Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-21T14:48:21.212088Z
Paper Citation Record · LEDGER
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models
As of 5 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2602.01167.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T22:13:46.282097Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
59 of 59 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 8e256846-dff4-4431-bdd5-02fbffa258d4 · outbound
This paper cites De- tecting and pruning prominent but detrimental neurons in large language models.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models De- tecting and pruning prominent but detrimental neurons in large language models
Reference 1
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Observation c96677e8-3beb-44cb-99af-a070f8588c4f · outbound
This paper cites Data- efficient learning via minimizing hyperspherical energy.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Data- efficient learning via minimizing hyperspherical energy
Reference 2
- Resolution
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Observation 58eb1595-376c-487c-a5f3-276de9e18f15 · outbound
This paper cites Mentored learning: Improving general- ization and convergence of student learner.Journal of Ma- chine Learning Research, 25(325):1–45.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Mentored learning: Improving general- ization and convergence of student learner.Journal of Ma- chine Learning Research, 25(325):1–45
Reference 3
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Observation 49fae418-09a6-4f36-8528-907aaa0b3831 · outbound
This paper cites Are we on the right way for evaluating large vision-language models? InNeurIPS 2024.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Are we on the right way for evaluating large vision-language models? InNeurIPS 2024
Reference 4
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Observation 0d89b0d5-9685-4935-a324-7e30f1618b49 · outbound
This paper cites Attribution analysis meets model editing: Advancing knowledge correction in vision language models with visedit.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Attribution analysis meets model editing: Advancing knowledge correction in vision language models with visedit
Reference 5
- Resolution
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Observation 4933708d-c02e-45ae-bdba-3873e5bbaf5f · outbound
This paper cites Bring reason to vision: Understanding perception and reasoning through model merging.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Bring reason to vision: Understanding perception and reasoning through model merging
Reference 6
- Resolution
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Observation feb907d2-ce00-4e25-a0a9-2dfd50b3b2d1 · outbound
This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks
Reference 7
- Resolution
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Source-reported events for the cited work
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Observation 91f1496f-e4bf-4813-9dc3-6d25bc5fa35f · outbound
This paper cites A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations.IEEE Trans.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations.IEEE Trans
Reference 8
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Observation f2cdee18-2ae8-4103-949d-0ba7e531606b · outbound
This paper cites Knowledge neurons in pretrained transform- ers.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Knowledge neurons in pretrained transform- ers
Reference 9
- Resolution
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Source-reported events for the cited work
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Observation 723de82a-572f-4a49-9b65-7410d50700c4 · outbound
This paper cites Editing factual knowledge in language models.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Editing factual knowledge in language models
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation 29de806f-a3d9-4696-982c-837cb84cc05d · outbound
This paper cites Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation 7645cd3c-ea8a-4bd9-ab6c-3c2cfd3ad47d · outbound
This paper cites Vlmevalkit: An open-source toolkit for evaluating large multi-modality models.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Vlmevalkit: An open-source toolkit for evaluating large multi-modality models
Reference 12
- Resolution
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Source-reported events for the cited work
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Observation 95472c66-1c38-422f-93ad-121ff81417d4 · outbound
This paper cites Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation 67001096-e166-4291-a8f6-01b36dccd459 · outbound
This paper cites Diverse data augmentation with diffusions for effective test-time prompt tuning.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Diverse data augmentation with diffusions for effective test-time prompt tuning
Reference 14
- Resolution
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This paper cites an unresolved cited work.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Unresolved cited work
Reference 15
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Observation 8652a24b-77a8-4ec2-842c-87a0e3881587 · outbound
This paper cites Vlm-auto: Vlm-based autonomous driving assistant with human-like behavior and understanding for complex road scenes.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Vlm-auto: Vlm-based autonomous driving assistant with human-like behavior and understanding for complex road scenes
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation 8c6d17ff-f74f-4141-8853-001c2cb85fbd · outbound
This paper cites Channel pruning for accelerating very deep neural networks.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Channel pruning for accelerating very deep neural networks
Reference 17
- Resolution
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This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen
Reference 18
- Resolution
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Source-reported events for the cited work
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Observation 755582c9-ff13-4f2d-8ad2-0e179d43b70a · outbound
This paper cites Lan- guage is not all you need: Aligning perception with language models.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Lan- guage is not all you need: Aligning perception with language models
Reference 19
- Resolution
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Observation 00928416-5345-4c5e-bdad-8c6ea9b2c305 · outbound
This paper cites Editing models with task arithmetic.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Editing models with task arithmetic
Reference 20
- Resolution
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Observation fabacff6-7b1f-409a-94b8-2e3b2d53ac48 · outbound
This paper cites Test-time classifier ad- justment module for model-agnostic domain generalization.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Test-time classifier ad- justment module for model-agnostic domain generalization
Reference 21
- Resolution
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Source-reported events for the cited work
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Observation e79b62a0-6a3d-4a1b-a7b9-c8b3b1a452c3 · outbound
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Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Unresolved cited work
Reference 22
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Observation 111ba3cc-f79b-4382-8f06-492146070afe · outbound
This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Reference 23
- Resolution
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Observation ae46d09c-919e-42c9-bdfc-8419e9c14e9d · outbound
This paper cites Llava-next: Stronger llms supercharge multimodal capa- bilities in the wild.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Llava-next: Stronger llms supercharge multimodal capa- bilities in the wild
Reference 24
- Resolution
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Source-reported events for the cited work
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Observation 1845f742-e58e-437a-a484-737a68f778ba · outbound
This paper cites Llava-med: Training a large language- and-vision assistant for biomedicine in one day.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Llava-med: Training a large language- and-vision assistant for biomedicine in one day
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f3008b76-9d85-408f-ab25-25b9baed2a81 · outbound
This paper cites HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
Reference 26
- Resolution
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Observation cd487b2e-86b0-40bb-bd38-15fb83e9df3c · outbound
This paper cites Mmbench: Is your multi-modal model an all-around player? InECCV , 2024, pages 216–233.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Mmbench: Is your multi-modal model an all-around player? InECCV , 2024, pages 216–233
Reference 27
- Resolution
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Source-reported events for the cited work
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Observation c7db26c9-5056-40e5-8fd5-1743befef51e · outbound
This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Learn to explain: Multimodal reasoning via thought chains for science question answering
Reference 28
- Resolution
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Source-reported events for the cited work
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Observation 93cb6101-c0ee-43a8-9c9e-8fce16b6a655 · outbound
This paper cites Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts
Reference 29
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Observation 3586dca3-d93e-4ad0-898f-22f01eecefb7 · outbound
This paper cites An image enhancing pattern-based sparsity for real-time inference on mobile de- vices.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models An image enhancing pattern-based sparsity for real-time inference on mobile de- vices
Reference 30
- Resolution
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Observation 3ffd9718-0183-4238-834e-a020980f6552 · outbound
This paper cites Shortgpt: Layers in large language mod- els are more redundant than you expect.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Shortgpt: Layers in large language mod- els are more redundant than you expect
Reference 31
- Resolution
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Observation e5b59a94-c7f9-4ce4-84e5-06d951947781 · outbound
This paper cites Locating and editing factual associations in GPT.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Locating and editing factual associations in GPT
Reference 32
- Resolution
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Observation d35e4da6-aa4a-472d-83b2-d2b62daa3150 · outbound
This paper cites Andonian, Yonatan Belinkov, and David Bau.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Andonian, Yonatan Belinkov, and David Bau
Reference 33
- Resolution
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Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Unresolved cited work
Reference 34
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This paper cites Manning, and Chelsea Finn.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Manning, and Chelsea Finn
Reference 35
- Resolution
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Observation 1e4fa39a-2790-47f8-8fce-ca2e4777ac9f · outbound
This paper cites Compact language models via pruning and knowledge distil- lation.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Compact language models via pruning and knowledge distil- lation
Reference 36
- Resolution
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Source-reported events for the cited work
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Observation 02c48d46-e9a9-4f56-b991-49b3fb21e68b · outbound
This paper cites Controlling text-to-image diffusion by orthogo- nal finetuning.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Controlling text-to-image diffusion by orthogo- nal finetuning
Reference 37
- Resolution
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Source-reported events for the cited work
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Observation b4624248-5400-40ff-a917-a26a9bd8a4ab · outbound
This paper cites Improving robustness against common corruptions by covariate shift adaptation.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Improving robustness against common corruptions by covariate shift adaptation
Reference 38
- Resolution
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Source-reported events for the cited work
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This paper cites Test- time prompt tuning for zero-shot generalization in vision- language models.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Test- time prompt tuning for zero-shot generalization in vision- language models
Reference 39
- Resolution
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Source-reported events for the cited work
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Observation f57a7ca0-7c11-4e34-b4eb-608d48aa0a9a · outbound
This paper cites A deeper look at depth pruning of LLMs.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models A deeper look at depth pruning of LLMs
Reference 40
- Resolution
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Source-reported events for the cited work
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Observation 29f3caf5-d09b-4f6b-ac56-66518581df91 · outbound
This paper cites Drivelm: Driving with graph visual question answering.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Drivelm: Driving with graph visual question answering
Reference 41
- Resolution
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Source-reported events for the cited work
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Observation db6d035d-ca52-4b5b-adc9-97d1de06d9f6 · outbound
This paper cites Transformer- squared: Self-adaptive llms.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Transformer- squared: Self-adaptive llms
Reference 42
- Resolution
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Source-reported events for the cited work
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Observation d0c8cb0f-8457-415e-8c8c-78707b6af251 · outbound
This paper cites The curse of depth in large language models.arXiv preprint arXiv:2502.05795.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models The curse of depth in large language models.arXiv preprint arXiv:2502.05795
Reference 43
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 92c73a0f-93b7-4935-934f-af5af38f8c34 · outbound
This paper cites Efros, and Moritz Hardt.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Efros, and Moritz Hardt
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.630324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7845a941-1f6d-4c67-8fb6-5455cd6adce1 · outbound
This paper cites Docllm: A layout-aware genera- tive language model for multimodal document understand- ing.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Docllm: A layout-aware genera- tive language model for multimodal document understand- ing
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.612098Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f5db06e1-ee93-4c0b-b4df-abed45cac9f6 · outbound
This paper cites Ol- shausen, and Trevor Darrell.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Ol- shausen, and Trevor Darrell
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.615060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 774b1aa7-8702-43f4-a0a1-a8a4e67a1376 · outbound
This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
Reference 47
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T14:50:15.304660Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 30ce3ee4-fcca-41fb-87e9-ef67150a4784 · outbound
This paper cites Skipnet: Learning dynamic routing in convolutional networks.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Skipnet: Learning dynamic routing in convolutional networks
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.589952Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 65f23d01-4758-4d49-9f57-3dc8f6163b1d · outbound
This paper cites Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T14:50:15.300489Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 5167df3d-40d2-4d34-b668-173c6245b386 · outbound
This paper cites Medical large vision language models with multi-image visual ability.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Medical large vision language models with multi-image visual ability
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.617969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d4dfdb2a-a067-41d4-9df6-a711da0f0fa0 · outbound
This paper cites an unresolved cited work.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Unresolved cited work
Reference 51
- Resolution
- unresolved
- raw_fallback, observed 2026-05-21T14:50:15.592890Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 3dcb079a-d6fc-4748-85d1-dbe130a39ea4 · outbound
This paper cites Outlier weighed layerwise spar- sity (OWL): A missing secret sauce for pruning llms to high sparsity.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Outlier weighed layerwise spar- sity (OWL): A missing secret sauce for pruning llms to high sparsity
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.541354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f7a3b58b-5796-4679-a592-9396afa4b8f8 · outbound
This paper cites A survey on multimodal large language models.National Science Review, 11(12).
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models A survey on multimodal large language models.National Science Review, 11(12)
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.599274Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation de194693-8016-496f-8729-b5a6d0a5a29f · outbound
This paper cites Mmmu: A massive multi-discipline multimodal understand- ing and reasoning benchmark for expert agi.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Mmmu: A massive multi-discipline multimodal understand- ing and reasoning benchmark for expert agi
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.623934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 73529d9d-c114-4160-9f73-3ea1efc7f7f2 · outbound
This paper cites MEMO: test time robustness via adaptation and augmentation.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models MEMO: test time robustness via adaptation and augmentation
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.520044Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 5ca77f49-4e61-482f-9122-d6d2d4df8ec9 · outbound
This paper cites Investigat- ing layer importance in large language models.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Investigat- ing layer importance in large language models
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.608638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 385cbaf2-4b61-46d3-baba-663a457b67f8 · outbound
This paper cites SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T14:50:15.327483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 6db2e6a4-a763-458a-8eea-b0d254225f92 · outbound
This paper cites Regularized mask tuning: Uncovering hidden knowledge in pre-trained vision- language models.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Regularized mask tuning: Uncovering hidden knowledge in pre-trained vision- language models
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T14:50:15.582906Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1a48d3eb-85ab-4726-b221-b0ab7ddf13d0 · outbound
This paper cites Modifying Memories in Transformer Models.
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Modifying Memories in Transformer Models
Reference 59
- Resolution
- malformed identifier
- arxiv_id, observed 2026-05-21T14:50:15.331562Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Pith citing papers
Observation 090bd470-abce-467d-8e05-fbfa90e6a56d · inbound
Von Mises-Fisher Mixture Model with Dynamic Shrinkage for Realistic Test-Time Transduction cites this paper.
Von Mises-Fisher Mixture Model with Dynamic Shrinkage for Realistic Test-Time Transduction Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T22:13:46.282097Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.