RRepoGEO

REPOGEO REPORT · LITE

EleutherAI/math-lm

Default branch main · commit 9575d138 · scanned 5/12/2026, 2:38:11 PM

GitHub: 1,098 stars · 95 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 EleutherAI/math-lm, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    Llemma: an open language model for mathematics, along with its training data (Proof-Pile-2, AlgebraicStack) and code for mathematical reasoning and proof generation.
  • mediumhomepage#2
    Add the arXiv paper link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2310.10631

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 EleutherAI/math-lm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Meta Llama 3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Meta Llama 3 · recommended 1×
  2. Mistral Large · recommended 1×
  3. Mixtral 8x22B · recommended 1×
  4. Google Gemma · recommended 1×
  5. Nous Research's Hermes 2 · recommended 1×
  • CATEGORY QUERY
    What open source large language models are best for advanced mathematical reasoning?
    you: not recommended
    AI recommended (in order):
    1. Meta Llama 3
    2. Mistral Large
    3. Mixtral 8x22B
    4. Google Gemma
    5. Nous Research's Hermes 2
    6. Qwen
    7. Wolfram Alpha

    AI recommended 7 alternatives but never named EleutherAI/math-lm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find large datasets to pre-train a language model for mathematical proofs?
    you: not recommended
    AI recommended (in order):
    1. arXiv
    2. Math StackExchange
    3. MathOverflow
    4. Lean Mathematical Library (leanprover-community/mathlib)
    5. Isabelle/HOL Archive of Formal Proofs (isabelle-prover/AFP)
    6. Mizar Mathematical Library
    7. ProofWiki

    AI recommended 7 alternatives but never named EleutherAI/math-lm. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 EleutherAI/math-lm?
    pass
    AI named EleutherAI/math-lm explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite