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Paper Citation Record · LEDGER

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs

As of 8 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2608.01979.

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

pith.paper-citation-record.v1
2608.01979 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:23:16.335985Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

52 of 52 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved52
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f9e7c20-679b-4990-acc4-9dfa85b50310 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 1

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source=arxiv_source observed=2026-08-04T17:23:16.208825Z digest=sha256:09901434c806e35dd528a26902eec4f13cf0b0cace09d4593e752f510398dc15

Observation 902236a9-bb7f-4284-b18b-dd8197834550 · outbound

This paper cites Scene Text Visual Question Answering , booktitle =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Scene Text Visual Question Answering , booktitle =

Reference 2

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source=arxiv_source observed=2026-08-04T17:23:16.213246Z digest=sha256:b06035a4cdad4544ac89faa48a5599f3184c64dc2c305c377ffe10bb3d45f0a5

Observation d58060be-1622-4ec4-9d08-57a113189909 · outbound

This paper cites 2024 , howpublished =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs 2024 , howpublished =

Reference 3

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source=arxiv_source observed=2026-08-04T17:23:16.215998Z digest=sha256:90ff1b8e17cade36035338a1814fa19729332a36ed507db8221c0fa5f6ca0292

Observation 4a9290b9-0fb4-441d-9ce0-c111292a5b09 · outbound

This paper cites 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages =

Reference 4

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source=arxiv_source observed=2026-08-04T17:23:16.218563Z digest=sha256:59e14a973603648b312275ab91ae0146add7d983e01cd02e697c5e99063977da

Observation f71cb57c-2418-4be8-bd2f-f9b793d1fa09 · outbound

This paper cites an unresolved cited work.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-04T17:23:16.221145Z digest=sha256:39ef82fd4a378f652b55e246390d7c3fa33493b02e1cf560bdb51f5d61c26aed

Observation 00536d71-db08-43c7-b9c9-9d97b1c892fa · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 6

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source=arxiv_source observed=2026-08-04T17:23:16.223733Z digest=sha256:bb0ba8aab619a95602f718d3f14658cd9ac8d56cf00058af20b2f1330d0f7b02

Observation f6b5f369-d12e-4297-b3e7-68f9c8070272 · outbound

This paper cites G$^2$TR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs G$^2$TR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models

Reference 7

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source=arxiv_source observed=2026-08-04T17:23:16.226474Z digest=sha256:b2a7cfa2baa85af03be24646565d1dfd418c431dc010a54c1810c8a4aeb6668f

Observation 009147b5-26c1-4b3b-a45b-a792df01a27e · outbound

This paper cites Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages =

Reference 8

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source=arxiv_source observed=2026-08-04T17:23:16.229295Z digest=sha256:6b27fe2e745be29a691c2c2422a0e32f09a8645bd8b0ccb9b763049cf41b0080

Observation 101befc7-c6f3-4d30-ac98-2129812b6496 · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2022 , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Findings of the Association for Computational Linguistics: ACL 2022 , pages =

Reference 9

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source=arxiv_source observed=2026-08-04T17:23:16.231673Z digest=sha256:85116168e633ead7400297526b4f2a0b0e85898c33f32f21d7a8f6a210618974

Observation 37191fb7-bd59-421d-8d64-0cfcecf53cc3 · outbound

This paper cites Computer Vision -- ECCV 2020 , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Computer Vision -- ECCV 2020 , pages =

Reference 10

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source=arxiv_source observed=2026-08-04T17:23:16.234091Z digest=sha256:5323bc9c2b4b469958c6aa92ba69cb0195ad16149dbc478fcf62edfa4d43f7ca

Observation 80f70ecc-109d-4d29-992a-4f3f815c1239 · outbound

This paper cites Science China Information Sciences , volume =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Science China Information Sciences , volume =

Reference 11

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source=arxiv_source observed=2026-08-04T17:23:16.236431Z digest=sha256:f683006d2ff25847f32ee925062847f22d988deb0c3de624d7af15cbc5055356

Observation e7b92d26-7f26-4394-a719-9e30eebff866 · outbound

This paper cites OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning

Reference 12

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source=arxiv_source observed=2026-08-04T17:23:16.238872Z digest=sha256:13e00d49837cf069b5ae413b9e8b97acca3f9b7562bdc827114ccb37126174c2

Observation b96a303f-5579-4ef9-8d47-82a7f6e9de61 · outbound

This paper cites Computer Vision -- ECCV 2024 , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Computer Vision -- ECCV 2024 , pages =

Reference 13

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source=arxiv_source observed=2026-08-04T17:23:16.241772Z digest=sha256:5d7fd1e68bc85d02a9ca32f7737af240122573ecb36f9b182301d7f8d97f4266

Observation af204b94-4cd4-4e3f-91de-fc9cdbedc8e3 · outbound

This paper cites LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

Reference 14

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source=arxiv_source observed=2026-08-04T17:23:16.244130Z digest=sha256:a283aa93d5792075ad473673613ef8bcbd0cf7a25df41fbbe86e7a7916f9b095

Observation 0ec51c70-979c-4de8-8738-41e0dc52ca0a · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2023 , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Findings of the Association for Computational Linguistics: EMNLP 2023 , pages =

Reference 15

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source=arxiv_source observed=2026-08-04T17:23:16.246873Z digest=sha256:053bdbc28577ce9ce56e39f5c8f28b486b2fc60b3ea7d4dfea99c15a4f664df7

Observation 9d757787-0fac-4949-865e-d84af149f26f · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2024 , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Findings of the Association for Computational Linguistics: EMNLP 2024 , pages =

Reference 16

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source=arxiv_source observed=2026-08-04T17:23:16.249278Z digest=sha256:33b5d3c1ef5ab09e3031509431c1d86aadf47e178945d9d695f54a57f986059b

Observation 1057ed13-8c63-4fe5-bc3e-5a8c6f9bc3f3 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 17

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source=arxiv_source observed=2026-08-04T17:23:16.251674Z digest=sha256:9f2e52877e4fe3df61b656a216a5c1507c0277b6328353d7f4e4807205111518

Observation b6c0bb62-f2d4-4b3f-9b37-ce1c836ab4cb · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the AAAI Conference on Artificial Intelligence , volume =

Reference 18

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source=arxiv_source observed=2026-08-04T17:23:16.254058Z digest=sha256:ef3685b67f2b70fbd7000cbc68a681844d2df78b40c2c0f2b0c6d47ebdbe9bc9

Observation a0c06f8c-e487-4dc6-a8de-b898dad33249 · outbound

This paper cites Qwen3-VL Technical Report.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Qwen3-VL Technical Report

Reference 19

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source=arxiv_source observed=2026-08-04T17:23:16.256270Z digest=sha256:f98c4dd6a2487ef77d4ea4402fd991762a31110410640723c5fc3eb1746b7da8

Observation 7b19cb67-6e49-4047-b027-beb260698c80 · outbound

This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Reference 20

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source=arxiv_source observed=2026-08-04T17:23:16.259245Z digest=sha256:50b08b75939c771b725a04fc1fdd2a4f67161e0421c55d01a9a8172869c2f361

Observation 2b9d2b5a-5ac7-45c6-a90f-6207f9b2b115 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 21

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source=arxiv_source observed=2026-08-04T17:23:16.262168Z digest=sha256:e85b6f6e66bd3de4d8120103861c5a0d7b3a99f90f44afb7b4d7f7d8c49b8741

Observation 4285a41b-b788-473e-afb3-6cb721ca6610 · outbound

This paper cites Computer Vision -- ECCV 2024 , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Computer Vision -- ECCV 2024 , pages =

Reference 22

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source=arxiv_source observed=2026-08-04T17:23:16.264509Z digest=sha256:f70a16d63eab08751ac5edff02c45fa77e910ea8fc058d554b340396660bdb97

Observation 3ced6690-2b55-486a-885a-e14346f1154e · outbound

This paper cites PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction

Reference 23

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source=arxiv_source observed=2026-08-04T17:23:16.266838Z digest=sha256:0d128a195bbc73d8e6f437c0ed2779eadaa8f69aaf42814196824fbe221f9695

Observation 91162634-3a3d-4b23-b02a-9c759a58f3bf · outbound

This paper cites and Okuno, Tomoyuki and Nakata, Yohei and Keutzer, Kurt and Zhang, Shanghang , title =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs and Okuno, Tomoyuki and Nakata, Yohei and Keutzer, Kurt and Zhang, Shanghang , title =

Reference 24

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source=arxiv_source observed=2026-08-04T17:23:16.269395Z digest=sha256:c843501a965432fc7e4d6dca015f32e71d76f47b03a5fcac44ab52c1eca2b118

Observation f7debecc-3b70-4cb7-a942-d6317034d828 · outbound

This paper cites The Fourteenth International Conference on Learning Representations , year =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs The Fourteenth International Conference on Learning Representations , year =

Reference 25

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source=arxiv_source observed=2026-08-04T17:23:16.271539Z digest=sha256:95da3166a83f14472fa209e8fc0c70840fa02bbca2423881d15776ba80fabddd

Observation e1349d50-b950-4aea-b4fa-c036eb7f4d40 · outbound

This paper cites arXiv preprint arXiv:2509.24837 , year =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs arXiv preprint arXiv:2509.24837 , year =

Reference 26

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source=arxiv_source observed=2026-08-04T17:23:16.274701Z digest=sha256:589cec240c3c8828b08f68cf439661630a1d0df29dca8ce01bae68c440818e6e

Observation ed74f993-a7ac-4ecf-b0fa-3ade1ecc9fce · outbound

This paper cites The Thirty-Ninth Annual Conference on Neural Information Processing Systems , year =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs The Thirty-Ninth Annual Conference on Neural Information Processing Systems , year =

Reference 27

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source=arxiv_source observed=2026-08-04T17:23:16.276987Z digest=sha256:32270766ca7d16dbf895092ab0df15411f2958f4eba6c062638e83e46f6218f3

Observation a1e8daec-0ad8-42fc-91ed-42ffb196d330 · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the IEEE/CVF International Conference on Computer Vision , pages =

Reference 28

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source=arxiv_source observed=2026-08-04T17:23:16.279335Z digest=sha256:9bb6545e1c91b807273e9440141b2b52145d17bf2b7d80339a53221e600e94a0

Observation 63f807ed-abae-4d20-bf38-3bea1f3aeaef · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the Computer Vision and Pattern Recognition Conference , pages =

Reference 29

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source=arxiv_source observed=2026-08-04T17:23:16.281537Z digest=sha256:6747f266e5adf82e4daff28025e9a3e71315ac8ab53a8f66c5c5176cf944057a

Observation 56ac6f38-9978-46a6-aff6-7293d6157dac · outbound

This paper cites Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model

Reference 30

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source=arxiv_source observed=2026-08-04T17:23:16.283827Z digest=sha256:85ca563ccb03fe9c6111f25f24ef5686fe34bdaa3e0c337d42457aa895a8f5fa

Observation f0aad939-9553-427f-ba33-a8fd57dc7134 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 31

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source=arxiv_source observed=2026-08-04T17:23:16.286392Z digest=sha256:b8799d9b497c49d83107ff182d11a78cdb51622e6cc325aefe6830e6d238e09e

Observation daba2d85-d58d-46fb-a2a7-a524b9a00b16 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the AAAI Conference on Artificial Intelligence , volume =

Reference 32

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source=arxiv_source observed=2026-08-04T17:23:16.288774Z digest=sha256:befbd9f9d68391764205b838930637a8ae91e0ae2e61bc2a06a1417285f55b25

Observation d5e0c4ff-4087-41e2-a3a7-3d7d5f21d778 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the AAAI Conference on Artificial Intelligence , volume =

Reference 33

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source=arxiv_source observed=2026-08-04T17:23:16.290878Z digest=sha256:a142c148d66de43cf4482ca8ee8eab9efd309a23ccbf4f2ce2147c24a4df6fcb

Observation e9991464-cee1-44fd-af42-393f698b63f7 · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the Computer Vision and Pattern Recognition Conference , pages =

Reference 34

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source=arxiv_source observed=2026-08-04T17:23:16.293693Z digest=sha256:30638e578e122a93ebc05d39db6f90bd327d864b4b487da2f2efa54f3b281f58

Observation 924ecc97-c796-401d-83cf-d8baec19dfba · outbound

This paper cites FocusLLaVA: A Coarse-to-Fine Approach for Efficient and Effective Visual Token Compression.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs FocusLLaVA: A Coarse-to-Fine Approach for Efficient and Effective Visual Token Compression

Reference 35

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source=arxiv_source observed=2026-08-04T17:23:16.296228Z digest=sha256:177818bec75130162f93b71b55c3a1997f865e513f4762d3220e133d6054b008

Observation d8689bff-a83a-4786-8b6c-a56d3bf64478 · outbound

This paper cites The Fourteenth International Conference on Learning Representations , year =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs The Fourteenth International Conference on Learning Representations , year =

Reference 36

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source=arxiv_source observed=2026-08-04T17:23:16.298693Z digest=sha256:5f8be38b3f9fd3ea2ebd1f3c1d015fc7ecb7d679c61049279c09fe797e8d59eb

Observation 5bfb6a66-56d3-4d8a-a9be-46a4c9ea2904 · outbound

This paper cites arXiv preprint arXiv:2507.20630 , year =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs arXiv preprint arXiv:2507.20630 , year =

Reference 37

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source=arxiv_source observed=2026-08-04T17:23:16.300943Z digest=sha256:8204d880469aa2beb705b7268f11e5a55bbe53560d535e9ca529c68b63628843

Observation 42e4f99b-8179-413f-96f8-ca3031ac7aab · outbound

This paper cites and Piergiovanni, A.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs and Piergiovanni, A

Reference 38

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no resolver link, observed 2026-08-04T17:23:16.303329Z

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source=arxiv_source observed=2026-08-04T17:23:16.303329Z digest=sha256:16f28ba683affed67525fce92088ac699efe00165b6b329ec541c66659e24bae

Observation 31b74e81-d549-48df-82dc-2bcc068b7f2f · outbound

This paper cites The Eleventh International Conference on Learning Representations , year =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs The Eleventh International Conference on Learning Representations , year =

Reference 39

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source=arxiv_source observed=2026-08-04T17:23:16.305635Z digest=sha256:da64658e66675d94a114c1e8be402c25feb0423206856c6550d0760f9b9f7f28

Observation 545396b8-e7c3-40e4-8bfa-7fdc8d6a834d · outbound

This paper cites Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =

Reference 40

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source=arxiv_source observed=2026-08-04T17:23:16.307728Z digest=sha256:4e8920d91590ebed757d559bd7c7a55fefda4d461c5876e8a5f849124e5b3179

Observation 60ac6df1-7e3a-44f3-9d55-e4376775bb35 · outbound

This paper cites Adaptive Computation Time for Recurrent Neural Networks.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Adaptive Computation Time for Recurrent Neural Networks

Reference 41

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source=arxiv_source observed=2026-08-04T17:23:16.309788Z digest=sha256:2ae224ee92dbe75071ad190243e9f11bb262ba60a0eae17903a26afae1a430f9

Observation b719c8b9-dc49-4e0d-9ad7-f2b85b8abc67 · outbound

This paper cites and Grauman, Kristen and Feris, Rogerio , title =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs and Grauman, Kristen and Feris, Rogerio , title =

Reference 42

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source=arxiv_source observed=2026-08-04T17:23:16.312500Z digest=sha256:2ce81f6c8a4398f8eec5ac0ff3be0616a7cafc496c8d803c9188623fa8ee1617

Observation a88c8932-9a4f-47ed-8f12-9fbe2c9227a8 · outbound

This paper cites , title =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs , title =

Reference 43

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source=arxiv_source observed=2026-08-04T17:23:16.314794Z digest=sha256:226d99ecf2f78e4be55bfe90b544b05e832d427c0982b7916bbf10ee5abc4cf9

Observation 680e4685-233e-498f-9200-c744d39e8287 · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Advances in Neural Information Processing Systems , volume =

Reference 44

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source=arxiv_source observed=2026-08-04T17:23:16.317042Z digest=sha256:ada327bf30f60dbd1cd9565c4df54544ad2c48b69a730fc1e4d8bca8c2fbca95

Observation 3ec376e1-657d-4e7d-b78a-271032e5e60d · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Advances in Neural Information Processing Systems , volume =

Reference 45

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source=arxiv_source observed=2026-08-04T17:23:16.319256Z digest=sha256:9d21a93a9aff7845ea3c112f6e95a0506e0f8bdc00c6836b1d5adc0e6c1b2d1a

Observation 116a78d3-95b8-4046-b8d6-70280056484c · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 46

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source=arxiv_source observed=2026-08-04T17:23:16.321518Z digest=sha256:586d827e769be07f6edae0750d6fa0edc4dd45694b9baafaea6f8c5a1ccd4642

Observation 13f46ad9-817f-476f-9ff8-8c2ddc5b1170 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 47

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source=arxiv_source observed=2026-08-04T17:23:16.323717Z digest=sha256:9ae94253dcec300594a2d4afc07c3bf4757a7fce0f850752c271d4a18b2ef49e

Observation 754eb6c5-b16d-4d12-9e8d-e0bf403b5466 · outbound

This paper cites and Tay, Yi and Metzler, Donald , title =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs and Tay, Yi and Metzler, Donald , title =

Reference 48

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source=arxiv_source observed=2026-08-04T17:23:16.326153Z digest=sha256:92339ed577b6a23d0e3bf05ca5f2716f173e8b937b7762bdf2afdf2bc4caa1a2

Observation f36d8354-964a-448f-a822-848958918f06 · outbound

This paper cites Mixture-of-Depths: Dynamically allocating compute in transformer-based language models.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Reference 49

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source=arxiv_source observed=2026-08-04T17:23:16.328575Z digest=sha256:f6d072fad761adc132b1305732dc4ca0e2701fe911298c9a89c20ecab41cb6ff

Observation b2336bbc-b73c-4319-a773-c473372379d0 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 50

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source=arxiv_source observed=2026-08-04T17:23:16.331156Z digest=sha256:4991515e47b688f3525e4226b308ac576938e2a7cfa0e8d71dd6b0d64129f6bd

Observation a1e4b0bf-e2ca-4828-9e18-74412de29071 · outbound

This paper cites RoboVQA: Multimodal Long-Horizon Reasoning for Robotics.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs RoboVQA: Multimodal Long-Horizon Reasoning for Robotics

Reference 51

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source=arxiv_source observed=2026-08-04T17:23:16.333572Z digest=sha256:92cafe1b5fe06cc09f589a4ed2471917182b8c8b602a7ed9ca5dbe95415a6f19

Observation b4063264-a7af-40be-af5e-44770881646b · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 52

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source=arxiv_source observed=2026-08-04T17:23:16.335985Z digest=sha256:ab6d776fee4f0c931e572f1be0c37076e72bece7204bc14dec09936293804db5

Pith citing papers

No inbound Pith citation observations are available.