RRepoGEO

REPOGEO REPORT · LITE

Chengsong-Huang/R-Zero

Default branch main · commit 5699329d · scanned 6/1/2026, 12:28:59 PM

GitHub: 807 stars · 78 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 Chengsong-Huang/R-Zero, 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.

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 Chengsong-Huang/R-Zero
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 4×
  2. langchain-ai/langchain · recommended 3×
  3. OpenAI API (GPT-4/GPT-3.5 Turbo) · recommended 2×
  4. run-llama/llama_index · recommended 2×
  5. huggingface/trl · recommended 1×
  • CATEGORY QUERY
    How can I train an LLM to reason effectively without needing large labeled datasets?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API (GPT-4/GPT-3.5 Turbo)
    2. Hugging Face Transformers (huggingface/transformers)
    3. TRL (Transformer Reinforcement Learning) (huggingface/trl)
    4. Anthropic's Constitutional AI (via their API)
    5. OpenAI API (GPT-4/GPT-3.5 Turbo)
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)
    8. LangChain (langchain-ai/langchain)
    9. Hugging Face Transformers (huggingface/transformers)
    10. OpenAI API (Function Calling)
    11. Llama 2 (facebookresearch/llama)
    12. Mistral (mistralai/mistral-src)
    13. Falcon (tiiuae/falcon-7b)
    14. GPT-3.5 Turbo
    15. Hugging Face Transformers (huggingface/transformers)
    16. Prolog/Datalog
    17. Z3 (Z3Prover/z3)

    AI recommended 17 alternatives but never named Chengsong-Huang/R-Zero. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks allow language models to autonomously enhance their reasoning capabilities from scratch?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Hugging Face Transformers (huggingface/transformers)
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. DeepMind's AlphaCode
    7. Google's Minerva
    8. RLlib (ray-project/ray)
    9. Stable Baselines3 (DLR-RM/stable-baselines3)
    10. OpenAI Gym (openai/gym)
    11. Farama Gymnasium (Farama-Foundation/Gymnasium)

    AI recommended 11 alternatives but never named Chengsong-Huang/R-Zero. 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 Chengsong-Huang/R-Zero?
    pass
    AI named Chengsong-Huang/R-Zero explicitly

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

  • If a team adopts Chengsong-Huang/R-Zero in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Chengsong-Huang/R-Zero 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 Chengsong-Huang/R-Zero solve, and who is the primary audience?
    pass
    AI named Chengsong-Huang/R-Zero 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 Chengsong-Huang/R-Zero. 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
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Chengsong-Huang/R-Zero — 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