Paper Citation Record · LEDGER

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding

As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2508.04101.

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

pith.paper-citation-record.v1
2508.04101 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:56:08.484023Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 809e9a60-5582-478b-86bf-e66fd7503300 · outbound

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

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Learning to prompt for vision- language models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:13.069194Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5cfb7abc-5b8e-4b53-9340-4d8be072aac5 · outbound

This paper cites Fate: Feature-adapted parameter tuning for vision-language models,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Fate: Feature-adapted parameter tuning for vision-language models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:12.830774Z

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Observation ddb2d2ae-649a-411c-9400-05b2712d0997 · outbound

This paper cites Deco-net: Robust multimodal brain tumor segmentation via decoupled complementary knowledge distillation,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Deco-net: Robust multimodal brain tumor segmentation via decoupled complementary knowledge distillation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:12.597872Z

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Observation 73e64fd6-0278-41df-a7eb-d04db631bc77 · outbound

This paper cites Pm 2: A new prompting multi-modal model paradigm for few-shot medical image classification,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Pm 2: A new prompting multi-modal model paradigm for few-shot medical image classification,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:12.425598Z

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Observation ce0c5f42-918f-4085-9b5d-8796a4340a82 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmenta- tion,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Swin-unet: Unet-like pure transformer for medical image segmenta- tion,

Reference 5

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Source-reported events for the cited work

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Observation f0eab527-4dd3-411b-ae2b-906c9d1b6fbd · outbound

This paper cites Multi-modal masked autoencoders for medical vision-and-language pre-training,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Multi-modal masked autoencoders for medical vision-and-language pre-training,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:12.249828Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7fa09a98-f784-4e0d-9897-cb272dda3ebb · outbound

This paper cites Dm-gan: A data augmentation-based approach for imbalanced medical image classification,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Dm-gan: A data augmentation-based approach for imbalanced medical image classification,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:12.104187Z

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Observation e3ae655a-a5d2-4735-a7ba-751c3439447a · outbound

This paper cites V oco: A simple-yet-effective volume contrastive learning framework for 3d medical image analysis,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding V oco: A simple-yet-effective volume contrastive learning framework for 3d medical image analysis,

Reference 8

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Observation 1b9a3890-e479-4a3b-8d3c-709801624a96 · outbound

This paper cites Mim: Mask in mask self-supervised pre-training for 3d medical image analysis,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Mim: Mask in mask self-supervised pre-training for 3d medical image analysis,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:11.831360Z

Source-reported events for the cited work

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Observation 6fad662f-79f7-4c96-8a6e-5f40a84129ae · outbound

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

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Learning transferable visual models from natural language supervision,

Reference 10

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Source-reported events for the cited work

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Observation 616b7a8c-250b-4b7d-a1ab-29820aa7c454 · outbound

This paper cites Xcoop: Explainable prompt learning for computer-aided diagnosis via concept-guided context op- timization,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Xcoop: Explainable prompt learning for computer-aided diagnosis via concept-guided context op- timization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:11.638446Z

Source-reported events for the cited work

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Observation 74f7bc72-4c04-42a4-bb24-184b60d2c7cc · outbound

This paper cites Aligning medical images with general knowledge from large language models,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Aligning medical images with general knowledge from large language models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:11.528122Z

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Observation de616ddb-7212-4dc0-9809-a061ff36ed66 · outbound

This paper cites GPT-4 Technical Report.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding GPT-4 Technical Report

Reference 13

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Source-reported events for the cited work

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Observation 3c321d6b-dc6f-477a-92ff-45814887016d · outbound

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

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Conditional prompt learning for vision-language models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:11.432671Z

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Observation 757d7457-f5b1-4cb6-814e-3043a6f8cd32 · outbound

This paper cites Maple: Multi-modal prompt learning,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Maple: Multi-modal prompt learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:11.241889Z

Source-reported events for the cited work

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Observation 805fad7d-d49e-42e6-b5b2-d5102c69c684 · outbound

This paper cites ¨Uber die aufl ¨osung linearer gleichungen mit unendlich vielen unbekannten,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding ¨Uber die aufl ¨osung linearer gleichungen mit unendlich vielen unbekannten,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:11.058274Z

Source-reported events for the cited work

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Observation 92dd524a-43b8-4a01-8300-4fafad3ea196 · outbound

This paper cites Identifying medical diagnoses and treatable diseases by image-based deep learning,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Identifying medical diagnoses and treatable diseases by image-based deep learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:10.915768Z

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Observation 88d5e451-9a7e-4dff-b6e3-ef6817816d17 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Flamingo: a visual language model for few-shot learning,

Reference 18

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Observation be377ac6-616a-4310-9dbd-5f6396f347d4 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 19

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Observation e5456bac-4cd6-4bfd-b52a-11de5549afa1 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:07.096137Z

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Observation 453a5d59-121b-46f1-86f1-378d0308b363 · outbound

This paper cites Language is not all you need: Aligning perception with language models,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Language is not all you need: Aligning perception with language models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:10.699193Z

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Observation 1aca4d1a-0883-40a8-89c4-edaf9b2f3ed8 · outbound

This paper cites Open-vocabulary detr with conditional matching,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Open-vocabulary detr with conditional matching,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:10.519520Z

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Observation ba80456d-e1c2-4c7e-b203-5228a40702b6 · outbound

This paper cites Gridclip: One-stage object detection by grid-level clip representation learning,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Gridclip: One-stage object detection by grid-level clip representation learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:10.317498Z

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Observation 9c44029c-7323-417a-8a51-9938a76374b0 · outbound

This paper cites Taskclip: Extend large vision-language model for task oriented object detection,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Taskclip: Extend large vision-language model for task oriented object detection,

Reference 24

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Observation d3ead0ee-25ed-4519-8cbd-0330522ca1d5 · outbound

This paper cites Weakclip: Adapting clip for weakly-supervised semantic segmentation,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Weakclip: Adapting clip for weakly-supervised semantic segmentation,

Reference 25

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Observation ca396e57-2aa6-47e5-99ae-12b493725b11 · outbound

This paper cites Understanding fine-tuning clip for open-vocabulary semantic segmentation in hyperbolic space,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Understanding fine-tuning clip for open-vocabulary semantic segmentation in hyperbolic space,

Reference 26

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Observation bc1274d7-ae32-4120-8c46-d470b5e6be2c · outbound

This paper cites Parameter- efficient fine-tuning in hyperspherical space for open-vocabulary seman- tic segmentation,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Parameter- efficient fine-tuning in hyperspherical space for open-vocabulary seman- tic segmentation,

Reference 27

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Observation 2f81a09c-6a64-44fe-b98a-5cdd0d0c1513 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 28

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NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Visual-language prompt tuning with knowledge-guided context optimization,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:09.422395Z

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Observation a3889016-4d71-4478-ae47-a3540404afab · outbound

This paper cites Advancing Textual Prompt Learning with Anchored Attributes.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Advancing Textual Prompt Learning with Anchored Attributes

Reference 30

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Observation e2cfea43-25df-4577-8863-fc5386ab9eb3 · outbound

This paper cites Nlprompt: Noise-label prompt learning for vision-language models,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Nlprompt: Noise-label prompt learning for vision-language models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:09.250054Z

Source-reported events for the cited work

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Observation bd0a43ef-fbcd-40e7-a94b-edbccd0d9847 · outbound

This paper cites TextRefiner: Internal Visual Feature as Efficient Refiner for Vision-Language Models Prompt Tuning.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding TextRefiner: Internal Visual Feature as Efficient Refiner for Vision-Language Models Prompt Tuning

Reference 32

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Observation f4dd3646-cc78-4012-9e0d-a7aa31e9c839 · outbound

This paper cites Open access series of imaging studies (oasis): Cross- sectional mri data in young, middle aged, nondemented, and demented older adults,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Open access series of imaging studies (oasis): Cross- sectional mri data in young, middle aged, nondemented, and demented older adults,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:09.045712Z

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Observation 91a6fc93-49dc-462f-920b-62a835a0ac93 · outbound

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

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Lora: Low-rank adaptation of large language models

Reference 34

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Observation 3f401bdd-5bee-4baa-972c-db78c07a811c · outbound

This paper cites Principal component analysis,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Principal component analysis,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:08.790563Z

Source-reported events for the cited work

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Observation 3d580767-5ef8-427b-9aa1-1232d1570a22 · outbound

This paper cites Visualizing data using t-sne,.

NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding Visualizing data using t-sne,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:08.484023Z

Source-reported events for the cited work

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Pith citing papers

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