RRepoGEO

REPOGEO REPORT · LITE

hardmaru/WorldModelsExperiments

Default branch master · commit fd982b96 · scanned 6/11/2026, 5:57:38 AM

GitHub: 712 stars · 179 forks

AI VISIBILITY SCORE
30 /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
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 hardmaru/WorldModelsExperiments, 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
  • highreadme#1
    Update repository description and README H1 for precise positioning

    Why:

    CURRENT
    Description: "World Models Experiments"
README H1: "# World Models Experiments"
    COPY-PASTE FIX
    Description: "Official experimental reproduction of the 'Recurrent World Models Facilitate Policy Evolution' (NIPS 2018) paper by Ha & Schmidhuber."
README H1: "# Official Reproduction: Recurrent World Models Facilitate Policy Evolution (NIPS 2018)"
  • highlicense#2
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License) in the repository root.

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 hardmaru/WorldModelsExperiments
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 1×
  2. tensorflow/probability · recommended 1×
  3. pytorch/pytorch · recommended 1×
  4. Lightning-AI/lightning · recommended 1×
  5. google/jax · recommended 1×
  • CATEGORY QUERY
    How can I reproduce research experiments on recurrent world models for policy evolution?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow 2.x (tensorflow/tensorflow)
    2. TensorFlow Probability (tensorflow/probability)
    3. PyTorch (pytorch/pytorch)
    4. PyTorch Lightning (Lightning-AI/lightning)
    5. JAX (google/jax)
    6. Haiku (deepmind/dm-haiku)
    7. Flax (google/flax)
    8. OpenAI Gym (openai/gym)
    9. Farama Foundation Gymnasium (Farama-Foundation/Gymnasium)
    10. Ray RLib (ray-project/ray)
    11. Weights & Biases
    12. MLflow (mlflow/mlflow)
    13. Docker
    14. Singularity (sylabs/singularity)

    AI recommended 14 alternatives but never named hardmaru/WorldModelsExperiments. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good examples of implementing model-based reinforcement learning with generative models?
    you: not recommended
    AI recommended (in order):
    1. DreamerV3
    2. PlaNet (Planning Network)
    3. World Models (Ha and Schmidhuber, 2018)
    4. MuZero
    5. MBPO (Model-Based Policy Optimization)
    6. SLBO (Stochastic Latent-space Bayesian Optimization)

    AI recommended 6 alternatives but never named hardmaru/WorldModelsExperiments. 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 hardmaru/WorldModelsExperiments?
    pass
    AI named hardmaru/WorldModelsExperiments explicitly

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

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

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

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hardmaru/WorldModelsExperiments — 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