Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T04:30:14.535753Z
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 100 of 105 outbound references and 0 inbound Pith citation observations for arXiv:2608.06411.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T04:30:14.535753Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 105 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation d3fefcc0-c1e5-49a9-8b02-7fb1be112d50 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Qwen2.5-VL Technical Report
Reference 1
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Observation 5bf4cec4-7fbf-4622-8715-6616263e904a · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Reference 2
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Observation 6613c666-b789-4a1d-ab34-393cc274f899 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 3
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Observation c552ff0a-8265-45dc-a260-ef63ec3ca58b · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ICML , year=
Reference 4
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Observation 9216dc4a-716e-4300-a6a9-785c5b82ebb1 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ICML , year=
Reference 5
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Observation 7f200349-2f41-498c-bbb6-00669a2110fe · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
Reference 6
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Observation 655ff2fc-a489-418d-8f29-ccdc15ad5dab · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
Reference 7
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Observation 788c189a-8fd3-4e98-b920-0f4319ec14bc · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
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Observation 36222e3f-701e-4743-b392-5722206979a5 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 9
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Observation caa82ddf-0c37-421f-a9fc-9152b55e2ff1 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Unresolved cited work
Reference 10
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Observation 456cabf5-7ad3-4338-a54b-94098b316d52 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
Reference 11
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Observation 4a5433c3-b826-4b3f-8dd8-2695fc491d56 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Reference 12
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Observation e3ed016b-e8d6-4909-8094-f1906e58ba7d · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Gemini: A Family of Highly Capable Multimodal Models
Reference 13
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Observation 498c2365-7b92-4d8d-a347-8476b14d3987 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reference 14
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Observation 83c95443-0f89-4b8f-b099-9c4efeda70fe · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Unresolved cited work
Reference 15
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Observation 04fcfba3-a3aa-4fc8-be22-c34dbc4866f3 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Token Merging: Your ViT But Faster
Reference 16
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Observation daddbc57-a323-435e-9fb5-7d783b1be326 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
Reference 17
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Observation d0f3ba0b-a1e1-49bf-ac40-98a4e7670049 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin arXiv preprint arXiv:2403.15388 , year=
Reference 18
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Observation 4cd2cbfe-3851-4f2f-9a68-ff4bc8485b30 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 19
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Observation c2530b53-5a83-4dd9-9946-b4b97b816b33 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
Reference 20
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Observation 3c74d0f9-56b4-442e-91c8-51e2e8a3ec73 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin TokenPacker: Efficient Visual Projector for Multimodal LLM
Reference 21
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Observation 5c9d12e3-3039-410b-b995-177db0a4e534 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin arXiv preprint arXiv:2411.17686 , year=
Reference 22
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Observation b05b1e3c-ad65-4f76-a39e-be00825a9b60 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Llama 2: Open Foundation and Fine-Tuned Chat Models
Reference 23
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Observation 73fd6f05-f54a-4edf-a71b-92ff8ec78783 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Qwen Technical Report
Reference 24
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Observation 6ed976d5-52fa-4f87-b350-6d6483148a3a · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Reference 25
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Observation 100f7f4a-5ab0-4897-aeac-f15ce15c3ddc · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Reference 26
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Observation 51982df7-5667-4d16-8a10-81e67ee8acb4 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 27
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Observation 5bc6395f-69e3-4fb0-b1e5-d9efa37ef0cf · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 28
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Observation 6164b773-31a9-42e1-a6bc-7d14aa21dccc · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs
Reference 29
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Observation e270aa12-acc4-49a4-8290-05c05ff6845a · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
Reference 30
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Observation b888b749-faa7-4be3-b08e-3024b0e967d0 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model
Reference 31
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Observation 5f172363-a22b-4c8d-b29a-583864f922bb · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models
Reference 32
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Observation f7e6a82c-d1ad-4b30-9c0c-272f3de478b9 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Efficient Vision-Language Models by Summarizing Visual Tokens into Compact Registers
Reference 33
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Unavailable: canonical work link unavailable.
Observation 608d509a-bded-40cb-8ee1-8cb0781c9a7d · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin InternLM2 Technical Report
Reference 34
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Observation 92b9e655-b4f4-4e0a-9a03-f79c0a52089d · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 35
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Observation aaa4e496-0e8a-42c7-b286-2b822f85099c · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin WACV , year=
Reference 36
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Observation 5cb99119-9302-4632-ba03-ef4f6964d551 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin WACV , year=
Reference 37
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Observation 678ccdbd-6575-4c75-9c72-c3410921ded0 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
Reference 38
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Observation 9567cdd9-78a5-40e5-b775-3c853645e88b · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 39
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Observation 8060f9d7-21bd-49d3-b775-04767abd0b3a · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 40
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Observation c9bd4578-b93d-4a31-a85d-2ccab01855f4 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin EMNLP , year=
Reference 41
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Observation 27495724-e151-41ce-b800-bc8f69d17a57 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 42
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Observation 3c74206c-3482-49aa-93da-85f0681ee83f · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
Reference 43
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Observation 8b16f436-314e-4cb7-879a-1c7375f87e85 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ECCV , year=
Reference 44
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Observation c8e980ba-49ac-46a2-9ef9-9724a899f10d · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin arXiv e-prints , year=
Reference 45
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Observation cc095e69-dfcf-4ff8-96bd-0af283339d9e · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 46
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Observation d44975fa-5d17-40c0-a7a4-11d836af12bf · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
Reference 47
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Observation 0a18acc9-376b-4343-b92c-c026baf70eed · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models
Reference 48
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Observation 20e53230-899e-417f-92db-a8dfd19d0703 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
Reference 49
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Observation d406f4c0-480d-4dd0-8802-74a953a56716 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ECCV , pages=
Reference 50
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Observation ef15e32c-ce71-4f94-aa36-064ccacaabc5 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Reference 51
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Observation 18ef600a-d58c-4cbe-8cdb-1238e205f75b · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
Reference 52
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Observation 5bbe1032-10e0-43e0-96f6-cb7127e9fa02 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs
Reference 53
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Observation 57db35f6-fe3e-49e7-a9fd-989fbf4a5cde · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings
Reference 54
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Observation c4bfe39b-061b-4146-86c4-abc0b20949bd · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin FocusLLaVA: A Coarse-to-Fine Approach for Efficient and Effective Visual Token Compression
Reference 55
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Observation 72812fb3-2681-4c41-b044-677deb2ad573 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin DeepSeek-VL: Towards Real-World Vision-Language Understanding
Reference 56
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Observation b7c065a8-5ccc-4a65-a1a6-4ab6c37f6b8f · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
Reference 57
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Observation 19a85498-d981-42e3-8a41-06ba36b4a12c · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 58
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Observation 868f1a82-2656-424b-b0a5-40c2d9dde5d3 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
Reference 59
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Observation 709f01b0-9b09-40de-bf1b-d9a8590fbc55 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
Reference 60
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Observation 444a931e-74ea-4114-b2aa-e45f19f5938f · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ICML , pages=
Reference 61
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Observation d3efd223-49a3-4e6d-b047-506a4b0bddac · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
Reference 62
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Observation 035b1fdb-dc96-40b6-aa2e-e4ddf1c81ea2 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ACM Transactions on Database Systems (TODS) , pages=
Reference 63
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Observation 6adf513c-42ee-4652-8ca4-90b192613326 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin GPT-4o System Card
Reference 64
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Observation b8db08b1-f786-44c5-981b-5861db570550 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Reference 65
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Observation 2d548e17-b98a-459e-b572-53109317b777 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Proceedings of the 32nd ACM International Conference on Multimedia , pages=
Reference 66
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Observation 4717ca4d-4ce6-4747-b553-94e6f052838b · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
Reference 67
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Observation 7d630164-4544-449e-8ca2-cd2eb7387854 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 68
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Observation 2bc0c6ba-b6c3-4fb4-a600-100a0fafb2da · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ECCV , year=
Reference 69
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Observation db8c182a-0f84-47e2-98b8-c2c81904cb62 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
Reference 70
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Observation 457f16e9-e01a-42bd-a39e-d79d004b12fc · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Reference 71
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Observation e148b1e9-b912-4325-bbd0-0e0adafa7149 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Reference 72
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Observation 4f4ca747-1fac-483b-b8cc-40afb9b37933 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ICCV , year=
Reference 73
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Observation 289f2c93-275c-42ca-99dd-849180301cb3 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings
Reference 74
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Observation e7414c56-1099-4ff0-8fe4-97423e1612eb · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Qwen3-VL Technical Report
Reference 75
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Observation 34ee7d7b-0e4d-426e-92b7-98060254abd8 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin LLaVA-OneVision: Easy Visual Task Transfer
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Observation 04c5c2e7-9d73-4d01-9880-688927c997e7 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 77
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Observation b7787d07-583c-4a18-81f7-f54d3e940750 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin AAAI , year=
Reference 78
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Observation 3ec38fa5-dacd-434a-b02d-69faf847e68e · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ECCV , year=
Reference 79
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Observation 40a305b0-f104-48b5-866b-d91e4a27bc00 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
Reference 80
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Observation f66abced-ddcb-4954-8520-3e15de978fef · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 81
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Observation caa523ce-41c7-476e-a371-1ba415be5e77 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Seed1.5-VL Technical Report
Reference 82
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Observation 3e278458-c443-4484-aac3-85b23c675074 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin InternVL-X: Advancing and Accelerating InternVL Series with Efficient Visual Token Compression
Reference 83
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Observation f3da428c-2a7a-49f5-ad92-2ef1b917e3e8 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin NeurIPS , year=
Reference 84
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Observation 33aaf4d2-def7-4a77-8995-12df540f570b · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin LLaMA: Open and Efficient Foundation Language Models
Reference 85
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Observation 019cdcc6-3c79-4eb6-aa13-02a89bf8fe65 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin DeepSeek-V3 Technical Report
Reference 86
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Observation a9fe5aea-29c6-4dcd-8398-07cc5861b1cb · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Qwen3 Technical Report
Reference 87
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Observation 4d21e384-dc0c-4128-b777-d704c90f14bb · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin AAAI , year=
Reference 88
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Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin ECCV , year=
Reference 89
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Observation f2c659c3-613d-410a-a1eb-09ec16f00ad9 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin CVPR , year=
Reference 90
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Observation c403b17c-df75-49e6-a8f4-2bb4ad3f6767 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
Reference 91
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Observation 8ec82576-f1d4-4563-a1ce-99ee577f7eab · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling
Reference 92
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Observation 204158c3-e71a-4351-8031-9963114faf95 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Reference 93
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Observation 41a5184a-a68f-454e-a9cd-4422249b0d01 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=
Reference 94
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Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin OneThinker: All-in-one Reasoning Model for Image and Video
Reference 95
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Observation b8dbd646-f81e-45ae-9aed-dae7da1668b8 · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin LearnPruner: Rethinking Attention-based Token Pruning in Vision Language Models
Reference 96
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Observation f5d1790a-cddf-444e-b41d-4f6533f24bba · outbound
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Reference 97
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Observation e7b0d891-b32c-481b-b396-e3e10cc6872e · outbound
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin AAAI , pages=
Reference 98
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Observation 13f1b919-3589-4789-9d9a-62e60ffdf5f7 · outbound
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Reference 99
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Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin arXiv preprint arXiv:2602.23699 , year=
Reference 100
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