Paper Citation Record · LEDGER

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

As of 11 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2608.06901.

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

pith.paper-citation-record.v1
2608.06901 v1

Coverage vector

measured 48 of 48 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

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External citation measurements

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Outbound references

Observation d01fa85b-2d54-4fb6-a7d2-281c4995122e · outbound

This paper cites In: Pro- ceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Pro- ceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

Reference 1

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This paper cites In: International Conference on Learning Representations (ICLR) (2022).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: International Conference on Learning Representations (ICLR) (2022)

Reference 2

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This paper cites Proceedings of the IEEE international conference on computer vision.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Proceedings of the IEEE international conference on computer vision

Reference 3

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This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 4

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This paper cites Journal of Artificial Intel- ligence Research (JAIR)55, 409–442 (2016).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Journal of Artificial Intel- ligence Research (JAIR)55, 409–442 (2016)

Reference 5

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Unresolved cited work

Reference 6

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This paper cites Image-text Retrieval: A Survey on Recent Research and Development.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Image-text Retrieval: A Survey on Recent Research and Development

Reference 7

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This paper cites In: International Conference on Machine Learning (ICML) (2025).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: International Conference on Machine Learning (ICML) (2025)

Reference 8

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This paper cites In: CVPR (2009).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: CVPR (2009)

Reference 9

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This paper cites In: Explainable Artificial Intelligence, pp.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Explainable Artificial Intelligence, pp

Reference 10

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 11

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This paper cites In: ICML (2023).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: ICML (2023)

Reference 12

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This paper cites Interpreting CLIP's Image Representation via Text-Based Decomposition.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Interpreting CLIP's Image Representation via Text-Based Decomposition

Reference 13

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This paper cites NeurIPS (2015).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models NeurIPS (2015)

Reference 14

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Observation b8e96d7b-dd68-4a40-ab4f-13795e6d10a6 · outbound

This paper cites Advances in neural information processing systems28 (2015) PORTA 17.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Advances in neural information processing systems28 (2015) PORTA 17

Reference 15

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This paper cites In: 4th Lifelong Machine Learning Workshop at ICML (2020).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: 4th Lifelong Machine Learning Workshop at ICML (2020)

Reference 16

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This paper cites In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 17

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This paper cites In: International Conference on Learning Representations (ICLR) (2021).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: International Conference on Learning Representations (ICLR) (2021)

Reference 18

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This paper cites International Journal of Computer Vision123(1), 32–73 (2017).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models International Journal of Computer Vision123(1), 32–73 (2017)

Reference 19

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Unresolved cited work

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: ICLR (2021)

Reference 21

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This paper cites In: ICLR (2019).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: ICLR (2019)

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: ICML (2023)

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: ICML (2022)

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Text Summarization Branches Out

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: ECCV

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Unresolved cited work

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: NeurIPS (2022)

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Indian Conference on Computer Vision, Graphics & Image Processing (2008)

Reference 29

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics (2002)

Reference 30

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Proceedings of the IEEE international con- ference on computer vision

Reference 31

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This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: ICML (2021) 18 Kang et al

Reference 33

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: ICML (2023)

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models ICLR (2024)

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: International Conference on Learning Repre- sentations (ICLR) (2024)

Reference 36

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Advances in Neural In- formation Processing Systems (NeurIPS) (2020)

Reference 37

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: CVPR (2015)

Reference 38

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: International Conference on Learning Representations (ICLR) (2020)

Reference 39

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Forty-first International Conference on Machine Learning (2024)

Reference 40

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Proc

Reference 41

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This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 42

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Find- ings of the Association for Computational Linguistics: ACL 2023 (2023)

Reference 43

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This paper cites In: International Conference on Learning Representations (ICLR) (2023).

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: International Conference on Learning Representations (ICLR) (2023)

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:47:14.404013Z

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Observation c0ad3e8b-5dda-40d3-ab7d-12871c329209 · outbound

This paper cites On the Impact of Calibration Data in Post-training Quantization and Pruning.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models On the Impact of Calibration Data in Post-training Quantization and Pruning

Reference 45

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Reference 46

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Observation 5e1e48da-62d4-4e61-af21-c3241e6000ff · outbound

This paper cites Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Reference 47

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models silver zippers,

Reference 48

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Pith citing papers

No inbound Pith citation observations are available.