METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models | Paper Citation Record

Observation 9bf00e6c-7602-4633-96e4-f9a9d413c4b0 · outbound

This paper cites HiRED: Attention-guided token dropping for efficient inference of high-resolution vision-language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models HiRED: Attention-guided token dropping for efficient inference of high-resolution vision-language models

Reference 1

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Observation c1485921-bb5c-4034-9de7-180bcad8880c · outbound

This paper cites Qwen Technical Report.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Qwen Technical Report

Reference 2

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Observation 87ea521d-1dbc-492e-9cda-e099ef20f5d9 · outbound

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

METEOR: Multi-Encoder Collaborative 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 ead1432d-477a-4e49-9bcb-f4c9afa33128 · outbound

This paper cites Language models are few-shot learners.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Language models are few-shot learners

Reference 4

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Observation 999b5cec-07e7-4562-9256-9980b3894810 · outbound

This paper cites Matryoshka multimodal models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Matryoshka multimodal models

Reference 5

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Observation a357e339-40d8-4090-b109-d7355d21abd4 · outbound

This paper cites Open-LLaV A-NeXT: An open- source implementation of LLaV A-NeXT series for facilitat- ing the large multi-modal model community.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Open-LLaV A-NeXT: An open- source implementation of LLaV A-NeXT series for facilitat- ing the large multi-modal model community

Reference 6

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Observation 31abbaa4-7649-4f69-9f56-0f1ac3899f23 · outbound

This paper cites An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceler- ation for large vision-language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceler- ation for large vision-language models

Reference 7

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Observation b63ad6fe-d42f-4d92-8267-c952b4650498 · outbound

This paper cites How far are we to GPT- 4V? Closing the gap to commercial multimodal models with open-source suites.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models How far are we to GPT- 4V? Closing the gap to commercial multimodal models with open-source suites

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.853260Z

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Observation f1a7e27f-3eba-4793-9aaa-97f459c7bee0 · outbound

This paper cites Intern VL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Intern VL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.841852Z

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Observation 5f33187f-e21e-4350-9a3b-7088eca57be7 · outbound

This paper cites Gonzalez, Ion Stoica, and Eric P.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Gonzalez, Ion Stoica, and Eric P

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.829581Z

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Observation 37646758-eeef-4934-a2ef-29c79e748467 · outbound

This paper cites InstructBLIP: Towards general-purpose vision- language models with instruction tuning.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models InstructBLIP: Towards general-purpose vision- language models with instruction tuning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.817783Z

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Observation 0c6b303e-068b-47bf-b7c6-3d6146ff82f1 · outbound

This paper cites Transformer-XL: Attentive language models beyond a fixed-length context.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Transformer-XL: Attentive language models beyond a fixed-length context

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.806432Z

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Observation 5a9f1c54-b5cd-455c-9b18-fbe011776efb · outbound

This paper cites Prune spatio-temporal to- kens by semantic-aware temporal accumulation.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Prune spatio-temporal to- kens by semantic-aware temporal accumulation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.794124Z

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Observation 7fac3077-35de-432e-899d-6aaaa01bad56 · outbound

This paper cites MouSi: Poly-Visual-Expert Vision-Language Models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models MouSi: Poly-Visual-Expert Vision-Language Models

Reference 14

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Observation 67d42725-c1e2-4d6e-bd75-eaa9a483f1cd · outbound

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

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:01.159137Z

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Observation 88619815-830c-4a39-a3b3-44a8887b3f14 · outbound

This paper cites Llava-uhd: an lmm perceiving any aspect ratio and high- resolution images.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Llava-uhd: an lmm perceiving any aspect ratio and high- resolution images

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.783034Z

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Observation e7f6c151-3316-4943-9fc6-eb171a65499d · outbound

This paper cites Re- thinking token reduction in MLLMs: Towards a unified paradigm for training-free acceleration.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Re- thinking token reduction in MLLMs: Towards a unified paradigm for training-free acceleration

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:01.321659Z

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Observation b21e7469-2038-42d5-85b6-e5790f74686a · outbound

This paper cites Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models

Reference 18

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Observation 26d5b24b-0dec-42fb-960f-03fc79cf751c · outbound

This paper cites ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification

Reference 19

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Observation 1bc995b1-b506-4ba3-8c1e-9a3d6fe5ede7 · outbound

This paper cites iLLaV A: An image is worth fewer than 1/3 input tokens in large multimodal models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models iLLaV A: An image is worth fewer than 1/3 input tokens in large multimodal models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:01.597860Z

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Observation 3898d8c7-9120-4921-a83b-c31e29a5a7e6 · outbound

This paper cites Matryoshka query Trans- former for large vision-language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Matryoshka query Trans- former for large vision-language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.772607Z

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Observation 5e583f37-876a-4183-bdcd-00b3f57fe84d · outbound

This paper cites IVTP: Instruction-guided visual token pruning for large vision-language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models IVTP: Instruction-guided visual token pruning for large vision-language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.761920Z

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Observation 4842f778-bffd-4c00-becb-7104d57a6a65 · outbound

This paper cites GQA: A new dataset for real-world visual reasoning and compositional question answering.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models GQA: A new dataset for real-world visual reasoning and compositional question answering

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.751293Z

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Observation 31d9f30d-3254-4f1c-8878-c08df639758a · outbound

This paper cites From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models

Reference 24

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Observation 41ffb6cd-4e62-4975-bb51-ac4ff2dec3bb · outbound

This paper cites FoPru: Focal Pruning for Efficient Large Vision-Language Models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models FoPru: Focal Pruning for Efficient Large Vision-Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:01.973663Z

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Observation 76f4f518-fae1-4b0b-b269-393b8a11e5a4 · outbound

This paper cites Devils in middle layers of large vision- language models: Interpreting, detecting and mitigating ob- ject hallucinations via attention lens.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Devils in middle layers of large vision- language models: Interpreting, detecting and mitigating ob- ject hallucinations via attention lens

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.740474Z

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Observation a4427d5e-302e-4b23-ae12-c54ea29f5961 · outbound

This paper cites BRA VE: Broadening the visual encoding of vision-language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models BRA VE: Broadening the visual encoding of vision-language models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.729482Z

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Observation 8dd021e5-ca85-4eb9-b5f1-346cd0c4358e · outbound

This paper cites A diagram is worth a dozen images.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models A diagram is worth a dozen images

Reference 28

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Observation cfcabdef-1f29-4320-ae73-b6344af3bff9 · outbound

This paper cites MoAI: Mixture of all intelligence for large language and vision models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models MoAI: Mixture of all intelligence for large language and vision models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.709154Z

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Observation 55b7590d-b294-472b-a4b8-5a07d4faaf0f · outbound

This paper cites Pix2Struct: Screenshot parsing as pretraining for visual lan- guage understanding.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Pix2Struct: Screenshot parsing as pretraining for visual lan- guage understanding

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.698366Z

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Observation bd6385b9-6310-4559-b82c-97769486e6ed · outbound

This paper cites SEED-Bench: Benchmarking multi- modal large language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models SEED-Bench: Benchmarking multi- modal large language models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.686313Z

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Observation a1defa97-b381-48e2-abbd-47a45d5d425d · outbound

This paper cites RedundancyLens: Revealing and Exploiting Visual Token Processing Redundancy for Efficient Decoder-Only MLLMs.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models RedundancyLens: Revealing and Exploiting Visual Token Processing Redundancy for Efficient Decoder-Only MLLMs

Reference 32

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Observation 040ac8eb-4830-45eb-bd0d-516ac390e385 · outbound

This paper cites TokenPacker: Efficient visual projector for multimodal LLM.International Journal of Computer Vision, pages 1–19, 2025.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models TokenPacker: Efficient visual projector for multimodal LLM.International Journal of Computer Vision, pages 1–19, 2025

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.675355Z

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Observation 41ad1dc6-0ca1-4e89-b11a-e48559200bef · outbound

This paper cites Evaluating object hallucination in large vision-language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Evaluating object hallucination in large vision-language models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.664717Z

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Observation cdad8285-6e6f-4c2a-9e95-6cca04aac393 · outbound

This paper cites Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:02.711071Z

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Observation 3b6694fd-4a77-40fe-9369-c16178c0e4f6 · outbound

This paper cites Mon- key: Image resolution and text label are important things for large multi-modal models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Mon- key: Image resolution and text label are important things for large multi-modal models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.654102Z

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Observation 62166a73-9f04-4031-9caf-5f17fd56f6d5 · outbound

This paper cites HRank: Filter pruning using high-rank feature map.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models HRank: Filter pruning using high-rank feature map

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.643503Z

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Observation 6d34a01d-7111-4e9d-97c4-c004e9b98230 · outbound

This paper cites SPHINX: A mixer of weights, visual em- beddings and image scales for multi-modal large language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models SPHINX: A mixer of weights, visual em- beddings and image scales for multi-modal large language models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.633793Z

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Observation c7edf160-0946-44c9-beb1-fab7aefc1333 · outbound

This paper cites Boosting multimodal large language models with visual to- kens withdrawal for rapid inference.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Boosting multimodal large language models with visual to- kens withdrawal for rapid inference

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.623493Z

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Observation 8ee48e26-ab84-46ec-8f53-0fbd3e941446 · outbound

This paper cites Visual instruction tuning.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Visual instruction tuning

Reference 40

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Observation ca253b1e-7681-4ec9-9b34-a803633737dd · outbound

This paper cites Improved baselines with visual instruction tuning.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Improved baselines with visual instruction tuning

Reference 41

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Observation abbbc795-999e-4f68-8531-01db123ee236 · outbound

This paper cites LLaV A-NeXT: Im- proved reasoning, OCR, and world knowledge.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models LLaV A-NeXT: Im- proved reasoning, OCR, and world knowledge

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.591311Z

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Observation b5ddd117-2279-42ae-a3d2-fd28eafd8d09 · outbound

This paper cites Prismer: A vision-language model with multi-task experts.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Prismer: A vision-language model with multi-task experts

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.581568Z

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Observation d0eca418-d976-4faa-adc6-75a5568b58d2 · outbound

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

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model

Reference 44

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Observation bce14f18-8693-40b5-a6be-ebd619080353 · outbound

This paper cites OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:03.510688Z

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Observation 480f02da-cf24-4459-a1fe-aa3824c592b7 · outbound

This paper cites MMBench: Is your multi-modal model an all-around player? In Proceedings of the 18th European Conference on Computer Vision (ECCV), pages 216–233, 2024.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models MMBench: Is your multi-modal model an all-around player? In Proceedings of the 18th European Conference on Computer Vision (ECCV), pages 216–233, 2024

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.570794Z

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Observation cee4870b-154d-4b25-95d9-b48a0270594a · outbound

This paper cites A ConvNet for the 2020s.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models A ConvNet for the 2020s

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.559619Z

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Observation ace58567-937f-457f-bebb-715a9d7d50e2 · outbound

This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:03.786752Z

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Observation ae65ec97-940f-4ab8-b051-589240f5ab3f · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.549553Z

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Observation 2c83b4ec-4b18-47d4-b38c-59f88bd7146e · outbound

This paper cites Feast your eyes: Mixture- of-resolution adaptation for multimodal large language mod- els.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Feast your eyes: Mixture- of-resolution adaptation for multimodal large language mod- els

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.539139Z

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Observation 70a081ae-ac1b-43fa-8d46-07caadfed91d · outbound

This paper cites OK-VQA: A visual question answer- ing benchmark requiring external knowledge.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models OK-VQA: A visual question answer- ing benchmark requiring external knowledge

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.527940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0937d56c-abf6-49c9-85fb-0718abffb9c1 · outbound

This paper cites ChartQA: A benchmark for question answering about charts with visual and logical reasoning.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models ChartQA: A benchmark for question answering about charts with visual and logical reasoning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.515372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ab13193-4dec-45a0-a24b-f1aebadadae2 · outbound

This paper cites DocVQA: A dataset for VQA on document images.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models DocVQA: A dataset for VQA on document images

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.504388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5066e26c-01aa-49ca-a91f-0f801504a090 · outbound

This paper cites MM1: methods, analysis and insights from multimodal LLM pre- training.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models MM1: methods, analysis and insights from multimodal LLM pre- training

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.493930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6da9200c-4623-4c24-bd9c-bdd68b00b26d · outbound

This paper cites DeepStack: Deeply stacking visual tokens is surprisingly simple and ef- fective for LMMs.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models DeepStack: Deeply stacking visual tokens is surprisingly simple and ef- fective for LMMs

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.482988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1181e175-b624-4b44-8bda-bc0c2878e5cf · outbound

This paper cites Learning transferable visual models from natural language supervision.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Learning transferable visual models from natural language supervision

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.471349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6ae5eacd-d64e-4b0a-8573-1e8e45d967d9 · outbound

This paper cites LLaV A-PruMerge: Adaptive token reduc- tion for efficient large multimodal models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models LLaV A-PruMerge: Adaptive token reduc- tion for efficient large multimodal models

Reference 57

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Source-reported events for the cited work

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Observation 3e60f03f-2a83-4af4-a291-5bbf33ee083f · outbound

This paper cites Eagle: Exploring the design space for multimodal LLMs with mixture of encoders.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Eagle: Exploring the design space for multimodal LLMs with mixture of encoders

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.460870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e706f53b-662e-4af2-abf9-bae93973a430 · outbound

This paper cites Towards VQA models that can read.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Towards VQA models that can read

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.450379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b0249041-ae47-478a-8c72-4f287eb73c4a · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:04.652268Z

Source-reported events for the cited work

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Observation e013054e-98ce-486e-8c52-63808d777c61 · outbound

This paper cites Cambrian-1: A fully open, vision-centric exploration of multimodal LLMs.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Cambrian-1: A fully open, vision-centric exploration of multimodal LLMs

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.438994Z

Source-reported events for the cited work

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Observation fb96b329-82b9-4b0c-965c-452cf3350ee7 · outbound

This paper cites Eyes wide shut? exploring the vi- sual shortcomings of multimodal LLMs.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Eyes wide shut? exploring the vi- sual shortcomings of multimodal LLMs

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.427427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 282b5c52-48b4-4e8c-881e-9ce32466a1d7 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:04.867176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9cfad62-f673-4087-bdbf-bcec45303e3c · outbound

This paper cites [CLS] Token Tells Everything Needed for Training-free Efficient MLLMs.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models [CLS] Token Tells Everything Needed for Training-free Efficient MLLMs

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:04.956498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d91cfad3-cda2-4078-8ccc-637e3f969956 · outbound

This paper cites FOLDER: Accelerating Multi-modal Large Language Models with Enhanced Performance.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models FOLDER: Accelerating Multi-modal Large Language Models with Enhanced Performance

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:05.021161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a0cabfe-691d-4cfa-8656-f7ba10c3efb5 · outbound

This paper cites Vary: Scaling up the vision vocabulary for large vision-language model.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Vary: Scaling up the vision vocabulary for large vision-language model

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.416656Z

Source-reported events for the cited work

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Observation 8e692868-cfec-4a7c-88a0-1d30e254f151 · outbound

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

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:05.173548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d4cf09f-6e98-4990-a85f-6589f2c41054 · outbound

This paper cites Mitigat- ing hallucination in large vision-language models via mod- ular attribution and intervention.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Mitigat- ing hallucination in large vision-language models via mod- ular attribution and intervention

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.406074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 77bc4401-8c03-49ac-8110-7207b35e9308 · outbound

This paper cites DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:05.305866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d637267a-2472-4098-a01c-724fea3ddf31 · outbound

This paper cites mPLUG- Owl2: Revolutionizing multi-modal large language model with modality collaboration.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models mPLUG- Owl2: Revolutionizing multi-modal large language model with modality collaboration

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.395012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4331c16f-9315-4166-b6d1-784332056de9 · outbound

This paper cites Fit and prune: Fast and training-free visual token pruning for multi- modal large language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Fit and prune: Fast and training-free visual token pruning for multi- modal large language models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.383458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation adce2a7b-2be5-4439-a3e7-a01e4d4d0829 · outbound

This paper cites ATP-LLaV A: Adaptive token pruning for large vision language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models ATP-LLaV A: Adaptive token pruning for large vision language models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.372227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 37c35d01-b84b-4c1d-a36b-98c03346ace0 · outbound

This paper cites V oCo-LLaMA: Towards vision compression with large language models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models V oCo-LLaMA: Towards vision compression with large language models

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.342800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 841174f8-18ae-47fb-be48-393b559fb4a1 · outbound

This paper cites Lifting the veil on visual information flow in MLLMs: Unlocking pathways to faster inference.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Lifting the veil on visual information flow in MLLMs: Unlocking pathways to faster inference

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.184101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 18328f71-a4db-42db-a401-0e22267c7c09 · outbound

This paper cites Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:05.802359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20c3e7d9-921a-4bb0-8a53-73fcbf1558ff · outbound

This paper cites LLaV A-Mini: Efficient image and video large multimodal models with one vision token.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models LLaV A-Mini: Efficient image and video large multimodal models with one vision token

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:07.952000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bacc4ae4-e3ee-494c-9aba-71a237fc9cf9 · outbound

This paper cites Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate Hallucination in LVLMs.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate Hallucination in LVLMs

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:05.927482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c1df209-b7b8-4371-b01c-df36f4f61cc0 · outbound

This paper cites SparseVLM: Visual token sparsification for efficient vision- language model inference.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models SparseVLM: Visual token sparsification for efficient vision- language model inference

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:07.783244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 342ec6a2-1905-450e-b7f2-2146ea7721b6 · outbound

This paper cites Treat Visual Tokens as Text? But Your MLLM Only Needs Fewer Efforts to See.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Treat Visual Tokens as Text? But Your MLLM Only Needs Fewer Efforts to See

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:06.117105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ceeb469-71fe-446b-85ce-77e1fbf36a68 · outbound

This paper cites Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:18:06.881928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7d65a803-0200-4e6b-aa91-044e0e65175b · outbound

This paper cites AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:06.370053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3bbd1c0-7f4e-49bb-aaaa-2dae09d258cf · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:06.501322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01988274-4b3b-42ef-bd1b-1caf36fbd444 · outbound

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

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models FocusLLaVA: A Coarse-to-Fine Approach for Efficient and Effective Visual Token Compression

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:06.528428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f050d5af-14cf-4662-9620-6bcb4201c868 · outbound

This paper cites MoV A: Adapting mixture of vision experts to multimodal context.

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models MoV A: Adapting mixture of vision experts to multimodal context

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:07.614354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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