RRepoGEO

REPOGEO REPORT · LITE

huggingface/Math-Verify

Default branch main · commit ba3d3aaf · scanned 7/1/2026, 10:51:41 AM

GitHub: 1,158 stars · 56 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 huggingface/Math-Verify, 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
  • highabout#1
    Add a concise About description

    Why:

    COPY-PASTE FIX
    A robust mathematical expression evaluation system designed for assessing Large Language Model outputs in mathematical tasks, achieving high accuracy on datasets like MATH.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ['llm-evaluation', 'math-verification', 'large-language-models', 'mathematical-reasoning', 'ai-evaluation', 'nlp', 'python']
  • mediumreadme#3
    Add a sentence to the README clarifying its unique focus

    Why:

    CURRENT
    A robust mathematical expression evaluation system designed for assessing Large Language Model outputs in mathematical tasks.
    COPY-PASTE FIX
    A robust mathematical expression evaluation system designed for assessing Large Language Model outputs in mathematical tasks. Unlike general-purpose math libraries, Math-Verify focuses specifically on verifying the correctness of given mathematical solutions rather than generating them or performing symbolic computations.

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 huggingface/Math-Verify
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Maple
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Maple · recommended 2×
  2. sympy/sympy · recommended 1×
  3. Mathematica · recommended 1×
  4. sagemath/sagemath · recommended 1×
  5. numpy/numpy · recommended 1×
  • CATEGORY QUERY
    How can I accurately evaluate mathematical problem-solving outputs from large language models?
    you: not recommended
    AI recommended (in order):
    1. SymPy (sympy/sympy)
    2. Mathematica
    3. Maple
    4. SageMath (sagemath/sagemath)
    5. NumPy (numpy/numpy)
    6. SciPy (scipy/scipy)
    7. MATLAB
    8. Pytest (pytest-dev/pytest)
    9. JUnit (junit-team/junit5)
    10. Google Test (google/googletest)
    11. Wolfram Alpha API
    12. Scale AI
    13. Appen
    14. GPT-4
    15. Claude 3
    16. Llama 3 (meta-llama/llama3)
    17. GSM8K
    18. MATH
    19. MiniF2F
    20. AQuA-RAT

    AI recommended 20 alternatives but never named huggingface/Math-Verify. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools offer highly accurate evaluation of mathematical expressions from AI models?
    you: not recommended
    AI recommended (in order):
    1. SymPy
    2. Mathematica (Wolfram Language)
    3. Maple
    4. Math.js
    5. NumPy/SciPy
    6. mpmath

    AI recommended 6 alternatives but never named huggingface/Math-Verify. 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 huggingface/Math-Verify?
    pass
    AI named huggingface/Math-Verify explicitly

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

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