RRepoGEO

REPOGEO REPORT · LITE

EvolvingLMMs-Lab/NEO

Default branch main · commit 73257166 · scanned 6/13/2026, 6:27:50 PM

GitHub: 826 stars · 28 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 EvolvingLMMs-Lab/NEO, 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 to clarify its role as a building framework

    Why:

    CURRENT
    # <p align="center">  NEO Series: Native Vision-Language Models </p>
    COPY-PASTE FIX
    # <p align="center">  NEO Series: Native Vision-Language Models </p>

This repository presents the NEO Series, a research framework and lab dedicated to exploring and building native vision-language models from first principles, focusing on unified architectures and end-to-end development.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the official project or lab homepage URL (e.g., 'https://evolvinglmms-lab.github.io/NEO' or 'https://your-lab-website.com/neo-series')
  • mediumtopics#3
    Enhance repository topics to emphasize 'framework' and 'building' aspects

    Why:

    CURRENT
    agi, encoder-free-vlm, large-language-models, mllm, multimodal, multimodal-large-language-models, native-multimodal-model, vlm
    COPY-PASTE FIX
    agi, encoder-free-vlm, large-language-models, mllm, multimodal, multimodal-large-language-models, native-multimodal-model, vlm, vlm-framework, model-building, research-framework

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 EvolvingLMMs-Lab/NEO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ImageBind
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ImageBind · recommended 2×
  2. Google Gemini · recommended 1×
  3. OpenAI GPT-4o · recommended 1×
  4. Meta Llama 3 · recommended 1×
  5. CM3leon · recommended 1×
  • CATEGORY QUERY
    Looking for a native multimodal large language model built from first principles.
    you: not recommended
    AI recommended (in order):
    1. Google Gemini
    2. OpenAI GPT-4o
    3. Meta Llama 3
    4. ImageBind
    5. CM3leon
    6. Microsoft Florence
    7. DeepMind Gato

    AI recommended 7 alternatives but never named EvolvingLMMs-Lab/NEO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build unified vision-language models end-to-end without separate encoders?
    you: not recommended
    AI recommended (in order):
    1. Flamingo
    2. LLaVA
    3. BLIP-2
    4. CoCa
    5. Perceiver IO
    6. ImageBind

    AI recommended 6 alternatives but never named EvolvingLMMs-Lab/NEO. 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 EvolvingLMMs-Lab/NEO?
    pass
    AI named EvolvingLMMs-Lab/NEO explicitly

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

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

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

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EvolvingLMMs-Lab/NEO — 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