RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/Cosmos-Tokenizer

Default branch main · commit 3584ae75 · scanned 5/27/2026, 3:28:38 AM

GitHub: 1,725 stars · 88 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface NVIDIA/Cosmos-Tokenizer, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README H1 to explicitly state visual-only focus

    Why:

    CURRENT
    # Cosmos Tokenizer: A suite of image and video neural tokenizers.
    COPY-PASTE FIX
    # Cosmos Tokenizer: A suite of **visual-only** image and video neural tokenizers.
  • hightopics#2
    Expand topics with specific visual tokenization keywords

    Why:

    CURRENT
    diffusion, tokenization, transformers
    COPY-PASTE FIX
    diffusion, tokenization, transformers, visual-tokenization, image-tokenization, video-tokenization, generative-ai, vae, vq-vae, diffusion-models
  • mediumreadme#3
    Clarify the read-only status and integration with NVIDIA/Cosmos

    Why:

    CURRENT
    As of February 10th, 2025, this repository is **read-only**. <br /> **Please visit github.com/NVIDIA/Cosmos for the latest updates and support on Cosmos Tokenizer**.
    COPY-PASTE FIX
    While this repository is read-only as of February 10th, 2025, it serves as the foundational implementation of Cosmos Tokenizer. For active development, issues, and the latest updates, please refer to the integrated project at github.com/NVIDIA/Cosmos.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface NVIDIA/Cosmos-Tokenizer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. CLIP · recommended 1×
  2. DALL-E's VQ-VAE · recommended 1×
  3. Stable Diffusion's VAE · recommended 1×
  4. VQGAN · recommended 1×
  5. BEiT · recommended 1×
  • CATEGORY QUERY
    Seeking efficient visual tokenization techniques for training large language or diffusion models.
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. DALL-E's VQ-VAE
    3. Stable Diffusion's VAE
    4. VQGAN
    5. BEiT
    6. Masked Autoencoders

    AI recommended 6 alternatives but never named NVIDIA/Cosmos-Tokenizer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools offer robust and scalable image and video tokenization for generative AI?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. TorchVision.Video (pytorch/vision)
    3. TensorFlow Models (tensorflow/models)
    4. OpenCV (opencv/opencv)
    5. MMAction2 (open-mmlab/mmaction2)
    6. DeepMind's Perceiver IO
    7. Facebook's Detectron2 (facebookresearch/detectron2)

    AI recommended 7 alternatives but never named NVIDIA/Cosmos-Tokenizer. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of NVIDIA/Cosmos-Tokenizer?
    pass
    AI named NVIDIA/Cosmos-Tokenizer explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts NVIDIA/Cosmos-Tokenizer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA/Cosmos-Tokenizer explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo NVIDIA/Cosmos-Tokenizer solve, and who is the primary audience?
    pass
    AI named NVIDIA/Cosmos-Tokenizer explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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NVIDIA/Cosmos-Tokenizer — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite