RRepoGEO

REPOGEO REPORT · LITE

Yuliang-Liu/Monkey

Default branch main · commit e6522ac0 · scanned 6/22/2026, 5:43:45 PM

GitHub: 1,947 stars · 139 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 Yuliang-Liu/Monkey, 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
    Reposition README opening to clearly state project's purpose for AI

    Why:

    COPY-PASTE FIX
    Add this sentence at the very top of the README, before any existing headings: 'Monkey is a research project and codebase for Large Multi-modal Models (LMMs), demonstrating how image resolution and text labels significantly improve LMM performance. This repository provides the official implementation for our CVPR 2024 Highlight paper.'
  • mediumhomepage#2
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2311.06607

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 Yuliang-Liu/Monkey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DALL-E 3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DALL-E 3 · recommended 1×
  2. Midjourney · recommended 1×
  3. Stable Diffusion · recommended 1×
  4. ControlNet · recommended 1×
  5. img2img · recommended 1×
  • CATEGORY QUERY
    How to improve large multi-modal model performance with better image resolution and text labels?
    you: not recommended
    AI recommended (in order):
    1. DALL-E 3
    2. Midjourney
    3. Stable Diffusion
    4. ControlNet
    5. img2img
    6. ESRGAN
    7. Real-ESRGAN
    8. SwinIR
    9. CLIP
    10. OpenCLIP
    11. BLIP-2
    12. GPT-4V
    13. Amazon Rekognition
    14. Google Cloud Vision AI
    15. Azure Cognitive Services

    AI recommended 15 alternatives but never named Yuliang-Liu/Monkey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best multimodal models for OCR-free document understanding and processing tasks?
    you: not recommended
    AI recommended (in order):
    1. LayoutLMv3
    2. Donut
    3. Pix2Struct
    4. UDOP
    5. LiLT
    6. Nougat

    AI recommended 6 alternatives but never named Yuliang-Liu/Monkey. 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 Yuliang-Liu/Monkey?
    pass
    AI named Yuliang-Liu/Monkey explicitly

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

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

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

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Yuliang-Liu/Monkey — 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
Yuliang-Liu/Monkey — RepoGEO report