Paper Citation Record · LEDGER

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks

As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2508.01805.

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

pith.paper-citation-record.v1
2508.01805 v1

Coverage vector

measured 44 of 44 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T05:28:31.007992Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 58c4b7eb-5136-45ed-9d07-f7ef1e6eed79 · outbound

This paper cites Attention is all you need,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Attention is all you need,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:37.887978Z

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Observation ed321ea6-c919-4db1-908b-82444a3d586c · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Flamingo: a visual language model for few-shot learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:37.678178Z

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Observation b15e45c5-ffd7-4bc5-a1bc-8c6eecc18dd3 · outbound

This paper cites BLIP-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks BLIP-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:37.468973Z

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Observation 77a02c35-d8e1-46ea-aab5-a0461c5db666 · outbound

This paper cites Visual instruction tuning,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Visual instruction tuning,

Reference 4

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Observation d7c8ea46-75e0-40fd-a5c4-67bee9205e83 · outbound

This paper cites GPT-4V(ision) system card,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks GPT-4V(ision) system card,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:36.901875Z

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Observation a7de76dd-a279-4fba-bad9-47a991aed3c7 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Gemini: A Family of Highly Capable Multimodal Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T05:28:27.682913Z

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Observation 3a6a6579-913a-4db8-8c3f-40291285f713 · outbound

This paper cites Multimodal Large Language Models: A Survey.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Multimodal Large Language Models: A Survey

Reference 7

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Observation 4b7ad9f2-723d-495f-8c70-bbbcceb3df7e · outbound

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

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Learning transferable visual models from natural language supervision,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:36.663757Z

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Observation 1c579584-1d74-401b-bf4d-c766e6744607 · outbound

This paper cites GPipe: Efficient training of giant neural networks using pipeline parallelism,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks GPipe: Efficient training of giant neural networks using pipeline parallelism,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:36.382048Z

Source-reported events for the cited work

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Observation 2fc2a36b-fc36-4aed-bc34-11bb0d7e18c6 · outbound

This paper cites Are we ready for autonomous driving? The KITTI vision benchmark suite,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Are we ready for autonomous driving? The KITTI vision benchmark suite,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:36.161539Z

Source-reported events for the cited work

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Observation e968f2ef-eb65-473b-a658-f297bfcc937b · outbound

This paper cites CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:35.979321Z

Source-reported events for the cited work

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Observation 4693fc8e-b960-4e65-a0da-3112d475814f · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks On the Opportunities and Risks of Foundation Models

Reference 12

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Observation ded83ce8-1ede-430b-8f3a-0071dfc61015 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:35.696692Z

Source-reported events for the cited work

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Observation 6dbc7e4e-478f-414e-ac8f-4d80661a55fa · outbound

This paper cites MoVA: Adapting Mixture of Vision Experts to Multimodal Context.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks MoVA: Adapting Mixture of Vision Experts to Multimodal Context

Reference 14

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Observation fc9e6c91-c1ad-4e8a-853f-ccccfda0095e · outbound

This paper cites MoE-LLaVA: Mixture of Experts for Large Vision-Language Models.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Reference 15

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Observation 800b636e-a3e2-47da-b929-62c22976b28c · outbound

This paper cites Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts

Reference 16

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Observation 5053dd87-4df4-425f-a960-ea5c9efa6517 · outbound

This paper cites SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

Reference 17

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Observation a1c95fab-a0bd-4c02-bdee-1baf6f6db446 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:35.498999Z

Source-reported events for the cited work

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Observation 7f590711-5ed2-4868-9ad3-f1c73bf8941a · outbound

This paper cites Semantic communi- cations for future Internet: Fundamentals, applications, and challenges,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Semantic communi- cations for future Internet: Fundamentals, applications, and challenges,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:35.259834Z

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Observation 7d2684f1-2d5e-4d65-bbb2-20e441305abc · outbound

This paper cites Model Context Protocol: An open standard for connecting AI assistants to the world,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Model Context Protocol: An open standard for connecting AI assistants to the world,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:35.048642Z

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Observation 2a38a444-a312-4dcd-892d-59ae20333ce1 · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:34.758087Z

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Observation cce9d416-f28e-4688-b77b-f1004b543d4d · outbound

This paper cites Variational inference: A review for statisticians,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Variational inference: A review for statisticians,

Reference 22

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Reference 23

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Observation 8d491f23-dcb5-4121-b3bb-c2effb726810 · outbound

This paper cites Goldsmith, Wireless Communications.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Goldsmith, Wireless Communications

Reference 24

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M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Correlation model for shadow fading in mobile radio systems,

Reference 25

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Observation b0baa3cc-65b8-466e-955f-beb4bfd46632 · outbound

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Reference 26

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Observation 36a5f0af-7e02-4208-ac75-e46934ddb293 · outbound

This paper cites Billion-scale similarity search with GPUs,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Billion-scale similarity search with GPUs,

Reference 27

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This paper cites Design of coherence- aware channel indication and prediction for rate adaptation,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Design of coherence- aware channel indication and prediction for rate adaptation,

Reference 28

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This paper cites Bayesian Forecasting and Dynamic Models,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Bayesian Forecasting and Dynamic Models,

Reference 29

Resolution: verified exact; doi, observed 2026-08-06T05:28:31.477624Z

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Observation 28e9d839-f3f2-4cc9-8531-de9553b01419 · outbound

This paper cites Exact Expressions for Kullback–Leibler Divergence for Univariate Distributions,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Exact Expressions for Kullback–Leibler Divergence for Univariate Distributions,

Reference 30

Resolution: verified exact; doi, observed 2026-08-06T05:28:31.241572Z

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Observation f56c7da4-9cd1-4f91-9c7f-8c997624180d · outbound

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

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 31

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Source-reported events for the cited work

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Observation a31d8df1-eb42-4528-9056-a0e2f1567566 · outbound

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

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Learn to explain: Multimodal reasoning via thought chains for science question answering,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:33.966178Z

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Observation 95778c97-8116-42e5-b4f8-c7e2cab0d1e7 · outbound

This paper cites EdgeViT: Efficient visual modeling for edge computing,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks EdgeViT: Efficient visual modeling for edge computing,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:33.744255Z

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Observation 08aff055-873e-4ef2-bb1c-568401557bfb · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks DINOv2: Learning Robust Visual Features without Supervision

Reference 34

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This paper cites Carion, F.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Carion, F

Reference 35

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This paper cites ”Segment anything.” Proceedings of the IEEE/CVF international conference on computer vision.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks ”Segment anything.” Proceedings of the IEEE/CVF international conference on computer vision

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:33.272765Z

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Observation ce69559f-c102-46ab-b8e5-a147c4252005 · outbound

This paper cites ”Pix2struct: Screenshot parsing as pretraining for visual language understanding.” International Conference on Machine Learning.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks ”Pix2struct: Screenshot parsing as pretraining for visual language understanding.” International Conference on Machine Learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:33.075370Z

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Observation be705dd5-c18e-4b77-9ace-73c05ce9de7e · outbound

This paper cites DePlot: One-shot visual language reasoning by plot-to-table translation,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks DePlot: One-shot visual language reasoning by plot-to-table translation,

Reference 38

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Observation 2746403a-70de-4f48-8073-6af1059634fe · outbound

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M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Unresolved cited work

Reference 39

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Observation 50dad1d6-a554-4ca9-a40a-e9e7f5189ced · outbound

This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 40

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Observation 6da73563-e187-441f-8a2f-5f71241f4148 · outbound

This paper cites MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T05:28:30.709973Z

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M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Resource manage- ment with deep reinforcement learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:32.621476Z

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Observation c0ffb6cc-622d-4746-a4ae-1b65ca98c86e · outbound

This paper cites Human-level control through deep reinforcement learning,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Human-level control through deep reinforcement learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:32.486135Z

Source-reported events for the cited work

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Observation 46009e0b-efe7-40a3-a11a-4b523ee682f1 · outbound

This paper cites Adaptive computation time for recurrent neural networks,.

M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks Adaptive computation time for recurrent neural networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:28:32.290618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.