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Paper Citation Record · LEDGER

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing

As of 22 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2607.08839.

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

pith.paper-citation-record.v1
2607.08839 v1

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measured 51 of 51 reference resolution

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measured 51 of 51 standing notices

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measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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51 of 51 outbound references displayed

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Outbound references

Observation 626f703a-095f-4fcd-92d8-6f8dfa5b23bc · outbound

This paper cites Soundnet: Learning sound representa- tions from unlabeled video.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Soundnet: Learning sound representa- tions from unlabeled video

Reference 1

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Observation e5ae8f13-b760-48b7-9f18-e4bb8da46024 · outbound

This paper cites Qwen2.5-VL Technical Report.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Qwen2.5-VL Technical Report

Reference 2

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Observation a42a5386-8c11-47aa-8e51-808434b3eafa · outbound

This paper cites Paligemma: A versatile 3b vlm for transfer, 2024.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Paligemma: A versatile 3b vlm for transfer, 2024

Reference 3

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Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Unresolved cited work

Reference 4

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Observation a711cd73-0465-40fb-9773-d401b416a485 · outbound

This paper cites X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages

Reference 5

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Observation d080e360-0eac-4be3-b623-c55c4e46301d · outbound

This paper cites Toyota smarthome untrimmed: Real-world untrimmed videos for activity detection.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Toyota smarthome untrimmed: Real-world untrimmed videos for activity detection.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022

Reference 6

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Observation 5ff11fed-02d1-4690-a5a5-af28d5b62a6a · outbound

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

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Instructblip: Towards general-purpose vision-language models with instruction tuning

Reference 7

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Observation b36b497c-6fe4-4e78-8866-dfda9c2fd602 · outbound

This paper cites Modality-specific and modality-general representations of subjective value in frontal cortex.Communications Biology, 7:1550, 2024.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Modality-specific and modality-general representations of subjective value in frontal cortex.Communications Biology, 7:1550, 2024

Reference 8

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Observation a6158c07-6efc-4ec9-94d7-6a0e422f5101 · outbound

This paper cites Toyota smarthome: Real-world activities of daily living.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Toyota smarthome: Real-world activities of daily living

Reference 9

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Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Unresolved cited work

Reference 10

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Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Unresolved cited work

Reference 11

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Observation 01732568-b904-4a37-b855-abad485ba64c · outbound

This paper cites Cross modal distillation for supervision transfer.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Cross modal distillation for supervision transfer

Reference 12

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Observation af776546-44b7-496d-add2-51e05deedf1b · outbound

This paper cites Onellm: One framework to align all modalities with language.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Onellm: One framework to align all modalities with language

Reference 13

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Observation bdcb36fe-bb42-4043-b21b-e1cbb45c799e · outbound

This paper cites Imagebind-llm: Multi-modality instruction tuning.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Imagebind-llm: Multi-modality instruction tuning

Reference 14

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Observation f935827b-1252-4276-82de-137d734d2f1c · outbound

This paper cites Enhanced computer vision with microsoft kinect sensor: A review.IEEE Transactions on Cybernetics, 43(5):1318–1334, 2013.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Enhanced computer vision with microsoft kinect sensor: A review.IEEE Transactions on Cybernetics, 43(5):1318–1334, 2013

Reference 15

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Observation ff52c1fd-2af1-46be-8c76-bd17425c6dbd · outbound

This paper cites Visual prompt tuning.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Visual prompt tuning

Reference 16

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Observation 5e1c56d7-3c1d-4b90-99fc-710c323929a7 · outbound

This paper cites Learning to answer questions in dynamic audio-visual scenarios.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Learning to answer questions in dynamic audio-visual scenarios

Reference 17

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Observation f4a318b7-0649-4984-a69f-6a7abd3abb53 · outbound

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Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Unresolved cited work

Reference 18

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Observation 1a9ad964-6b8f-4907-a408-ed459e0b3ec9 · outbound

This paper cites Video-llava: Learning united visual representation by alignment before projection.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Video-llava: Learning united visual representation by alignment before projection

Reference 19

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Observation 83e4e8e5-f9cf-4c34-8070-bf1c7f657c62 · outbound

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Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Visual instruction tuning

Reference 20

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Observation 4c65beae-391c-4092-8111-7a1769271147 · outbound

This paper cites Unifying distillation and privileged information.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Unifying distillation and privileged information

Reference 21

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Observation 96beb16c-6d58-4486-9589-32eee04feb83 · outbound

This paper cites Pretrained transformers as universal computation engines.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Pretrained transformers as universal computation engines

Reference 22

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Observation f6cd8396-4f70-4826-a7bb-0643e3a81b89 · outbound

This paper cites Smil: Multimodal learning with severely missing modality.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Smil: Multimodal learning with severely missing modality

Reference 23

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Observation 8e84c323-6dcc-4433-b93a-6f4faf148709 · outbound

This paper cites Video-chatgpt: Towards detailed video understanding via large vision and language models.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Video-chatgpt: Towards detailed video understanding via large vision and language models

Reference 24

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Observation 2ac9a716-fe90-4b8a-a993-919a0edcabd6 · outbound

This paper cites Anymal: An efficient and scalable any-modality augmented language model.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Anymal: An efficient and scalable any-modality augmented language model

Reference 25

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Observation 340ee3e5-a208-4ea8-a014-42d4e388dc4a · outbound

This paper cites Taylor, and Florian Nebout.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Taylor, and Florian Nebout

Reference 26

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Observation 365376f4-bc59-4cf6-b717-39a3be9d3163 · outbound

This paper cites Gpt-4o system card, 2024.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Gpt-4o system card, 2024

Reference 27

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Observation dddb8882-7ef5-486e-8988-4b5d37693553 · outbound

This paper cites McDermott, William T.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing McDermott, William T

Reference 28

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Observation b5a8313c-c7ac-43a1-b789-49eab812a745 · outbound

This paper cites X-instructblip: A framework for aligning x-modal instruction-aware representations to llms and emergent cross-modal reasoning.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing X-instructblip: A framework for aligning x-modal instruction-aware representations to llms and emergent cross-modal reasoning

Reference 29

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Observation 23a9d4b9-5904-4dd6-a7bf-9c3c4a2e6d38 · outbound

This paper cites Missrag: Addressing the missing modality challenge in multimodal large language models.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Missrag: Addressing the missing modality challenge in multimodal large language models

Reference 30

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Observation 33074b1e-ca8d-4687-ae4c-406c95357da7 · outbound

This paper cites Qwen2.5 Technical Report.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Qwen2.5 Technical Report

Reference 31

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Observation b71cccaf-52ec-4b9b-9160-cdb225ba875e · outbound

This paper cites Do vision transformers see like convolutional neural networks? InAdvances in Neural Information Processing Systems (NeurIPS), 2021.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Do vision transformers see like convolutional neural networks? InAdvances in Neural Information Processing Systems (NeurIPS), 2021

Reference 32

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Observation 2d8071e2-9198-4a93-b2da-694d5b8e3031 · outbound

This paper cites Llavidal: A large language-vision model for daily activities of living.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Llavidal: A large language-vision model for daily activities of living

Reference 33

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Observation c10bd90e-a26c-4bb9-b954-b2fbbc7f999e · outbound

This paper cites From My View to Yours: Learning Egocentric Cues from Exocentric Video using Privileged Egocentric Supervision.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing From My View to Yours: Learning Egocentric Cues from Exocentric Video using Privileged Egocentric Supervision

Reference 34

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Observation ca84a7c2-2335-45d8-8cc1-9c5ad1f2e077 · outbound

This paper cites Viscop: Visual probing for video domain adaptation of vision language models.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Viscop: Visual probing for video domain adaptation of vision language models

Reference 35

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This paper cites TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding

Reference 36

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Observation 436579f9-fb15-4103-aa2c-c530183e9bda · outbound

This paper cites Pandagpt: One model to instruction-follow them all.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Pandagpt: One model to instruction-follow them all

Reference 37

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Observation 025c9f29-273e-4d73-abec-e5eff61c208e · outbound

This paper cites an unresolved cited work.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Unresolved cited work

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Observation 9ab33f9a-a6c6-4717-b042-fc9e6004c88f · outbound

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

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing LLaMA: Open and Efficient Foundation Language Models

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Observation 14936159-99e8-4181-8f1e-728abba11442 · outbound

This paper cites Learning using privileged information: Similarity control and knowledge transfer.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Learning using privileged information: Similarity control and knowledge transfer

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Observation 0d940a38-29a1-463c-ad07-82105f412342 · outbound

This paper cites Deep multimodal learning with missing modality: A survey.Transactions on Machine Learning Research (TMLR), 2026.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Deep multimodal learning with missing modality: A survey.Transactions on Machine Learning Research (TMLR), 2026

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Observation 2411e2f5-dd31-4654-952c-067e43d15dbf · outbound

This paper cites Qwen2.5-Omni Technical Report.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Qwen2.5-Omni Technical Report

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Observation 0fa61c4f-d2fe-482e-b6c9-c4688e8a05df · outbound

This paper cites The modality focusing hypothesis: Towards understanding crossmodal knowledge distillation.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing The modality focusing hypothesis: Towards understanding crossmodal knowledge distillation

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Observation 3e810d4c-a0ff-4ecd-9349-5fad21b68efd · outbound

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Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Depth anything v2

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Observation 82ca6b6e-400e-4d22-abc4-0c909ef136c6 · outbound

This paper cites mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration

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Observation 24ddc168-f15f-4540-bdd2-b60ee6b1cdb0 · outbound

This paper cites Sigmoid loss for language image pre-training.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Sigmoid loss for language image pre-training

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Observation a3a22237-a628-46fd-8bf5-8fb7a228fc64 · outbound

This paper cites VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

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Observation 1fd7051e-8890-4b0f-bff8-3f46d9b1627a · outbound

This paper cites Video-llama: An instruction-tuned audio-visual language model for video understanding.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Video-llama: An instruction-tuned audio-visual language model for video understanding

Reference 48

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Observation a9ad08bf-df7f-44ad-863a-96d7cb48e4ad · outbound

This paper cites Video-llama: An instruction-tuned audio-visual language model for video understanding.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Video-llama: An instruction-tuned audio-visual language model for video understanding

Reference 49

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Observation d9ed1494-8d8a-42e7-9ef6-8ce8174919a0 · outbound

This paper cites Meta-Transformer: A Unified Framework for Multimodal Learning.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing Meta-Transformer: A Unified Framework for Multimodal Learning

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Observation 2dfae590-14b8-40e3-84e3-e8a20030a4be · outbound

This paper cites ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst.

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst

Reference 51

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