RRepoGEO

REPOGEO REPORT · LITE

PKU-YuanGroup/LLaVA-CoT

Default branch main · commit 081cc3fe · scanned 6/19/2026, 10:12:46 PM

GitHub: 2,137 stars · 82 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
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

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

OVERALL DIRECTION
  • highhomepage#1
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2411.10440
  • mediumreadme#2
    Add a concise, keyword-rich introductory sentence

    Why:

    CURRENT
    The README currently starts with a `div` block followed by the main H2 title.
    COPY-PASTE FIX
    Add the following sentence immediately after the initial `</div>` and before the `<h2>` tag: "LLaVA-CoT is a cutting-edge visual language model designed for spontaneous, systematic, step-by-step reasoning, enhancing multimodal AI understanding."

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
LLaVA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LLaVA · recommended 2×
  2. OpenAI GPT-4V (Vision) · recommended 1×
  3. Google Gemini (Pro/Ultra) · recommended 1×
  4. Llama-2-V · recommended 1×
  5. InstructBLIP · recommended 1×
  • CATEGORY QUERY
    How to implement a vision language model that performs systematic, step-by-step reasoning?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4V (Vision)
    2. Google Gemini (Pro/Ultra)
    3. Llama-2-V
    4. LLaVA
    5. InstructBLIP
    6. MiniGPT-4
    7. Hugging Face Transformers
    8. ViT
    9. Swin Transformer
    10. CLIP
    11. T5
    12. BART
    13. Flan-T5
    14. DeepMind Gato
    15. Microsoft Visual ChatGPT

    AI recommended 15 alternatives but never named PKU-YuanGroup/LLaVA-CoT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a multi-modal AI model for complex visual understanding requiring deliberate, systematic thought processes.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini 1.5 Pro
    3. Claude 3 Opus
    4. LLaVA
    5. CogVLM

    AI recommended 5 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?

Embed your GEO score

Drop this badge into the README of PKU-YuanGroup/LLaVA-CoT. 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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Pro

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PKU-YuanGroup/LLaVA-CoT — 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