RRepoGEO

REPOGEO REPORT · LITE

ByteDance-Seed/VideoWorld

Default branch main · commit 3dc3e563 · scanned 6/15/2026, 12:58:24 AM

GitHub: 789 stars · 41 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 ByteDance-Seed/VideoWorld, 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 README opening to emphasize generative model for world dynamics

    Why:

    CURRENT
    This repository hosts the **VideoWorld** research project series, exploring how deep generative models can learn complex world knowledge, physics, and dynamics solely from visual inputs.
    COPY-PASTE FIX
    This repository hosts the **VideoWorld** research project series, a collection of deep generative models designed to learn complex world knowledge, physics, and dynamics solely from visual inputs, much like how babies learn by observing their environment.
  • hightopics#2
    Add specific topics for generative AI and world models

    Why:

    CURRENT
    research
    COPY-PASTE FIX
    research, generative-ai, world-models, video-generation, deep-learning, computer-vision, cvpr, reinforcement-learning, latent-dynamics-model
  • mediumabout#3
    Update repository description to reflect project series and future iterations

    Why:

    CURRENT
    [CVPR 2025] VideoWorld is a simple generative model that learns purely from unlabeled videos—much like how babies learn by observing their environment.
    COPY-PASTE FIX
    [CVPR 2025 & 2026] VideoWorld is a research project series exploring generative models that learn complex world dynamics purely from unlabeled videos—much like how babies learn by observing their environment.

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 ByteDance-Seed/VideoWorld
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. PyTorch Lightning · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Diffusers · recommended 1×
  • CATEGORY QUERY
    How can I build AI models that learn complex world dynamics from unlabeled video?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. PyTorch Lightning
    3. Hugging Face Transformers
    4. Diffusers
    5. TensorFlow
    6. Keras
    7. TensorFlow Probability
    8. JAX
    9. Flax
    10. Haiku
    11. OpenCV
    12. FFmpeg
    13. Weights & Biases (W&B)
    14. MLflow
    15. Google Cloud Platform (GCP)
    16. AWS
    17. Azure
    18. Google Compute Engine
    19. Google Cloud Storage
    20. EC2 instances
    21. S3
    22. Azure Virtual Machines
    23. Azure Blob Storage
    24. Kubernetes
    25. GKE
    26. EKS
    27. AKS

    AI recommended 27 alternatives but never named ByteDance-Seed/VideoWorld. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What generative AI frameworks learn physics and game rules solely from observed video data?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch
    3. NeRF
    4. 3D-GANs
    5. GIRAFFE
    6. StyleGAN-XL
    7. tiny-cuda-nn
    8. PredRNN
    9. SVG
    10. PhyDNet
    11. Interaction Networks
    12. Relational Neural Networks
    13. PyTorch Geometric (PyG)
    14. Deep Graph Library (DGL)
    15. DreamerV3
    16. PlaNet

    AI recommended 16 alternatives but never named ByteDance-Seed/VideoWorld. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 ByteDance-Seed/VideoWorld?
    pass
    AI named ByteDance-Seed/VideoWorld explicitly

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

  • If a team adopts ByteDance-Seed/VideoWorld in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ByteDance-Seed/VideoWorld 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 ByteDance-Seed/VideoWorld solve, and who is the primary audience?
    pass
    AI named ByteDance-Seed/VideoWorld explicitly

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

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ByteDance-Seed/VideoWorld — 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