RRepoGEO

REPOGEO REPORT · LITE

pat-jj/s3

Default branch main · commit ba08d588 · scanned 6/13/2026, 11:32:40 PM

GitHub: 837 stars · 143 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 pat-jj/s3, 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
  • highreadme#1
    Clarify project identity and disambiguate from Amazon S3 in README

    Why:

    COPY-PASTE FIX
    Add a prominent statement at the very beginning of the README, e.g., "Note: This project, `s3`, is a research framework for Efficient & Effective Search Agent Training, and is *not* related to Amazon S3 cloud storage."
  • highabout#2
    Update repository description to explicitly disambiguate from Amazon S3

    Why:

    CURRENT
    [EMNLP'25] s3 - ⚡ Efficient & Effective Search Agent Training via RL for RAG (RLVR for Search with Minimal Data)
    COPY-PASTE FIX
    [EMNLP'25] s3 (Search Agent Training via RL) - ⚡ Efficient & Effective Search Agent Training via RL for RAG (RLVR for Search with Minimal Data). Note: This project is unrelated to Amazon S3.
  • mediumtopics#3
    Add more specific topics related to the project's core problem and methodology

    Why:

    CURRENT
    agentic-ai, efficiency, gpt-5, information-retrieval, large-language-models, rag, rlvr, search-agent, verifier
    COPY-PASTE FIX
    agentic-ai, efficiency, gpt-5, information-retrieval, large-language-models, rag, rlvr, search-agent, verifier, reinforcement-learning-for-rag, minimal-data-training, search-agent-optimization

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 pat-jj/s3
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TRL Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TRL Library · recommended 1×
  2. Ray RLlib · recommended 1×
  3. DeepMind's Acme · recommended 1×
  4. Farama Foundation Gymnasium · recommended 1×
  5. PyTorch-Lightning · recommended 1×
  • CATEGORY QUERY
    How to efficiently train a search agent for RAG using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. TRL Library
    2. Ray RLlib
    3. DeepMind's Acme
    4. Farama Foundation Gymnasium
    5. PyTorch-Lightning
    6. TorchRL

    AI recommended 6 alternatives but never named pat-jj/s3. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods for developing effective search agents with minimal data?
    you: not recommended
    AI recommended (in order):
    1. BERT
    2. RoBERTa
    3. T5
    4. GPT-3
    5. GPT-4
    6. sentence-transformers
    7. all-MiniLM-L6-v2
    8. msmarco-distilbert-base-v4
    9. Hugging Face Transformers
    10. bert-base-uncased
    11. roberta-base
    12. t5-small
    13. OpenAI API
    14. Elasticsearch
    15. OpenSearch
    16. Pinecone
    17. Weaviate
    18. Qdrant
    19. ModAL
    20. scikit-learn
    21. Prodigy
    22. NLPAug
    23. Google Translate API
    24. Claude
    25. Anthropic API

    AI recommended 25 alternatives but never named pat-jj/s3. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 pat-jj/s3?
    pass
    AI named pat-jj/s3 explicitly

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

  • If a team adopts pat-jj/s3 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pat-jj/s3 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 pat-jj/s3 solve, and who is the primary audience?
    pass
    AI named pat-jj/s3 explicitly

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

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pat-jj/s3 — 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