RRepoGEO

REPOGEO REPORT · LITE

PKU-YuanGroup/LLaVA-CoT

Default branch main · commit 081cc3fe · scanned 5/10/2026, 12:47:52 AM

GitHub: 2,135 stars · 82 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 PKU-YuanGroup/LLaVA-CoT, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llava, multimodal-ai, vision-language-model, chain-of-thought, reasoning, llm, computer-vision
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2411.10440
  • mediumreadme#3
    Add a concise descriptive sentence after the main title in README

    Why:

    CURRENT
    <h2 align="center"> <a href="https://arxiv.org/abs/2411.10440">LLaVA-CoT: Let Vision Language Models Reason Step-by-Step</a></h2>
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for the latest update.</h5>
    COPY-PASTE FIX
    <h2 align="center"> <a href="https://arxiv.org/abs/2411.10440">LLaVA-CoT: Let Vision Language Models Reason Step-by-Step</a></h2>
<p align="center">LLaVA-CoT is an ICCV 2025 accepted visual language model designed to enhance LLaVA's capabilities with spontaneous, systematic Chain-of-Thought reasoning for complex visual understanding tasks.</p>
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for the latest update.</h5>

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 PKU-YuanGroup/LLaVA-CoT
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Vision Transformers (ViT) · recommended 1×
  3. Large Language Models (LLMs) · recommended 1×
  4. PyTorch Lightning · recommended 1×
  5. torchvision · recommended 1×
  • CATEGORY QUERY
    How to build a visual language model that performs systematic, step-by-step reasoning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Vision Transformers (ViT)
    3. Large Language Models (LLMs)
    4. PyTorch Lightning
    5. torchvision
    6. TensorFlow
    7. Keras
    8. TensorFlow Hub
    9. KerasCV
    10. OpenAI API
    11. GPT-4V
    12. DALL-E 3
    13. Google Cloud Vertex AI
    14. Vision AI
    15. Generative AI Studio
    16. Microsoft Azure AI
    17. Azure Cognitive Services (Vision)
    18. Azure OpenAI Service

    AI recommended 18 alternatives but never named PKU-YuanGroup/LLaVA-CoT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source multimodal AI for complex visual understanding with spontaneous reasoning capabilities.
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. MiniGPT-4
    3. BLIP-2
    4. OpenFlamingo
    5. InternLM-XComposer
    6. Qwen-VL

    AI recommended 6 alternatives but never named PKU-YuanGroup/LLaVA-CoT. 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 PKU-YuanGroup/LLaVA-CoT?
    pass
    AI named PKU-YuanGroup/LLaVA-CoT explicitly

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

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

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

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PKU-YuanGroup/LLaVA-CoT — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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