RRepoGEO

REPOGEO REPORT · LITE

gaoyuezhou/dino_wm

Default branch main · commit 0a9492fa · scanned 6/15/2026, 6:38:43 AM

GitHub: 502 stars · 67 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 gaoyuezhou/dino_wm, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    DINO-WM is a framework for training world models using pre-trained visual features, enabling zero-shot planning in robotics and control tasks.
  • mediumhomepage#2
    Add the project website to the repository's About section

    Why:

    COPY-PASTE FIX
    https://dino-wm.github.io/

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 gaoyuezhou/dino_wm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenVLA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenVLA · recommended 1×
  2. RT-X · recommended 1×
  3. CLIP · recommended 1×
  4. Diffusion Policy · recommended 1×
  5. ACT (Action Chunking with Transformers) · recommended 1×
  • CATEGORY QUERY
    How to achieve zero-shot planning in robotics using advanced visual features?
    you: not recommended
    AI recommended (in order):
    1. OpenVLA
    2. RT-X
    3. CLIP
    4. Diffusion Policy
    5. ACT (Action Chunking with Transformers)
    6. Perceiver IO
    7. Gato
    8. DINOv2
    9. Segment Anything Model (SAM)
    10. Robotics Transformer 2 (RT-2)
    11. PaLM-E
    12. OWL-ViT
    13. PDDL
    14. Behavior Tree

    AI recommended 14 alternatives but never named gaoyuezhou/dino_wm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for training world models with pre-trained visual encoders for control tasks.
    you: not recommended
    AI recommended (in order):
    1. DreamerV3
    2. PlaNet
    3. DayDreamer
    4. MBPO
    5. MuZero
    6. IRL
    7. SimPLe

    AI recommended 7 alternatives but never named gaoyuezhou/dino_wm. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 gaoyuezhou/dino_wm?
    pass
    AI did not name gaoyuezhou/dino_wm — likely talking about a different project

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

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

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

Embed your GEO score

Drop this badge into the README of gaoyuezhou/dino_wm. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/gaoyuezhou/dino_wm"><img src="https://repogeo.com/badge/gaoyuezhou/dino_wm.svg" alt="RepoGEO" /></a>
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gaoyuezhou/dino_wm — 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