Paper Citation Record · LEDGER

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge

As of 21 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2506.16673.

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

pith.paper-citation-record.v1
2506.16673 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

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measured 49 of 49 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-11T03:31:33.964471Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T03:40:54.141854Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation bb0d8947-a5cd-476a-a08b-6a3b270f23f1 · outbound

This paper cites Layer Normalization.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Layer Normalization

Reference 1

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Observation 1210daea-a2a6-4b2a-8591-31dd5b9ec0f7 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Distilling the Knowledge in a Neural Network

Reference 11

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Observation 8d20089f-b83f-4d4d-af8c-0e25addf1014 · outbound

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

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Scaling up visual and vision-language representation learning with noisy text su- pervision

Reference 13

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This paper cites Learning multiple layers of features from tiny im- ages.Handbook of Systemic Autoimmune Diseases, 1(4),.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Learning multiple layers of features from tiny im- ages.Handbook of Systemic Autoimmune Diseases, 1(4),

Reference 14

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This paper cites Clipath: Fine-tune clip with visual fea- ture fusion for pathology image analysis towards min- imizing data collection efforts.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Clipath: Fine-tune clip with visual fea- ture fusion for pathology image analysis towards min- imizing data collection efforts

Reference 15

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Observation 9f089dd5-871e-45fd-8274-8df9a9fc5a1f · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 16

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This paper cites Align before fuse: Vision and language representation learning with momentum distilla- tion.Advances in neural information processing systems, 34:9694–9705,.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Align before fuse: Vision and language representation learning with momentum distilla- tion.Advances in neural information processing systems, 34:9694–9705,

Reference 17

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This paper cites Blip-2: Bootstrapping language-image pre- training with frozen image encoders and large language models.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Blip-2: Bootstrapping language-image pre- training with frozen image encoders and large language models

Reference 18

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This paper cites Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics

Reference 19

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Observation e4239e0a-3066-4fb3-9eaf-840639acf3d5 · outbound

This paper cites FoldGPT: Simple and Effective Large Language Model Compression Scheme.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge FoldGPT: Simple and Effective Large Language Model Compression Scheme

Reference 21

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Observation 78e710c1-ceb4-43b7-a2bc-32a33ff584d7 · outbound

This paper cites Clip-branches: Interactive fine-tuning for text- image retrieval.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Clip-branches: Interactive fine-tuning for text- image retrieval

Reference 22

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Observation 59700003-ea25-4573-9220-5fa415e5f49c · outbound

This paper cites ClipCap: CLIP Prefix for Image Captioning.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge ClipCap: CLIP Prefix for Image Captioning

Reference 23

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This paper cites Compact language models via pruning and knowledge distillation.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Compact language models via pruning and knowledge distillation

Reference 24

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This paper cites CHiLS: Zero- shot image classification with hierarchical label sets.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge CHiLS: Zero- shot image classification with hierarchical label sets

Reference 25

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Unresolved cited work

Reference 26

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Observation 1c4ace28-b9e0-45ec-a7f1-0dbdb30aa7ca · outbound

This paper cites Language models are unsupervised multitask learners.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Language models are unsupervised multitask learners

Reference 29

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This paper cites Learning transferable visual models from nat- ural language supervision.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Learning transferable visual models from nat- ural language supervision

Reference 30

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This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 31

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This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 33

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This paper cites Characterizing and avoid- ing negative transfer.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Characterizing and avoid- ing negative transfer

Reference 35

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Observation 8b136c55-2dc0-455d-87f1-4f9dd86c18f7 · outbound

This paper cites Learngene: From open-world to your learning task.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Learngene: From open-world to your learning task

Reference 36

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This paper cites Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models

Reference 37

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This paper cites Vision transformers as probabilistic expan- sion from learngene.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Vision transformers as probabilistic expan- sion from learngene

Reference 38

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This paper cites Exploring Learngene via Stage-wise Weight Sharing for Initializing Variable-sized Models.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Exploring Learngene via Stage-wise Weight Sharing for Initializing Variable-sized Models

Reference 39

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge KIND: Knowledge Integration and Diversion for Training Decomposable Models

Reference 40

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Observation ceb347dc-1b90-4c89-aef2-57be930a8a3b · outbound

This paper cites CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination

Reference 41

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Unresolved cited work

Reference 42

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Sigmoid loss for language image pre-training

Reference 43

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Minivit: Compressing vision transformers with weight multiplexing

Reference 44

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This paper cites CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs

Reference 45

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Learning clip guided visual- text fusion transformer for video-based pedestrian attribute recognition

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:45.302679Z

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge 8 with the loss weightλ set to1

Reference 47

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Cats and dogs

Reference 2001

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Microsoft coco: Com- mon objects in context

Reference 2004

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Bert: Pre-training of deep bidirectional transformers for language understand- ing

Reference 2009

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Clipping: Distilling clip-based models with a stu- dent base for video-language retrieval

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:46.673029Z

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:46.839841Z

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Openclip, July

Reference 2015

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Observation 0f226c26-7bbe-4ff2-a102-1d8c3ee06c94 · outbound

This paper cites Vlmo: Unified vision-language pre-training with mixture-of-modality- experts.Advances in Neural Information Processing Sys- tems, 35:32897–32912,.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Vlmo: Unified vision-language pre-training with mixture-of-modality- experts.Advances in Neural Information Processing Sys- tems, 35:32897–32912,

Reference 2016

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This paper cites Lawrence Zitnick, and Devi Parikh.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Lawrence Zitnick, and Devi Parikh

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:46.616326Z

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Observation 0da41141-771f-40f8-aeda-fcc84f6ac6df · outbound

This paper cites Building variable- sized models via learngene pool.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Building variable- sized models via learngene pool

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:46.637519Z

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Observation 73c67a20-8c1a-40ae-bdf5-6971a45b3686 · outbound

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

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2019

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Transferring Core Knowledge via Learngenes

Reference 2020

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Observation 59e47a0f-3e1a-43cd-b200-b7144e282dc5 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Reproducible scaling laws for contrastive language-image learning

Reference 2021

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Food-101–mining discriminative com- ponents with random forests

Reference 2022

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge Imagenet: A large-scale hierarchical image database

Reference 2023

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Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models

Reference 2024

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Pith citing papers

Observation cddb2aaa-c23f-4a4c-ae77-8b645044def7 · inbound

Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions cites this paper.

Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge

Reference 28

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Observation ce73f04e-e77b-4e33-a86e-6f8d47c2ade2 · inbound

Chain-based Distillation for Effective Initialization of Variable-Sized Small Language Models cites this paper.

Chain-based Distillation for Effective Initialization of Variable-Sized Small Language Models Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:40:54.143600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.