RRepoGEO

REPOGEO REPORT · LITE

simplescaling/s1

Default branch main · commit 77272c6e · scanned 5/31/2026, 9:53:27 AM

GitHub: 6,658 stars · 757 forks

AI VISIBILITY SCORE
28 /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
2 / 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 simplescaling/s1, 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
    Reposition the README's opening sentence to explicitly state its LLM domain

    Why:

    CURRENT
    Minimal recipe for test-time scaling and strong reasoning performance matching o1-preview with just 1,000 examples & budget forcing
    COPY-PASTE FIX
    s1 provides a minimal recipe for test-time scaling in Large Language Models (LLMs), achieving strong reasoning performance matching o1-preview with just 1,000 examples & budget forcing.
  • hightopics#2
    Add relevant topics to clearly signal the repository's domain

    Why:

    COPY-PASTE FIX
    llm, large-language-models, test-time-scaling, reasoning, nlp, machine-learning, ai
  • mediumreadme#3
    Add a dedicated section to the README explaining s1's core differentiator

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., `## Why s1?` or `## Comparison to other LLM Scaling Methods`, and elaborate on how `s1` stands out, for example: 's1 offers a uniquely minimal and budget-efficient approach to test-time scaling for LLMs, achieving strong reasoning performance with significantly fewer examples (1,000) compared to methods requiring extensive fine-tuning or complex prompting strategies.'

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 simplescaling/s1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 1×
  2. LangChain · recommended 1×
  3. Pinecone · recommended 1×
  4. Weaviate · recommended 1×
  5. ChromaDB · recommended 1×
  • CATEGORY QUERY
    How to improve large language model reasoning performance with minimal training data?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Pinecone
    4. Weaviate
    5. ChromaDB
    6. LangChain Agents
    7. AutoGPT
    8. Gorilla
    9. Hugging Face Transformers
    10. TRL library
    11. Open Assistant

    AI recommended 11 alternatives but never named simplescaling/s1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective techniques for test-time scaling to boost LLM inference quality?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Llama 3 (meta-llama/llama-models)
    4. Cohere Rerank
    5. BGE-Reranker (FlagOpen/FlagEmbedding)

    AI recommended 5 alternatives but never named simplescaling/s1. 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 simplescaling/s1?
    pass
    AI did not name simplescaling/s1 — likely talking about a different project

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

  • If a team adopts simplescaling/s1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named simplescaling/s1 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 simplescaling/s1 solve, and who is the primary audience?
    pass
    AI named simplescaling/s1 explicitly

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

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simplescaling/s1 — 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