RRepoGEO

REPOGEO REPORT · LITE

ChenxinAn-fdu/POLARIS

Default branch main · commit eda12fe2 · scanned 6/3/2026, 12:13:20 AM

GitHub: 680 stars · 41 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 ChenxinAn-fdu/POLARIS, 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 ChenxinAn-fdu/POLARIS
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 3 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 3×
  2. ray-project/ray · recommended 3×
  3. microsoft/DeepSpeed · recommended 3×
  4. pytorch/pytorch · recommended 2×
  5. huggingface/trl · recommended 1×
  • CATEGORY QUERY
    How can I enhance pre-trained models for complex reasoning tasks using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. TRL (Transformer Reinforcement Learning) Library (huggingface/trl)
    3. DeepMind's AlphaCode
    4. AlphaZero
    5. JAX (google/jax)
    6. TensorFlow (tensorflow/tensorflow)
    7. RLlib (ray-project/ray)
    8. Ray (ray-project/ray)
    9. Gymnasium (Farama-Foundation/Gymnasium)
    10. OpenAI Gym
    11. PyTorch (pytorch/pytorch)
    12. Stable Baselines3 (DLR-RM/stable-baselines3)
    13. Microsoft's DeepSpeed-MII (microsoft/DeepSpeed)
    14. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 14 alternatives but never named ChenxinAn-fdu/POLARIS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help scale reinforcement learning for post-training optimization of reasoning models?
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib (ray-project/ray)
    2. DeepMind Acme (deepmind/acme)
    3. Hugging Face Transformers (huggingface/transformers)
    4. Accelerate (huggingface/accelerate)
    5. Trainer (huggingface/transformers)
    6. PyTorch FSDP (pytorch/pytorch)
    7. Microsoft DeepSpeed (microsoft/DeepSpeed)
    8. Kubernetes (kubernetes/kubernetes)
    9. Kubeflow (kubeflow/kubeflow)
    10. MLflow (mlflow/mlflow)
    11. Google Cloud Vertex AI
    12. AWS SageMaker
    13. Azure Machine Learning

    AI recommended 13 alternatives but never named ChenxinAn-fdu/POLARIS. 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 ChenxinAn-fdu/POLARIS?
    pass
    AI named ChenxinAn-fdu/POLARIS explicitly

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

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