RRepoGEO

REPOGEO REPORT · LITE

showlab/Awesome-Unified-Multimodal-Models

Default branch main · commit 50b718a4 · scanned 6/11/2026, 9:28:07 PM

GitHub: 826 stars · 41 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 showlab/Awesome-Unified-Multimodal-Models, 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
  • highlicense#1
    Add a LICENSE file to clarify usage terms

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License) in the repository root to clearly state the terms under which the content can be used.
  • mediumreadme#2
    Clarify the README's opening sentence to emphasize its 'awesome list' nature

    Why:

    CURRENT
    This is a repository for organizing papers, codes and other resources related to unified multimodal models.
    COPY-PASTE FIX
    This awesome list curates papers, code, and other essential resources specifically for unified multimodal models, integrating both understanding and generation tasks.

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 showlab/Awesome-Unified-Multimodal-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 1×
  2. arXiv.org · recommended 1×
  3. Google Scholar · recommended 1×
  4. GitHub · recommended 1×
  5. Hugging Face Hub · recommended 1×
  • CATEGORY QUERY
    How to find research papers and code for models combining multimodal understanding and generation?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. arXiv.org
    3. Google Scholar
    4. GitHub
    5. Hugging Face Hub
    6. ACL Anthology
    7. EMNLP
    8. CVPR
    9. ICCV
    10. NeurIPS
    11. ICLR
    12. Semantic Scholar

    AI recommended 12 alternatives but never named showlab/Awesome-Unified-Multimodal-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best frameworks enabling any-to-any multimodal input and output generation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Diffusers
    3. PyTorch Lightning
    4. Keras
    5. OpenAI API
    6. GPT-4V
    7. DALL-E 3
    8. Whisper
    9. MMDetection
    10. MMEngine
    11. OpenMMLab
    12. DeepMind's Perceiver IO

    AI recommended 12 alternatives but never named showlab/Awesome-Unified-Multimodal-Models. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 showlab/Awesome-Unified-Multimodal-Models?
    pass
    AI did not name showlab/Awesome-Unified-Multimodal-Models — likely talking about a different project

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

  • If a team adopts showlab/Awesome-Unified-Multimodal-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named showlab/Awesome-Unified-Multimodal-Models 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 showlab/Awesome-Unified-Multimodal-Models solve, and who is the primary audience?
    pass
    AI did not name showlab/Awesome-Unified-Multimodal-Models — likely talking about a different project

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

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showlab/Awesome-Unified-Multimodal-Models — 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