Paper Citation Record · LEDGER

Collaborative Edge-to-Server Inference for Vision-Language Models

As of 23 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2512.16349.

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

pith.paper-citation-record.v1
2512.16349 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:37:51.027208Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T22:24:19.238442Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T09:31:25.570140Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5ea58c87-488c-4205-a8b1-c1e319ab77c6 · outbound

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

Collaborative Edge-to-Server Inference for Vision-Language Models Learning transferable visual models from natural language supervision,

Reference 1

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Observation d9066750-7f8d-4221-a1a2-523081d9a568 · outbound

This paper cites Vision-language mo dels for vision tasks: a survey,.

Collaborative Edge-to-Server Inference for Vision-Language Models Vision-language mo dels for vision tasks: a survey,

Reference 2

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Observation 56450dae-7570-430e-a692-095e6d7667e7 · outbound

This paper cites Improved baselines wit h visual in- struction tuning,.

Collaborative Edge-to-Server Inference for Vision-Language Models Improved baselines wit h visual in- struction tuning,

Reference 3

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Observation 42c01d30-2ed0-4d7b-bbc3-9ee7e4a0f872 · outbound

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

Collaborative Edge-to-Server Inference for Vision-Language Models InstructBLIP: Towards general-purpose vision -language models with instruction tuning,

Reference 4

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Observation b6e5c192-da06-4a1f-9ae8-288abe456ddb · outbound

This paper cites Qwen2.5-VL Technical Report.

Collaborative Edge-to-Server Inference for Vision-Language Models Qwen2.5-VL Technical Report

Reference 5

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Observation 1c72c860-9df8-4854-8425-f0b97404df86 · outbound

This paper cites Vision -language models for edge networks: A comprehensive survey,.

Collaborative Edge-to-Server Inference for Vision-Language Models Vision -language models for edge networks: A comprehensive survey,

Reference 6

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Observation d2773722-48cc-4ffd-9ec6-cca37239e2b1 · outbound

This paper cites Task- oriented feature compression for multimodal understandin g via device- edge co-inference,.

Collaborative Edge-to-Server Inference for Vision-Language Models Task- oriented feature compression for multimodal understandin g via device- edge co-inference,

Reference 7

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Observation f56f103b-8ad4-4950-9da5-d87db867292e · outbound

This paper cites V aVLM: Toward efﬁcient edge-cloud video analytics with vision- language models,.

Collaborative Edge-to-Server Inference for Vision-Language Models V aVLM: Toward efﬁcient edge-cloud video analytics with vision- language models,

Reference 8

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Observation 30a5ef0a-8f78-4bb5-87fa-a756ea80ac59 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recog nition at scale,.

Collaborative Edge-to-Server Inference for Vision-Language Models An image is worth 16x16 words: Transformers for image recog nition at scale,

Reference 9

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Observation 3a121aa3-ad9b-4973-9c11-2fb3f370800f · outbound

This paper cites Sigmo id loss for language image pre-training,.

Collaborative Edge-to-Server Inference for Vision-Language Models Sigmo id loss for language image pre-training,

Reference 10

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Observation de37b8ea-03d5-430e-b5f9-a5b4a6f38f66 · outbound

This paper cites MLLMs know where to look: Training-free perception of small visual det ails with multimodal LLMs,.

Collaborative Edge-to-Server Inference for Vision-Language Models MLLMs know where to look: Training-free perception of small visual det ails with multimodal LLMs,

Reference 11

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Observation b428fb37-831b-47b1-b562-6f7435c7d61a · outbound

This paper cites Reducing activation recompu tation in large transformer models,.

Collaborative Edge-to-Server Inference for Vision-Language Models Reducing activation recompu tation in large transformer models,

Reference 12

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Observation 350f408b-651d-423a-8f85-e12527ce26f5 · outbound

This paper cites The operational meaning of min- and max-entropy,.

Collaborative Edge-to-Server Inference for Vision-Language Models The operational meaning of min- and max-entropy,

Reference 13

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Observation cacec9fc-fe24-44c1-ac2d-e6d980b978cc · outbound

This paper cites A mathematical theory of communication ,.

Collaborative Edge-to-Server Inference for Vision-Language Models A mathematical theory of communication ,

Reference 14

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Observation e9448300-2e82-4258-91bc-c9588a2f0300 · outbound

This paper cites Settles, Active Learning.

Collaborative Edge-to-Server Inference for Vision-Language Models Settles, Active Learning

Reference 15

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Observation 7dc207d2-6bc3-427a-84db-a8e0b31a99b2 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Collaborative Edge-to-Server Inference for Vision-Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 16

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Observation 047ec4f7-9c92-47f9-998e-52b9795a8975 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Collaborative Edge-to-Server Inference for Vision-Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 17

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Observation 761d2c2f-932a-4f52-8e20-8466386296e1 · outbound

This paper cites Vicuna: An open-source chatbot impressing GPT- 4 with 90%* chatGPT quality,.

Collaborative Edge-to-Server Inference for Vision-Language Models Vicuna: An open-source chatbot impressing GPT- 4 with 90%* chatGPT quality,

Reference 18

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Observation f78f37a0-15b2-493e-877d-87c4c8ad5070 · outbound

This paper cites Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural t ext process- ing,.

Collaborative Edge-to-Server Inference for Vision-Language Models Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural t ext process- ing,

Reference 19

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Observation 9d32617a-63db-4c92-a9c9-6f241b7808b8 · outbound

This paper cites Attention is all you need,.

Collaborative Edge-to-Server Inference for Vision-Language Models Attention is all you need,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T15:37:48.420489Z

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Observation 00bca13a-86ba-49ec-bc4e-efd61c307a78 · outbound

This paper cites Attent ion-aware semantic communications for collaborative inference,.

Collaborative Edge-to-Server Inference for Vision-Language Models Attent ion-aware semantic communications for collaborative inference,

Reference 21

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Observation 85c2e35b-2efd-4287-86bf-6a435bd994b7 · outbound

This paper cites Vision transform er-based semantic communications with importance-aware quantizat ion,.

Collaborative Edge-to-Server Inference for Vision-Language Models Vision transform er-based semantic communications with importance-aware quantizat ion,

Reference 22

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Observation bbc4b75a-6b1e-44a6-aaf0-4c945ee002f1 · outbound

This paper cites An image is worth 1/2 tokens after layer 2: Plug-and-play in ference acceleration for large vision-language models,.

Collaborative Edge-to-Server Inference for Vision-Language Models An image is worth 1/2 tokens after layer 2: Plug-and-play in ference acceleration for large vision-language models,

Reference 23

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Observation f383a59d-88a2-4564-9f55-2697344a1350 · outbound

This paper cites Beyond text-visual attention: Exploiting vi sual cues for effective token pruning in VLMs,.

Collaborative Edge-to-Server Inference for Vision-Language Models Beyond text-visual attention: Exploiting vi sual cues for effective token pruning in VLMs,

Reference 24

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Observation fe6f0464-c1b2-441c-8b7f-fba67127f8e8 · outbound

This paper cites Beyond transmitting bits: Conte xt, seman- tics, and task-oriented communications,.

Collaborative Edge-to-Server Inference for Vision-Language Models Beyond transmitting bits: Conte xt, seman- tics, and task-oriented communications,

Reference 25

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Observation 020baf28-dc28-4faf-99c0-af9e82d93e07 · outbound

This paper cites From semantic communi cation to semantic-aware networking: Model, architecture, and open problems,.

Collaborative Edge-to-Server Inference for Vision-Language Models From semantic communi cation to semantic-aware networking: Model, architecture, and open problems,

Reference 26

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This paper cites What is semantic communication? A view on conveyi ng meaning in the era of machine intelligence,.

Collaborative Edge-to-Server Inference for Vision-Language Models What is semantic communication? A view on conveyi ng meaning in the era of machine intelligence,

Reference 27

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Observation ae04d61b-8f46-4e7b-8e63-b15623a64aa6 · outbound

This paper cites Toward semanti c communications: Deep learning-based image semantic codin g,.

Collaborative Edge-to-Server Inference for Vision-Language Models Toward semanti c communications: Deep learning-based image semantic codin g,

Reference 28

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Observation d3b52840-9817-445e-b792-30132a890261 · outbound

This paper cites A lite distributed semantic communic ation system for internet of things,.

Collaborative Edge-to-Server Inference for Vision-Language Models A lite distributed semantic communic ation system for internet of things,

Reference 29

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Observation 6eb50103-bc93-4ac2-9431-80aafecb2708 · outbound

This paper cites A uniﬁed multi- task semantic communication system for multimodal data,.

Collaborative Edge-to-Server Inference for Vision-Language Models A uniﬁed multi- task semantic communication system for multimodal data,

Reference 30

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This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and op en questions,.

Collaborative Edge-to-Server Inference for Vision-Language Models A survey on hallucination in large language models: Principles, taxonomy, challenges, and op en questions,

Reference 31

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This paper cites Fact-checking the output of large la nguage models via token-level uncertainty quantiﬁcation,.

Collaborative Edge-to-Server Inference for Vision-Language Models Fact-checking the output of large la nguage models via token-level uncertainty quantiﬁcation,

Reference 32

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Observation 760c39af-4adb-4b2c-b448-8897b370d55b · outbound

This paper cites Language model cascades: Token-level uncert ainty and beyond,.

Collaborative Edge-to-Server Inference for Vision-Language Models Language model cascades: Token-level uncert ainty and beyond,

Reference 33

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This paper cites On the efﬁcient estimat ion of min- entropy,.

Collaborative Edge-to-Server Inference for Vision-Language Models On the efﬁcient estimat ion of min- entropy,

Reference 34

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This paper cites Towards VQA models that can read,.

Collaborative Edge-to-Server Inference for Vision-Language Models Towards VQA models that can read,

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Collaborative Edge-to-Server Inference for Vision-Language Models Or ca: A distributed serving system for Transformer-based generat ive models,

Reference 36

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This paper cites Taming throughput-latency trad eoff in LLM inference with Sarathi-serve,.

Collaborative Edge-to-Server Inference for Vision-Language Models Taming throughput-latency trad eoff in LLM inference with Sarathi-serve,

Reference 37

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This paper cites PaliGemma: A versatile 3B VLM for transfer.

Collaborative Edge-to-Server Inference for Vision-Language Models PaliGemma: A versatile 3B VLM for transfer

Reference 38

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This paper cites E valuating object hallucination in large vision-language models,.

Collaborative Edge-to-Server Inference for Vision-Language Models E valuating object hallucination in large vision-language models,

Reference 39

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This paper cites A-OKVQA: A benchmark for visual question answering using w orld knowledge,.

Collaborative Edge-to-Server Inference for Vision-Language Models A-OKVQA: A benchmark for visual question answering using w orld knowledge,

Reference 40

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

Collaborative Edge-to-Server Inference for Vision-Language Models GQA: A new dataset for rea l-world visual reasoning and compositional question answering,

Reference 41

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This paper cites Making the V in VQA matter: Elevating the role of image understandin g in visual question answering,.

Collaborative Edge-to-Server Inference for Vision-Language Models Making the V in VQA matter: Elevating the role of image understandin g in visual question answering,

Reference 42

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This paper cites On a measure of divergence between t wo multino- mial populations,.

Collaborative Edge-to-Server Inference for Vision-Language Models On a measure of divergence between t wo multino- mial populations,

Reference 43

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Pith citing papers

Observation 724d9d35-6568-465b-90f8-c266f6616baf · inbound

Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models cites this paper.

Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Collaborative Edge-to-Server Inference for Vision-Language Models

Reference 2

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Observation bef0fc3b-9390-43eb-9614-0282e69eae04 · inbound

LAST: The Last Query Token Guides Visual Token Pruning for Edge-Cloud Collaborative MLLM Inference cites this paper.

LAST: The Last Query Token Guides Visual Token Pruning for Edge-Cloud Collaborative MLLM Inference Collaborative Edge-to-Server Inference for Vision-Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-31T22:24:19.238442Z

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