RRepoGEO

REPOGEO REPORT · LITE

Optima-CityU/LLM4AD

Default branch main · commit e8d848fe · scanned 6/9/2026, 1:31:47 AM

GitHub: 723 stars · 90 forks

AI VISIBILITY SCORE
35 /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
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 Optima-CityU/LLM4AD, 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
  • highreadme#1
    Clarify the 'AD' acronym in the README's main title

    Why:

    CURRENT
    <h1 align="center">
LLM4AD: Large Language Model for Algorithm Design
</h1>
    COPY-PASTE FIX
    <h1 align="center">
LLM4AD: Large Language Model for Automatic Algorithm Design
</h1>
  • mediumreadme#2
    Refine the README introduction to highlight core differentiation

    Why:

    CURRENT
    LLM4AD is an open-source Python-based Platform leveraging **Large Language Models (LLMs)** for **Automatic Algorithm Design (AD)**. Please refer to the paper [LLM4AD] for detailed information, including
the overview, methodology, and benchmark results.
    COPY-PASTE FIX
    LLM4AD is an open-source Python-based Platform leveraging **Large Language Models (LLMs)** for **Automatic Algorithm Design (AD)**. This platform specifically enables LLMs to *generate, evaluate, and refine algorithms* for complex problems, particularly in optimization, distinguishing it from general LLM frameworks or traditional algorithm libraries. Please refer to the paper [LLM4AD] for detailed information, including
the overview, methodology, and benchmark results.

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 Optima-CityU/LLM4AD
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. microsoft/semantic-kernel · recommended 1×
  4. deepset-ai/haystack · recommended 1×
  5. OpenAI API · recommended 1×
  • CATEGORY QUERY
    Seeking a platform to automate algorithm creation using large language models.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    4. Haystack (deepset-ai/haystack)
    5. OpenAI API

    AI recommended 5 alternatives but never named Optima-CityU/LLM4AD. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework assists in designing new algorithms with AI for optimization challenges?
    you: not recommended
    AI recommended (in order):
    1. Google OR-Tools (google/or-tools)
    2. Ray RLlib (ray-project/ray)
    3. PyTorch Geometric (PyG) (pyg-team/pytorch_geometric)
    4. Deep Graph Library (DGL) (dmlc/dgl)
    5. TensorFlow Agents (TF-Agents) (tensorflow/agents)
    6. Nevergrad (facebookresearch/nevergrad)
    7. Optuna (optuna/optuna)

    AI recommended 7 alternatives but never named Optima-CityU/LLM4AD. 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 Optima-CityU/LLM4AD?
    pass
    AI named Optima-CityU/LLM4AD explicitly

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

  • If a team adopts Optima-CityU/LLM4AD in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Optima-CityU/LLM4AD 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 Optima-CityU/LLM4AD solve, and who is the primary audience?
    pass
    AI named Optima-CityU/LLM4AD explicitly

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

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Optima-CityU/LLM4AD — 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