67230e68-6cb6-4156-aa49-5af5f107ea33 · outbound

This paper cites Gradient episodic memory for continual learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Gradient episodic memory for continual learning

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.402816Z

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Observation ebb59362-1a8b-4d2d-8c54-f1252cb66d6d · outbound

This paper cites Gradient episodic memory for continual learning, 2022.

Parameter-Efficient Continual Fine-Tuning: A Survey Gradient episodic memory for continual learning, 2022

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.407679Z

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Observation 1d7d7702-d5b8-4736-91a4-7ec6766abe52 · outbound

This paper cites Visual prompt tuning in null space for contin- ual learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Visual prompt tuning in null space for contin- ual learning

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.411836Z

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Observation 10029647-c288-4013-8867-4cf15a2a5729 · outbound

This paper cites UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation.

Parameter-Efficient Continual Fine-Tuning: A Survey UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.416207Z

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Observation 0dd0c912-2588-44e0-b9b2-88173d1237b6 · outbound

This paper cites Language semantic graph guided data-efficient learning.

Parameter-Efficient Continual Fine-Tuning: A Survey Language semantic graph guided data-efficient learning

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.420884Z

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Observation 95e72d4b-65e0-4bf4-84dd-0541f5279278 · outbound

This paper cites Online continual learning in image classification: An em- pirical survey.

Parameter-Efficient Continual Fine-Tuning: A Survey Online continual learning in image classification: An em- pirical survey

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Resolution: unresolved; no resolver link, observed 2026-08-16T12:01:30.424989Z

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Pith citing papers

Observation fc5ff43f-3029-4360-8f34-5d2f638a3dbf · inbound

Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation cites this paper.

Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation Parameter-Efficient Continual Fine-Tuning: A Survey

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-22T01:23:30.189453Z

Source-reported events for the cited work

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Observation 94b7a491-dda5-4163-a5d8-1d6599ff2bed · inbound

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs cites this paper.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Parameter-Efficient Continual Fine-Tuning: A Survey

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:50:59.444442Z

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