Paper Citation Record · LEDGER

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models

As of 21 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 4 inbound Pith citation observations for arXiv:2603.05950.

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

pith.paper-citation-record.v1
2603.05950 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T18:45:59.108312Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:51:16.303359Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T23:04:01.860140Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
verified fuzzy0
unresolved50
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cf9e99a0-5d13-45f3-ae7d-e06540dfed11 · outbound

This paper cites GPT-4 Technical Report.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models GPT-4 Technical Report

Reference 1

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Observation 89064253-6f59-4169-b841-7e12ac980067 · outbound

This paper cites Advances in neural information processing systems35, 23716– 23736 (2022).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Advances in neural information processing systems35, 23716– 23736 (2022)

Reference 2

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Observation 737e7fd8-18a0-48b8-84d0-7c6ee8a36fcb · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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Observation db7334e7-2a43-4b80-aabc-43555b41d59c · outbound

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

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: The Eleventh International Conference on Learning Representations (2023)

Reference 4

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Observation caee3828-128e-4f67-bd2b-caaf39f1e181 · outbound

This paper cites In: The Thirteenth International Conference on Learning Representations (2025).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: The Thirteenth International Conference on Learning Representations (2025)

Reference 5

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pat- tern recognition.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the IEEE/CVF conference on computer vision and pat- tern recognition

Reference 6

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Observation 031a7641-ca60-496b-96d3-f95ba606ce62 · outbound

This paper cites Advances in neural information pro- cessing systems37, 73986–74007 (2024).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Advances in neural information pro- cessing systems37, 73986–74007 (2024)

Reference 7

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Observation 2ae9ef64-9935-49b0-bd5b-0d5a512b1aeb · outbound

This paper cites In: European Conference on Computer Vision.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: European Conference on Computer Vision

Reference 8

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This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 9

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This paper cites Advances in neural information processing systems36, 49250–49267 (2023).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Advances in neural information processing systems36, 49250–49267 (2023)

Reference 10

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Observation 136ca392-b46a-455f-a8e1-1849a7b89ab5 · outbound

This paper cites In: Proceedings of the 60th Annual Meeting ofthe Associationfor Computational Linguistics (Volume 1: Long Papers).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the 60th Annual Meeting ofthe Associationfor Computational Linguistics (Volume 1: Long Papers)

Reference 11

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Observation 06a622f6-4bee-4ef6-b20e-78dfdb4966d4 · outbound

This paper cites In: The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2025).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2025)

Reference 12

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This paper cites SIAM review53(2), 217–288 (2011).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models SIAM review53(2), 217–288 (2011)

Reference 13

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Observation e51d978a-1939-449c-aeb7-8a6aa642e034 · outbound

This paper cites He et al.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models He et al

Reference 14

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This paper cites In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition

Reference 15

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Observation 8718a084-e66a-4fae-9638-2b895e403e2d · outbound

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

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Pro- ceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

Reference 16

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Observation 8b64c871-dbff-4c4b-baf2-5860863ef341 · outbound

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

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 17

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Observation 99c97fa7-1ccb-4c44-9798-330010bd7f50 · outbound

This paper cites In: International conference on machine learning.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: International conference on machine learning

Reference 18

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Observation ec0d5457-7463-4ed4-b9ff-039f5e55a94d · outbound

This paper cites In: The Thirteenth International Conference on Learning Representations (2025).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: The Thirteenth International Conference on Learning Representations (2025)

Reference 19

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Observation 45d059c6-e33e-440d-9bb5-37218d97ca41 · outbound

This paper cites In: Proceedings of the 2023 conference on empirical methods in natural language processing.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the 2023 conference on empirical methods in natural language processing

Reference 20

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Observation 99926049-bdbb-4d59-aa20-e4231d3562af · outbound

This paper cites A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges.

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges

Reference 21

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the AAAI Confer- ence on Artificial Intelligence

Reference 22

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This paper cites In: The Thirteenth International Con- ference on Learning Representations (2025).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: The Thirteenth International Con- ference on Learning Representations (2025)

Reference 23

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Unresolved cited work

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Observation 764ee4e0-db35-4c93-858d-694c4755edbb · outbound

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Thirty-seventh Conference on Neural Information Processing Systems (2023)

Reference 26

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Unresolved cited work

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This paper cites Advances in neural information processing systems35, 2507– 2521 (2022).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Advances in neural information processing systems35, 2507– 2521 (2022)

Reference 28

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: The Thirty-ninth Annual Conference on Neural Information Processing Sys- tems (2025)

Reference 29

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This paper cites Foundations and Trends®in Machine Learning3(2), 123–224 (2011).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Foundations and Trends®in Machine Learning3(2), 123–224 (2011)

Reference 30

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Observation bcbba7f6-d38c-4206-9506-7878dcbec738 · outbound

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Unresolved cited work

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 32

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

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This paper cites Advances in neural information pro- cessing systems30(2017).

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Advances in neural information pro- cessing systems30(2017)

Reference 35

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Observation c0c7c5ee-86de-481d-b890-4434ea4d2150 · outbound

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

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025)

Reference 39

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceed- ings of the IEEE/CVF International Conference on Computer Vision

Reference 40

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models Authorea Preprints (2025)

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the Computer Vision and Pattern Recognition Conference

Reference 43

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 44

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models IEEE transactions on pattern analysis and machine intelligence46(8), 5625–5644 (2024)

Reference 45

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 47

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: Forty-second International Conference on Machine Learning (2025)

Reference 48

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models In: The Twelfth International Conference on Learning Representations (2024)

Reference 49

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Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models highlighted tokens

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Pith citing papers

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FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching cites this paper.

FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models

Reference 10

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Observation c8d63ad2-fb6b-4485-9345-34bb3353ab34 · inbound

Toward Native Multimodal Modeling: A Roadmap cites this paper.

Toward Native Multimodal Modeling: A Roadmap Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models

Reference 260

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Observation 78282162-7d68-4e52-8bd3-e48a6164f750 · inbound

PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding cites this paper.

PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models

Reference 44

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Observation 92c823a5-1470-42e2-8e75-c50bdc24a828 · inbound

A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy cites this paper.

A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models

Reference 13

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