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Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models

Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models

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AEO Score: 3/10

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varybase.github.io

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About Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models

Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models Haoran Wei1* Lingyu Kong2* Jinyue Chen 2 Liang Zhao 1 Zheng Ge 1 Jinrong Yang 3 Jianjian Sun 1 Chunrui Han 1 Xiangyu Zhang1 1MEGVII Technology 2UCAS 3HUST Paper Demo Code Abstract This paper presents Vary, an efficient and effective method to scale up the Vision vocabulary of LVLMs.

Key Topics

Vary: Scaling up the Vision Vocabulary for
Large Vision-Language Models

Details

Category: Technology

varybase.github.io

AI Visibility Breakdown

1

Structured Data

4

Content Structure

4

Entity Clarity

2

E-E-A-T Signals

5

Technical AEO

2

AI Discoverability

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Exact fixes: robots.txt, schema, llms.txt

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Source & Attribution

Scored by Engagemii on June 9, 2026. Methodology: engagemii.com/aeo/methodology

Source URL: https://engagemii.com/aeo/brands/varybase-github-io

Cite this score: Engagemii (2026). "AEO Score for Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models." Retrieved from https://engagemii.com/aeo/brands/varybase-github-io

Licensed under CC BY 4.0. You may reuse this data with attribution: a visible link to engagemii.com.

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