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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-09T06:31:02.800959+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=sha256:1cf7d5ee34b123c0ee4a178ac5fe6a76860609cd60a9365e50db50445998bd70

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=sha256:85971d0eef623e53deafba6dfb0776f5a0f1b91cb70104e7bd454d63f1e34404

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=sha256:81bbeb058724711470947ce0786197eca5fea6d77f8d2a6a5da8a6139a2d3b1e

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=sha256:7755e9bf2d37d6aebbaddc48b3ef3d19d789adbe4e523b5ee16b1fc96301000f

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:d4c9e79e8ed9177077dc51d1ba159eafe961c6b9620dc2298676ff9b8e52f313

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:f6cb7cc7392039002af01dfcb820c74d3bed6d920e988bfb41aaf3348cf9e982

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:b2ff56c0d70afdc17425da90f0d90255e9074f6d4ccf7c0ffa6d8076c9b3ae34

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=sha256:215125bb8c926b8d30b134b1fc73540c821006b2263c04e6f21c0923a55e4bb0

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:c47724d8c52787c1f5a52b294b4d9364ccb5469845a6ff6f8a0bf5a1675c68e7

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=sha256:2ff179a0f1c7d9f335740c281635ca54e28cdb39489265a3292b4f5600c45136

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:c47a48a611634dc6b67c674faee5850bc283ccb9e79b913f2635a07427605a60

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=sha256:671cc33d1807a235e0d940c05b43319971107acc14a5177d8edad40659159641

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=sha256:1a080581cb969d06507ffaf8fa422818ba5243253f0b057eed6c6c8f78f06464

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=sha256:2e7664797370d82a9d3c70db9e7826107c95c2f179533d585c188796a5b683a2

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=sha256:25c597687b056f93998cac7655b65b2e5e280eef3bdf49e6a25431c39e46ff7c

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=sha256:53649dd02f6095d4c774307b225b40b6b6836159fc70da15879fe0d50f7919ee

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=sha256:6483db172e361dcd8339d50e4eff75dfba2ec25010f08ec40ad79422012fcc43

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=sha256:008f95d5d2dfa7dbb52abd5d3cab54e6d08ba85e3d218871da3e1f4a623298d3

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:d9f3384854375a4791bdae8e6b9d1151598345098ee60610e45c1e9e27b02dce

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=sha256:3d19c09242baafc6d542f2bbdf7449f3d6ae822a8e607aab855c2d3df1317ee5

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:f239cbc0e9cf06fd7116ff2b6116d5a81ae7892e97676fea4e08485cbee78e00

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:b0a91c3f017ee75cf86758d4eda91f2d2dc84a11d57da2d480b52273128f5db0

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:cd0a310f8fe762e105324605a904f8c8a2dd7cbee79b01a78897b62173606642

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=sha256:1813ed1f2a3060525cb39741462e3f2326e3fe36dbb092c14a9a53e9f12db928

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=sha256:1a27ded79f0ff77d5d0fbcd7f58a30198d657dc767044d2d3db41a3aad9171fb

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:fc326f4c79801c5eb736e45812bcf8ca3541da591f5d69e24cb402bfbc64ff55

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:b2735ed21876013d01033b2a4bebd025dd4ed56a16ebe4d4547e6c94feb0ee28

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=sha256:1a6a1c8347d709812a1d8f26e86a0a8a4e20a94104c05569861a3fbfb7a42286

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:bca16514ba69a2c294fbd9b1e75dd3e3c5dc1e6607ea287db0c02179a9de0f99

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:d6f541d7d8d239e30d334cdcaa3aee0f3260a7f8e4fb88079ea746ba0bd82d6d

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=sha256:9b80f853c775e595d70e1550429998e3f5de194a9f2fea50bebb94a86257a060

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=sha256:1ade3efcafec53cc04a5cbc2dd4afebd00397b89cd1cba6e1a83383da52e091a

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:b674f8c2bfa38b6362820046698f8b60cb4106db7027429715eca9510a68e775

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:e76df08ecbc6460d3b15c72e287f431bc8e7d27d0c9ec06a0ba09853b74b28fb

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=sha256:0ec9266a665ab70f78a732ced9e19123b60f5c930b0df513699e1c2228bbe7ea

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=sha256:08b7b7929673c63f7ff05536ca64ca8b74e3d096c0f217680ac527fd36e2a72d

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=sha256:929c872604b16b885e5e21461d77200394a0460eb083488af6130092517f4d8f

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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source=arxiv_source observed=2026-08-04T17:23:16.303329Z digest=sha256:d512c953d1c2610608af28407e4245d28b0db9803210f6b4e3629a3fcbf8f47e

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:e824894820548b5f57b41dd4bc4da0c5cf970c8db04cce911ac206e44c298ea5

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=sha256:43538e8e1096212594d0535abf814a325ad98e663e86f8b130621071c918fa85

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=sha256:399d2195fd4aaf69d7e73b455d5a765cf82ca3fbfafbfb82189b4a951cdb09ae

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=sha256:5a9a5d1a055c5dfc667b903ff66f529ed85f167b409707dd57cea42159ed4d17

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=sha256:5b42fa24f579265ee0f248ebd6251e0e56914b4600c3b4bc2437609df72c128a

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=sha256:74b3f33d7c01c3644fd7fc32a8897668880bc33ba2f43bd26b6332d40abb548a

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:de5b853b2330f9709be644c495ae136b49cb5f4f42f97a8f4bffd00446f80a02

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:f09c3b183c0d768e7b26d67249ac99cc4383251aa73a0098b6a0376f7db3a183

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:f4ceaca28e5ced8ebe9603edfe4e94bad3cca2d05e03c88cf19237f14619a2a3

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=sha256:34b1763d4fb1aa5bbe93a183b439fb817b709a8fa86a221975d7f050317040d0

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=sha256:9f7fbaa83ffb43d70144e96da81adc5d02cc27a5a1a1ebbb78fd5a29c80254bc

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:f37cac0ce5ea249671fbe4fcf8be231731593a2e2f77ec96fbb2bf44e4336260

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:f3e98e331cd45ab7f551f70bebc8b4495bb3591bdfaf34827a885c9d7044855e

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=sha256:191d79941b9ec590e333871e2da93d5fdf4869be8ff17c6ce389e3a01bffa612

Pith citing papers

No inbound Pith citation observations are available.