RRepoGEO

REPOGEO REPORT · LITE

eureka-research/Eureka

Default branch main · commit 9eee4280 · scanned 5/24/2026, 8:27:58 AM

GitHub: 3,162 stars · 298 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 eureka-research/Eureka, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    reinforcement-learning, llm, reward-design, robotics, code-generation, iclr-2024, deep-learning, ai-research
  • highreadme#2
    Reposition the README's opening statement to clearly define Eureka's purpose

    Why:

    CURRENT
    # Eureka: Human-Level Reward Design via Coding Large Language Models (ICLR 2024)
    COPY-PASTE FIX
    # Eureka: Human-Level Reward Design via Coding Large Language Models (ICLR 2024)

Eureka is an innovative algorithm that leverages Large Language Models (LLMs) to automatically design and optimize human-level reward functions for complex reinforcement learning tasks, especially in robotics.
  • mediumabout#3
    Refine the 'About' description for clarity and keyword richness

    Why:

    CURRENT
    Official Repository for "Eureka: Human-Level Reward Design via Coding Large Language Models" (ICLR 2024)
    COPY-PASTE FIX
    Eureka: An ICLR 2024 algorithm leveraging LLMs to automatically design human-level reward functions for complex reinforcement learning and robotic control tasks.

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 eureka-research/Eureka
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. GPT-4 · recommended 1×
  3. Claude 3 Opus · recommended 1×
  4. Anthropic API · recommended 1×
  5. Stable Baselines3 · recommended 1×
  • CATEGORY QUERY
    How to automatically design effective reward functions for complex reinforcement learning tasks using LLMs?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. OpenAI API
    4. Anthropic API
    5. Stable Baselines3
    6. Ray RLlib
    7. Gymnasium
    8. MuJoCo
    9. DEAP
    10. PyGAD

    AI recommended 10 alternatives but never named eureka-research/Eureka. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for leveraging large language models to generate and optimize reward code for robotic control?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Hugging Face Transformers (huggingface/transformers)
    3. LangChain (langchain-ai/langchain)
    4. LlamaIndex (run-llama/llama_index)
    5. Google Gemini API
    6. GitHub Copilot
    7. Code Llama (facebookresearch/codellama)

    AI recommended 7 alternatives but never named eureka-research/Eureka. 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 eureka-research/Eureka?
    pass
    AI named eureka-research/Eureka explicitly

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

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

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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