RRepoGEO

REPOGEO REPORT · LITE

LMD0311/Awesome-World-Model

Default branch main · commit f78487c9 · scanned 5/16/2026, 6:47:52 AM

GitHub: 2,043 stars · 80 forks

AI VISIBILITY SCORE
22 /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
1 / 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 LMD0311/Awesome-World-Model, 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
  • highabout#1
    Clarify the 'About' description to explicitly state it's an 'awesome list' of papers

    Why:

    CURRENT
    Collect some World Models for Autonomous Driving (and Robotic, etc.) papers.
    COPY-PASTE FIX
    An awesome list and curated collection of World Model papers for Autonomous Driving, Robotics, and related fields, supplementing our comprehensive survey.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the MIT License text. (e.g., copy from https://opensource.org/licenses/MIT)
  • mediumtopics#3
    Add the 'awesome-list' topic to the repository

    Why:

    CURRENT
    artificial-intelligence, artificial-intelligence-algorithms, autonomous-driving, autonomous-vehicles, awesome, computer-vision, deep-learning, future-predict, robotics, world-model
    COPY-PASTE FIX
    artificial-intelligence, artificial-intelligence-algorithms, autonomous-driving, autonomous-vehicles, awesome, awesome-list, computer-vision, deep-learning, future-predict, robotics, world-model

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 LMD0311/Awesome-World-Model
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Vision Transformer (ViT)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Vision Transformer (ViT) · recommended 1×
  2. Perceiver IO · recommended 1×
  3. DETR · recommended 1×
  4. Motion Transformer (MTF) · recommended 1×
  5. Scene Transformer · recommended 1×
  • CATEGORY QUERY
    What are the best deep learning models for predicting future states in autonomous driving?
    you: not recommended
    AI recommended (in order):
    1. Vision Transformer (ViT)
    2. Perceiver IO
    3. DETR
    4. Motion Transformer (MTF)
    5. Scene Transformer
    6. Interaction-aware Trajectory Predictor (ITP)
    7. VectorNet
    8. LaneGCN
    9. LSTM
    10. GRU
    11. Social LSTM
    12. TrajGRU
    13. ConvLSTM
    14. PredRNN
    15. ST-GCN
    16. Social GAN
    17. PECNet
    18. MTP
    19. TNT

    AI recommended 19 alternatives but never named LMD0311/Awesome-World-Model. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive overview of world models used in robotics and self-driving?
    you: not recommended
    AI recommended (in order):
    1. DreamerV3 (danijar/dreamerv3)
    2. PlaNet (danijar/dreamer)

    AI recommended 2 alternatives but never named LMD0311/Awesome-World-Model. 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 LMD0311/Awesome-World-Model?
    pass
    AI did not name LMD0311/Awesome-World-Model — 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 LMD0311/Awesome-World-Model in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LMD0311/Awesome-World-Model 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 LMD0311/Awesome-World-Model solve, and who is the primary audience?
    pass
    AI did not name LMD0311/Awesome-World-Model — 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?

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LMD0311/Awesome-World-Model — 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