LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning | Paper Citation Record

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

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Expert gate: Lifelong learning with a network of experts

Reference 1

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Observation 6fd3ef03-bba8-4bdb-bddf-868670e11e75 · outbound

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

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Memory aware synapses: Learning what (not) to forget

Reference 2

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This paper cites Food-101 - mining discriminative components with random forests.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Food-101 - mining discriminative components with random forests

Reference 3

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This paper cites Sharegpt4v: Improving large multi-modal models with better captions.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Sharegpt4v: Improving large multi-modal models with better captions

Reference 4

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This paper cites Describing textures in the wild.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Describing textures in the wild

Reference 5

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Observation 324a7e5b-cc6f-4de9-9ec9-85ecb03ebff9 · outbound

This paper cites The MNIST database of handwritten digit images for machine learning research [best of the web].IEEE Signal Processing Magazine, 29, 2012.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning The MNIST database of handwritten digit images for machine learning research [best of the web].IEEE Signal Processing Magazine, 29, 2012

Reference 6

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Observation 5ef582b7-e63b-4786-8c8b-09e1cb679efa · outbound

This paper cites Learning without mem- orizing.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learning without mem- orizing

Reference 7

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

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 8

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Observation c8d57873-943a-4894-a9a8-65e062e68a02 · outbound

This paper cites Podnet: Pooled outputs distil- lation for small-tasks incremental learning.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Podnet: Pooled outputs distil- lation for small-tasks incremental learning

Reference 9

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Observation 0b217111-7bfb-4753-9ea4-8171fa230b19 · outbound

This paper cites Dytox: Transformers for continual learn- ing with dynamic token expansion.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Dytox: Transformers for continual learn- ing with dynamic token expansion

Reference 10

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Observation 1577edf1-a715-4c28-a786-90a2cda48e1b · outbound

This paper cites Scaling laws of synthetic images for model training.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Scaling laws of synthetic images for model training

Reference 11

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Observation c18e0157-591a-48df-9663-a8abe66b929f · outbound

This paper cites Learning gen- erative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learning gen- erative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 12

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Observation 9ef151ed-61fc-460a-96ad-32bd1edf5166 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Clip-adapter: Better vision-language models with feature adapters

Reference 13

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Observation c8506f8b-8e51-4eac-9b13-9dc3aaf3c4fc · outbound

This paper cites DDGR: continual learning with deep diffusion-based generative replay.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning DDGR: continual learning with deep diffusion-based generative replay

Reference 14

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Observation 32f364f8-0256-428d-9ac6-fe483baa48f8 · outbound

This paper cites Finetune like you pretrain: Im- proved finetuning of zero-shot vision models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Finetune like you pretrain: Im- proved finetuning of zero-shot vision models

Reference 15

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Observation a64db165-4b06-40ab-9da7-53b8db24df41 · outbound

This paper cites SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

Reference 16

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This paper cites EuroSAT: A novel dataset and deep learn- ing benchmark for land use and land cover classification.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning EuroSAT: A novel dataset and deep learn- ing benchmark for land use and land cover classification

Reference 17

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Observation 72776ca2-0d88-46b3-9bd7-2f07a0b42aae · outbound

This paper cites Feedback-guided data synthesis for imbalanced classifica- tion.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Feedback-guided data synthesis for imbalanced classifica- tion

Reference 18

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Observation 856e9deb-edee-4a5a-902c-a66534d1a353 · outbound

This paper cites Learning a unified classifier incrementally via rebalancing.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learning a unified classifier incrementally via rebalancing

Reference 19

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This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 20

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Observation 2c113551-971b-4d9d-a14b-85294427cf58 · outbound

This paper cites Dense network expansion for class in- cremental learning.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Dense network expansion for class in- cremental learning

Reference 21

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Observation 3dd389d7-4e46-4f77-95f2-713ec43ba291 · outbound

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

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning

Reference 22

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This paper cites Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig

Reference 23

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This paper cites Belongie, Bharath Hariharan, and Ser-Nam Lim.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Belongie, Bharath Hariharan, and Ser-Nam Lim

Reference 24

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This paper cites Class-incremental learning using diffusion model for distillation and replay.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Class-incremental learning using diffusion model for distillation and replay

Reference 25

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Observation 117c2913-6d52-4663-9bc4-aaed4ea791bc · outbound

This paper cites SDDGR: stable diffusion- based deep generative replay for class incremental object de- tection.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning SDDGR: stable diffusion- based deep generative replay for class incremental object de- tection

Reference 26

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Observation a2069725-81b2-47ba-a55d-9320e92613b9 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Overcoming catastrophic forgetting in neural networks

Reference 27

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This paper cites 3d object representations for fine-grained categorization.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning 3d object representations for fine-grained categorization

Reference 28

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

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learning multiple layers of features from tiny images

Reference 29

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Observation 0d94308c-8d96-4a93-83e1-da95f5c1497e · outbound

This paper cites Continual Classification Learning Using Generative Models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Continual Classification Learning Using Generative Models

Reference 30

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Observation 43a9a2fd-7585-407f-9d1b-74481df0a5bc · outbound

This paper cites Overcoming catastrophic forgetting by incremental moment matching.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Overcoming catastrophic forgetting by incremental moment matching

Reference 31

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This paper cites What If We Recaption Billions of Web Images with LLaMA-3?.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning What If We Recaption Billions of Web Images with LLaMA-3?

Reference 32

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Observation 2df8038a-0dbc-4edb-8560-756b12ae8124 · outbound

This paper cites Learning without forgetting.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learning without forgetting

Reference 33

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Observation 3da346e3-fa88-4f43-98b8-c57ac1a5e868 · outbound

This paper cites SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios

Reference 34

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This paper cites SynthVLM: Towards High-Quality and Efficient Synthesis of Image-Caption Datasets for Vision-Language Models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning SynthVLM: Towards High-Quality and Efficient Synthesis of Image-Caption Datasets for Vision-Language Models

Reference 35

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Observation 124c8f5d-7fa3-487b-a436-4041ae7b66c4 · outbound

This paper cites Gradient episodic memory for continual learning.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Gradient episodic memory for continual learning

Reference 36

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Observation 3c07401a-2939-4653-964f-42d76b5011a8 · outbound

This paper cites Decoupled weight decay regularization.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Decoupled weight decay regularization

Reference 37

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This paper cites Fine-Grained Visual Classification of Aircraft.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Fine-Grained Visual Classification of Aircraft

Reference 38

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This paper cites Diffclass: Diffusion-based class incremental learning.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Diffclass: Diffusion-based class incremental learning

Reference 39

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Observation 8f6b41f2-88fd-4d59-a2f0-6ba49e0aa1fb · outbound

This paper cites Improving multimodal datasets with image captioning.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Improving multimodal datasets with image captioning

Reference 40

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This paper cites Automated flower classification over a large number of classes.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Automated flower classification over a large number of classes

Reference 41

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This paper cites Parkhi, Andrea Vedaldi, Andrew Zisserman, and C.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Parkhi, Andrea Vedaldi, Andrew Zisserman, and C

Reference 42

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LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Unresolved cited work

Reference 43

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

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learning transferable visual models from natural language supervision

Reference 44

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LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Unresolved cited work

Reference 45

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Observation 02738c9b-c261-44f3-a3bf-a90dc36d775a · outbound

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

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning High-resolution image syn- thesis with latent diffusion models

Reference 46

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Observation 9baf47ff-08f1-4b30-890d-20557c5c4dcb · outbound

This paper cites Imagenet large scale visual recognition challenge.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Imagenet large scale visual recognition challenge

Reference 47

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Observation ed815fdb-309a-448f-8c91-594e38b795a9 · outbound

This paper cites Continual learning with deep generative replay.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Continual learning with deep generative replay

Reference 48

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Observation 27e1ce66-aa5c-45b0-96a3-c552b372bd2e · outbound

This paper cites Is synthetic data all we need? benchmarking the robustness of models trained with synthetic images.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Is synthetic data all we need? benchmarking the robustness of models trained with synthetic images

Reference 49

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Observation c398e235-75b9-48e2-bb0c-7aea10858343 · outbound

This paper cites StableRep: Synthetic images from text-to- image models make strong visual representation learners.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning StableRep: Synthetic images from text-to- image models make strong visual representation learners

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.266137Z

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Observation 3c86d73c-5999-4dbe-a7a3-53f57636bb98 · outbound

This paper cites Learning vision from models rivals learning vision from data.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learning vision from models rivals learning vision from data

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.245446Z

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Observation 50f66cd8-1e6b-4d19-90bf-c60ccdcdc588 · outbound

This paper cites S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.226946Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2540e778-982d-4e66-828d-d05790cb50fc · outbound

This paper cites Dy, and Tomas Pfister.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Dy, and Tomas Pfister

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.207526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 753247d8-4ae2-4c77-b798-4093bd302740 · outbound

This paper cites Dy, and Tomas Pfister.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Dy, and Tomas Pfister

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.188577Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8dcc18c0-98c1-454e-b6ea-c6189ddaeb56 · outbound

This paper cites Robust fine-tuning of zero-shot models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Robust fine-tuning of zero-shot models

Reference 55

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Observation 8ebd7431-77fc-405b-8664-65f10d92af55 · outbound

This paper cites Syn- thetic data is an elegant GIFT for continual vision-language models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Syn- thetic data is an elegant GIFT for continual vision-language models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.166201Z

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Observation ca3917d2-1d9c-4ea5-9cf6-b3f90a9a833a · outbound

This paper cites Ehinger, Aude Oliva, and Antonio Torralba.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Ehinger, Aude Oliva, and Antonio Torralba

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.397189Z

Source-reported events for the cited work

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Observation 5d853061-3916-4db2-b894-2dd23ad965c2 · outbound

This paper cites DER: dynam- ically expandable representation for class incremental learn- ing.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning DER: dynam- ically expandable representation for class incremental learn- ing

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.124562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f947eeb3-e44e-4d56-bca6-9e3c863d7d95 · outbound

This paper cites an unresolved cited work.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:26:36.100836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5ef6e0a5-ad4a-41d2-83e4-bdd5f9d72886 · outbound

This paper cites Controlled training data generation with diffusion mod- els.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Controlled training data generation with diffusion mod- els

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.079185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bcdf61bb-a8a0-4940-b5a3-4f30bfa7021f · outbound

This paper cites Lifelong learning with dynamically expandable net- works.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Lifelong learning with dynamically expandable net- works

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.059223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 393dc943-036d-4e33-bba2-ec070aa55fd2 · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Coca: Contrastive captioners are image-text foundation models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.038689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e9d2eb05-ad30-48e0-85e8-0a0bf417b3e8 · outbound

This paper cites Boosting continual learning of vision-language models via mixture-of-experts adapters.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Boosting continual learning of vision-language models via mixture-of-experts adapters

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:36.014985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 73786bac-8b93-49e8-b287-e930a7f00028 · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Florence: A New Foundation Model for Computer Vision

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.445457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86599f12-d084-45d7-9b64-6589340eba32 · outbound

This paper cites Contin- ual learning through synaptic intelligence.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Contin- ual learning through synaptic intelligence

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.992001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e7835c4-c6ae-4439-986c-c65e1cb446e5 · outbound

This paper cites Lit: Zero-shot transfer with locked-image text tuning.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Lit: Zero-shot transfer with locked-image text tuning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.963785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 85aa4f5b-11d5-47ad-a0ba-cff6a482250c · outbound

This paper cites Preventing zero-shot transfer degradation in continual learning of vision-language models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Preventing zero-shot transfer degradation in continual learning of vision-language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.939775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 01c01543-e306-4f2d-a47a-1a4d5567aaa0 · outbound

This paper cites Preventing zero-shot transfer degradation in continual learning of vision-language models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Preventing zero-shot transfer degradation in continual learning of vision-language models

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.913806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation defae5cf-7b45-4c8b-bd0a-e45b6d0a4e9f · outbound

This paper cites Learning with- out forgetting for vision-language models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learning with- out forgetting for vision-language models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.893970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a7220a1-a264-4bb4-80ca-cfc24c55bd69 · outbound

This paper cites Conditional prompt learning for vision-language models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Conditional prompt learning for vision-language models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.873832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e39ead18-e2bd-4384-b47c-ce70f9bd67ea · outbound

This paper cites Learning to prompt for vision-language models.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Learning to prompt for vision-language models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.851499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1430810a-ddf8-4bfb-a7d2-9d6ee8e816f6 · outbound

This paper cites 6 and Tab.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning 6 and Tab

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.829712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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