RRepoGEO

REPOGEO REPORT · LITE

alibaba/AliceMind

Default branch main · commit a6d5afe5 · scanned 6/29/2026, 7:57:11 PM

GitHub: 2,044 stars · 299 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 alibaba/AliceMind, 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
  • highreadme#1
    Reposition the README's opening paragraph to highlight core strengths

    Why:

    CURRENT
    This repository provides pre-trained encoder-decoder models and its related optimization techniques developed by Alibaba's MinD (Machine IntelligeNce of Damo) Lab.
    COPY-PASTE FIX
    This repository provides a comprehensive collection of Alibaba's state-of-the-art pre-trained multimodal large language models (MLLMs) and encoder-decoder models, developed by MinD Lab. AliceMind specializes in advanced capabilities like universal document understanding, vision-language integration, and video-language processing, alongside related optimization techniques.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    bert, deep-learning, natural-language-processing, nlp
    COPY-PASTE FIX
    bert, deep-learning, natural-language-processing, nlp, multimodal-llm, vision-language-models, video-language-models, document-understanding, large-language-models
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Insert official project homepage URL here, e.g., a Damo Academy page or project website]

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 alibaba/AliceMind
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4o
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4o · recommended 1×
  2. Gemini 1.5 Pro · recommended 1×
  3. Claude 3 Opus · recommended 1×
  4. llava-vl/llava · recommended 1×
  5. naver-ai/donut · recommended 1×
  • CATEGORY QUERY
    Looking for a multimodal large language model for universal document understanding tasks.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini 1.5 Pro
    3. Claude 3 Opus
    4. LLaVA (llava-vl/llava)
    5. Donut (naver-ai/donut)

    AI recommended 5 alternatives but never named alibaba/AliceMind. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best pre-trained models for integrating text, image, and video data?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's CLIP
    2. OpenCLIP
    3. CLIPA
    4. Google's PaLM-E
    5. Meta's ImageBind
    6. DeepMind's Perceiver IO
    7. Microsoft's Florence-2
    8. Google's VideoMAE
    9. LAION-5B
    10. Stable Diffusion

    AI recommended 10 alternatives but never named alibaba/AliceMind. 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 alibaba/AliceMind?
    pass
    AI named alibaba/AliceMind explicitly

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

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

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

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alibaba/AliceMind — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite