Paper Citation Record · LEDGER

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning

As of 5 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2505.12318.

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

pith.paper-citation-record.v1
2505.12318 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T13:54:38.393967Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T10:19:16.463961Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-30T12:04:39.205624Z

Reference resolution

57 of 57 outbound references displayed

verified exact17
verified fuzzy40
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ad45260-c7c2-4e7e-9016-6abf0f1e8f47 · outbound

This paper cites Pre-trained models: Past, present and future.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Pre-trained models: Past, present and future

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.211766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 01120017-1fb3-45be-a7e0-b8e7e5ffa7bc · outbound

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

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.145485Z

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Observation 675f626d-ba0d-40a8-8de6-29a9c89129f0 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:54:52.934051Z

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Observation ecb03750-f1b8-4336-b4e8-169969b6c538 · outbound

This paper cites FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre- trained Language Models.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre- trained Language Models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.181479Z

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Observation bb987e81-c53b-4d64-93b4-144cfedd0618 · outbound

This paper cites Towards building the federatedgpt: Federated instruction tuning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Towards building the federatedgpt: Federated instruction tuning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.098984Z

Source-reported events for the cited work

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Observation e3ac6733-7050-47a2-a9b5-3eb70fdc5a95 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Lora: Low-rank adaptation of large language models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.153064Z

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Observation 0d2d4c00-cd96-49ae-9fa7-82c0d313bdf7 · outbound

This paper cites SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.187242Z

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Observation 9dc7436c-434b-4e80-8561-a05a99856386 · outbound

This paper cites A comprehen- sive survey of continual learning: theory, method and application.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning A comprehen- sive survey of continual learning: theory, method and application

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.153732Z

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Observation 8cb0cfd8-ceba-4275-aabc-6120e0cffe18 · outbound

This paper cites Ode: An online data selection framework for federated learning with limited storage.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Ode: An online data selection framework for federated learning with limited storage

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.196292Z

Source-reported events for the cited work

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Observation b4856ff5-b6bf-4f2c-90d9-bfcdce5b62a6 · outbound

This paper cites A ug fl: Augmenting federated learning with pretrained models.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning A ug fl: Augmenting federated learning with pretrained models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.198857Z

Source-reported events for the cited work

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Observation bdb48a5f-7d92-4068-8362-8b2ff734a937 · outbound

This paper cites Class-incremental learning: A survey.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Class-incremental learning: A survey

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.144372Z

Source-reported events for the cited work

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Observation b243f813-bb13-495a-9da8-f5d389cb33f8 · outbound

This paper cites Federated Class-Incremental Learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Federated Class-Incremental Learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.162731Z

Source-reported events for the cited work

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Observation 33a30714-9a21-48b6-b759-4d4fa4d31336 · outbound

This paper cites Federated Continual Learning for Edge-AI: A Comprehensive Survey.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.922620Z

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Observation aad7f318-e963-446e-ab34-b30c33d4d71c · outbound

This paper cites Improving LoRA in Privacy-preserving Federated Learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Improving LoRA in Privacy-preserving Federated Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.979661Z

Source-reported events for the cited work

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Observation 9bc4ca67-8f20-4ac5-bea1-d07e7fc47c03 · outbound

This paper cites Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.942994Z

Source-reported events for the cited work

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Observation d13fcfda-b2cd-49f7-a1ed-48c6f3ddb317 · outbound

This paper cites Continual Adaptation of Vision Transformers for Federated Learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Continual Adaptation of Vision Transformers for Federated Learning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.938054Z

Source-reported events for the cited work

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Observation b23a07cc-232f-4bb2-9a04-b4c6c694bbe2 · outbound

This paper cites FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.204772Z

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Observation 0a0e2354-932d-4ea1-97d9-a1e54ba53eb9 · outbound

This paper cites Pilora: Prototype guided incremental lora for federated class-incremental learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Pilora: Prototype guided incremental lora for federated class-incremental learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.199562Z

Source-reported events for the cited work

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Observation 75f58770-267e-44c6-9325-b804391f8198 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Communication-efficient learning of deep networks from decentralized data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.202125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 38d930cf-1a70-45c0-80d7-b5c7488852dd · outbound

This paper cites FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.916473Z

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Observation 6da4d65d-5c10-43bf-9185-a8527996d4d1 · outbound

This paper cites FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.910543Z

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Observation eac5458c-6c84-4142-8a8f-6a0d78ce0703 · outbound

This paper cites Flora: Low-Rank Adapters Are Secretly Gradient Compressors.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Flora: Low-Rank Adapters Are Secretly Gradient Compressors

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.949057Z

Source-reported events for the cited work

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Observation 91306d68-11f7-4b4d-be96-c06e0714cad3 · outbound

This paper cites Fedex-lora: Exact aggregation for federated and efficient fine-tuning of large language models.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Fedex-lora: Exact aggregation for federated and efficient fine-tuning of large language models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.137144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dcf9bead-d147-4dd1-859e-dabcd936cd67 · outbound

This paper cites No One Left Behind: Real-World Federated Class-Incremental Learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning No One Left Behind: Real-World Federated Class-Incremental Learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.187452Z

Source-reported events for the cited work

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Observation 7f62b513-7482-422f-a0b9-3266459451ec · outbound

This paper cites Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.954222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 554e8e63-dd53-477a-8a37-bb27d258116b · outbound

This paper cites TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.193593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9e578274-6979-4179-9406-4681324c3240 · outbound

This paper cites Fedprok: Trustworthy federated class-incremental learning via pro- totypical feature knowledge transfer.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Fedprok: Trustworthy federated class-incremental learning via pro- totypical feature knowledge transfer

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.208978Z

Source-reported events for the cited work

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Observation 4115181c-281c-453f-ba1f-1f13514e21e7 · outbound

This paper cites Closed-form merging of parameter-efficient modules for Federated Continual Learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Closed-form merging of parameter-efficient modules for Federated Continual Learning

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.959843Z

Source-reported events for the cited work

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Observation ba1012c4-93a4-46f9-9b55-6a3103d91fee · outbound

This paper cites pfedmxf: Personalized federated class- incremental learning with mixture of frequency aggrega- tion.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning pfedmxf: Personalized federated class- incremental learning with mixture of frequency aggrega- tion

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.170562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c335d0fa-ba06-4baf-b63a-4d88dacd76f7 · outbound

This paper cites Parameter-Efficient Fine-Tuning without Introducing New Latency.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Parameter-Efficient Fine-Tuning without Introducing New Latency

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.159677Z

Source-reported events for the cited work

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Observation 236e3c3e-bcd8-4df8-800a-23d10dd961c3 · outbound

This paper cites Sd-lora: Scalable decou- pled low-rank adaptation for class incremental learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Sd-lora: Scalable decou- pled low-rank adaptation for class incremental learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.216598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8068d97e-7c57-4b08-a4b4-7ff8a0a8d70a · outbound

This paper cites SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.922589Z

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Observation 0018d204-82ce-4fef-bf4f-58f64a9e9136 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 33

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Observation 48039c74-85c0-4f8f-8894-cf23e48c4bea · outbound

This paper cites A Note on LoRA.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning A Note on LoRA

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.933069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f5e5d2a4-eb05-4835-a4b3-c4c0e5c0e62c · outbound

This paper cites Tracking meets lora: Faster training, larger model, stronger performance.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Tracking meets lora: Faster training, larger model, stronger performance

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.214228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8a0f8cdb-bc35-431d-a664-6af4e801b287 · outbound

This paper cites Mtlora: Low-rank adaptation approach for efficient multi-task learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Mtlora: Low-rank adaptation approach for efficient multi-task learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.166140Z

Source-reported events for the cited work

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Observation 961133bf-f1f4-4ddb-b4df-e7d9ee4d9aae · outbound

This paper cites Federated optimization in heterogeneous networks.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Federated optimization in heterogeneous networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.184328Z

Source-reported events for the cited work

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Observation e29f80ae-904a-4256-885a-b7e31565a2c3 · outbound

This paper cites Tighter theory for local sgd on identical and heterogeneous data.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Tighter theory for local sgd on identical and heterogeneous data

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.174870Z

Source-reported events for the cited work

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Observation c7c630e2-f41e-45a3-8814-aa75d6a6272b · outbound

This paper cites Personalized federated learning with theoretical guarantees: A model- agnostic meta-learning approach.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Personalized federated learning with theoretical guarantees: A model- agnostic meta-learning approach

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.112264Z

Source-reported events for the cited work

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Observation f0bb75bc-67ed-4b05-a70e-5f055b29d2d9 · outbound

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

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Learning multiple layers of features from tiny images

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.191447Z

Source-reported events for the cited work

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Observation 979d08ff-6d52-4d98-87ca-0967fa18bb6d · outbound

This paper cites Tiny imagenet visual recognition challenge.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Tiny imagenet visual recognition challenge

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.197140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b65cf3f0-99d1-422b-8a6b-9a87e3af32bd · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Imagenet: A large-scale hierarchical image database

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.210510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 01310030-d464-4cf8-80e1-8e5f9d5ec4d4 · outbound

This paper cites Distilling causal effect of data in class-incremental learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Distilling causal effect of data in class-incremental learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.219780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 39438782-752a-4b50-949d-31a515d9fc57 · outbound

This paper cites Py- CIL: A Python Toolbox for Class-Incremental Learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Py- CIL: A Python Toolbox for Class-Incremental Learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.178754Z

Source-reported events for the cited work

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Observation acea887c-ee5b-48ee-84cb-ed0544a467ce · outbound

This paper cites Federated Learning on Non-IID Data Silos: An Experimental Study.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Federated Learning on Non-IID Data Silos: An Experimental Study

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.177916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ac416ecd-0229-415b-832f-4cdd0e44e7b7 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Overcoming catastrophic forgetting in neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.194675Z

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Observation d22f7ad6-7f5b-41d3-8cf3-fcae58a46a3b · outbound

This paper cites Learning without Forgetting.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Learning without Forgetting

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.163262Z

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Observation 09f6cfc0-d776-4d74-8641-3c4ebf3d564c · outbound

This paper cites ICaRL: Incremental classifier and representation learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning ICaRL: Incremental classifier and representation learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.170019Z

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Observation 43631103-ea87-4820-8d75-4730a7501b9b · outbound

This paper cites Learning to Prompt for Continual Learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Learning to Prompt for Continual Learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.148265Z

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Observation 4375fc16-76d8-43d0-8dd7-232757c3f84f · outbound

This paper cites Inflora: Interference-free low- rank adaptation for continual learning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Inflora: Interference-free low- rank adaptation for continual learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.207904Z

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Observation 5d08be97-0652-4855-b3f4-772c8956e82b · outbound

This paper cites Guiding the last layer in federated learning with pre-trained models.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Guiding the last layer in federated learning with pre-trained models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.135412Z

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Observation e03c6a6e-254f-4d70-872d-b49c8cda454f · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Pytorch: An imperative style, high-performance deep learning library

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.147737Z

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Observation a069e85d-af99-487c-9415-d687ab5353db · outbound

This paper cites Emerging Proper- ties in Self-Supervised Vision Transformers.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Emerging Proper- ties in Self-Supervised Vision Transformers

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:54:53.156389Z

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Observation 064d4aa9-d317-4589-b4d0-5b971637e53a · outbound

This paper cites What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.974868Z

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Observation f62cf2ff-7cbf-49f0-97ca-0d85bccdeaca · outbound

This paper cites Surgical Fine-Tuning Improves Adaptation to Distribution Shifts.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.893045Z

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Observation 16e11aef-77df-4871-b768-91d5fc9014cd · outbound

This paper cites Federated Learning with Non-IID Data.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Federated Learning with Non-IID Data

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:54:52.964923Z

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Observation 70fa2f81-9116-4d65-837d-cdf00cfd781a · outbound

This paper cites Asymmetry in Low-Rank Adapters of Foundation Models.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Asymmetry in Low-Rank Adapters of Foundation Models

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.983692Z

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Pith citing papers

Observation 4f7004df-0cb1-4f32-a926-7b14a79f13cb · inbound

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning cites this paper.

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning

Reference 66

Resolution: metadata mismatch; local_arxiv, observed 2026-06-30T12:04:39.207091Z

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