RRepoGEO

REPOGEO REPORT · LITE

usail-hkust/LLM-MM-Agent

Default branch main · commit 8abc1300 · scanned 6/8/2026, 4:53:10 AM

GitHub: 583 stars · 42 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 usail-hkust/LLM-MM-Agent, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm-agents, mathematical-modeling, problem-solving, code-generation, neurips, ai-agents, large-language-models, automation, scientific-computing
  • highreadme#2
    Add a concise unique value proposition to the README introduction

    Why:

    COPY-PASTE FIX
    Add this sentence immediately after the H1 or the initial keyword block: "Unlike general-purpose LLM frameworks, MM-Agent is specifically engineered to tackle complex real-world mathematical modeling challenges, providing an end-to-end workflow from problem understanding to intelligent code generation and solution validation."
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://arxiv.org/abs/2505.14148

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 usail-hkust/LLM-MM-Agent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. OpenAI API · recommended 1×
  3. LangChain · recommended 1×
  4. LlamaIndex · recommended 1×
  5. numpy · recommended 1×
  • CATEGORY QUERY
    How can I use large language models to automate complex mathematical problem-solving workflows?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. LangChain
    3. LlamaIndex
    4. numpy
    5. Wolfram Alpha API
    6. Wolfram Language
    7. PaLM 2
    8. Gemini API
    9. Hugging Face Transformers
    10. Llama 2
    11. Mistral
    12. CodeLlama
    13. SymPy
    14. SageMath

    AI recommended 14 alternatives but never named usail-hkust/LLM-MM-Agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help generate code and solve real-world mathematical modeling problems using AI agents?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5
    2. Google Gemini
    3. Wolfram Alpha / Wolfram Mathematica
    4. GitHub Copilot
    5. DeepMind AlphaFold / AlphaTensor
    6. Hugging Face Transformers
    7. MATLAB / Simulink

    AI recommended 7 alternatives but never named usail-hkust/LLM-MM-Agent. 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 usail-hkust/LLM-MM-Agent?
    pass
    AI named usail-hkust/LLM-MM-Agent explicitly

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

  • If a team adopts usail-hkust/LLM-MM-Agent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named usail-hkust/LLM-MM-Agent 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 usail-hkust/LLM-MM-Agent solve, and who is the primary audience?
    pass
    AI did not name usail-hkust/LLM-MM-Agent — 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?

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usail-hkust/LLM-MM-Agent — 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