Paper Citation Record · LEDGER

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning

As of 23 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2508.02978.

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

pith.paper-citation-record.v1
2508.02978 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:51:52.390069Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy32
unresolved10
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cbd238f6-0221-4ecc-be28-52996de6124f · outbound

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

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning MT- LoRA: Low-rank adaptation approach for efficient multi- task learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:59.011333Z

Source-reported events for the cited work

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Observation 689f34fc-d8fa-45aa-a146-9388ef3c2ab2 · outbound

This paper cites Multi-Task Learning with Deep Neural Networks: A Survey.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Multi-Task Learning with Deep Neural Networks: A Survey

Reference 2

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Observation ffd3a88c-e16b-43df-9f98-c04a83e52085 · outbound

This paper cites QLoRA: Efficient finetuning of quantized llms.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning QLoRA: Efficient finetuning of quantized llms

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:58.830205Z

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Observation 0342470e-cbfa-4ed2-8d1f-5419737eb2a4 · outbound

This paper cites BERT: Pre-training of deep bidirectional trans- formers for language understanding.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning BERT: Pre-training of deep bidirectional trans- formers for language understanding

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:58.614512Z

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Observation 99ad3ebf-cdce-4826-ab16-8abf8fbe161a · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:58.425082Z

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Observation d02a173c-fcff-400c-a402-54514778c786 · outbound

This paper cites Omnivore: A sin- gle model for many visual modalities.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Omnivore: A sin- gle model for many visual modalities

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:58.247567Z

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Observation 944b0a5e-96ad-41bf-82a7-62429a489a99 · outbound

This paper cites Parameter-efficient fine-tuning for large models: A comprehensive survey.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Parameter-efficient fine-tuning for large models: A comprehensive survey

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:58.027116Z

Source-reported events for the cited work

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Observation 2ef26e3c-2d04-4b9c-946f-e92ea82887cb · outbound

This paper cites Deep residual learning for image recognition.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Deep residual learning for image recognition

Reference 8

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Observation 42a52814-50b2-448b-a98b-176f61922dff · outbound

This paper cites Parameter-efficient transfer learning for NLP.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Parameter-efficient transfer learning for NLP

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:57.784891Z

Source-reported events for the cited work

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Observation 895a8b4f-1ea3-4c49-aa60-94fb9f8a5a69 · outbound

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

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning LoRA: Low-rank adaptation of large language models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:57.564498Z

Source-reported events for the cited work

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Observation a4e7779d-091c-46cb-baa4-09578933de03 · outbound

This paper cites Parameter-efficient Multi- task Fine-tuning for Transformers via Shared Hypernet- works.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Parameter-efficient Multi- task Fine-tuning for Transformers via Shared Hypernet- works

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:57.445917Z

Source-reported events for the cited work

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Observation 3ebb7966-4a42-4007-8800-963357463d57 · outbound

This paper cites The Kinetics Human Action Video Dataset.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning The Kinetics Human Action Video Dataset

Reference 12

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Observation 0ae3f84d-04e8-4d82-b79e-cc87081436ee · outbound

This paper cites Human action recognition and predic- tion: A survey.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Human action recognition and predic- tion: A survey

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:57.233032Z

Source-reported events for the cited work

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Observation 8e0b9f3c-f8d4-4b25-be0c-474751efeff9 · outbound

This paper cites Poggio, and Thomas Serre.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Poggio, and Thomas Serre

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:57.109339Z

Source-reported events for the cited work

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Observation 06097439-4ca9-4ffe-bf06-13a7e55dc89b · outbound

This paper cites Efficient multi-domain learning by covariance normalization.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Efficient multi-domain learning by covariance normalization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:56.900828Z

Source-reported events for the cited work

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Observation d9527c21-a666-4ef5-a40c-5f7b3bf4d57e · outbound

This paper cites REPAIR: Removing repre- sentation bias by dataset resampling.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning REPAIR: Removing repre- sentation bias by dataset resampling

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:56.739057Z

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Observation d008149c-7eeb-4489-a60b-2b20039252de · outbound

This paper cites RESOUND: To- wards Action Recognition Without Representation Bias.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning RESOUND: To- wards Action Recognition Without Representation Bias

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:56.551926Z

Source-reported events for the cited work

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Observation 72a628a0-c4e3-4115-94a5-9036d26ab035 · outbound

This paper cites Multi-dataset Training of Transformers for Robust Action Recognition.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Multi-dataset Training of Transformers for Robust Action Recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:56.358225Z

Source-reported events for the cited work

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Observation 17da5dcf-d249-4693-b3e8-36f54d58c123 · outbound

This paper cites PolyViT: Co-training vision transformers on images, videos and audio.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning PolyViT: Co-training vision transformers on images, videos and audio

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:56.242711Z

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Observation 6ba998cd-0026-49be-bd4c-51ab5a185b57 · outbound

This paper cites Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:56.046671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b3c93f7d-9cd9-48d6-af96-3dee9c373a4c · outbound

This paper cites Decoupled weight de- cay regularization.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Decoupled weight de- cay regularization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:55.856061Z

Source-reported events for the cited work

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Observation d7d4c076-73b3-4641-a8fd-5807771cc532 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Learning transferable visual models from natural language supervision

Reference 22

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Source-reported events for the cited work

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Observation 94ac5601-d187-4f77-a12f-d3a31c80969c · outbound

This paper cites Learning multiple visual domains with residual adapters.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Learning multiple visual domains with residual adapters

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:55.714391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2e7243bb-80a8-4033-b5fd-d59b7565e6c7 · outbound

This paper cites Efficient parametrization of multi-domain deep neural net- works.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Efficient parametrization of multi-domain deep neural net- works

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:55.556921Z

Source-reported events for the cited work

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Observation 8ab3cbd3-ad19-4fac-b752-58a335a66251 · outbound

This paper cites Imagenet-21k pretraining for the masses.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Imagenet-21k pretraining for the masses

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:55.378224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fe3e0498-2857-4d05-9b32-edb9ee97b96c · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning High-resolution image synthesis with latent diffusion models

Reference 26

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Observation 72e6dfcd-95a1-44d2-8c8c-d93207434f06 · outbound

This paper cites Johansen, Sergio Escalera, Kamal Nasrollahi, Thomas B.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Johansen, Sergio Escalera, Kamal Nasrollahi, Thomas B

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:55.232187Z

Source-reported events for the cited work

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Observation c8d4f36d-4a32-44b0-99ea-282810f56188 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 28

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Observation c8bcc86c-fea3-46e7-ab4d-2d4163821636 · outbound

This paper cites VL-Adapter: Parameter-efficient transfer learning for vision-and-language tasks.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning VL-Adapter: Parameter-efficient transfer learning for vision-and-language tasks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:55.075190Z

Source-reported events for the cited work

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Observation facd8571-3cd1-40d5-9d0d-90d5a828aa4c · outbound

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Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Unresolved cited work

Reference 30

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Observation bf98a876-2d0f-47ef-9c13-77dbf4196918 · outbound

This paper cites Vision Transformers for Action Recognition: A Survey.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Vision Transformers for Action Recognition: A Survey

Reference 31

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Observation f267c686-820a-4e38-9fe9-b328c82e9989 · outbound

This paper cites Multi-task learning for dense prediction tasks: A survey.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Multi-task learning for dense prediction tasks: A survey

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:54.682432Z

Source-reported events for the cited work

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Observation bd4d3c28-ac4f-4345-9ced-f163a46d39af · outbound

This paper cites Interpretable image recognition by constructing transparent embedding space.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Interpretable image recognition by constructing transparent embedding space

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:54.438471Z

Source-reported events for the cited work

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Observation 244523c9-a70a-41ec-b2d6-169024253c31 · outbound

This paper cites Mimetics: To- wards understanding human actions out of context.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Mimetics: To- wards understanding human actions out of context

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:54.260653Z

Source-reported events for the cited work

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Observation 6c79fc6c-5692-48db-a413-ec83758c0a2a · outbound

This paper cites Large Multimodal Agents: A Survey.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Large Multimodal Agents: A Survey

Reference 35

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Observation 9e1e3c63-9270-4f8e-879e-2b3ed80ca6cb · outbound

This paper cites VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:54.031724Z

Source-reported events for the cited work

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Observation 6b503157-d550-402c-b0e0-b66ecc803d41 · outbound

This paper cites Parameter-efficient fine- tuning for pre-trained vision models: A survey.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Parameter-efficient fine- tuning for pre-trained vision models: A survey

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:51.966116Z

Source-reported events for the cited work

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Observation 3296a260-b09c-41d7-a95e-2273c5a01e06 · outbound

This paper cites A survey of efficient fine- tuning methods for Vision-Language Models — Prompt and Adapter.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning A survey of efficient fine- tuning methods for Vision-Language Models — Prompt and Adapter

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:53.837288Z

Source-reported events for the cited work

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Observation 171c0a52-60cd-43ed-80e4-ee69401f85c8 · outbound

This paper cites an unresolved cited work.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-06T04:51:53.642647Z

Source-reported events for the cited work

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Observation a1056671-317b-48a9-a34a-efaa50c6c5d7 · outbound

This paper cites Tensors for Data Processing: Theory, Methods, and Applications.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Tensors for Data Processing: Theory, Methods, and Applications

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:53.450658Z

Source-reported events for the cited work

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Observation f9af3a86-9f77-4fde-b079-e14932a7953f · outbound

This paper cites LLaMA-adapter: Efficient fine-tuning of large language models with zero- initialized attention.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning LLaMA-adapter: Efficient fine-tuning of large language models with zero- initialized attention

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:53.314893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 65374403-ef63-4862-b601-21a777136b89 · outbound

This paper cites A survey on multi-task learning.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning A survey on multi-task learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:53.172115Z

Source-reported events for the cited work

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Observation 20890385-00f7-46fa-a2de-8973e4f1bf9c · outbound

This paper cites Sim- ple multi-dataset detection.

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning Sim- ple multi-dataset detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:52.979007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.