RRepoGEO

REPOGEO REPORT · LITE

ATH-MaaS/Awesome-Unified-Multimodal-Models

Default branch main · commit c81ef568 · scanned 7/1/2026, 10:57:59 AM

GitHub: 1,292 stars · 41 forks

AI VISIBILITY SCORE
22 /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
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 ATH-MaaS/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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify repo's identity as a curated list in the README's opening

    Why:

    CURRENT
    The current README starts with a large title and then a survey link, followed by a 'What is This Repo for?' section further down.
    COPY-PASTE FIX
    Add the following sentence immediately after the main title in the README: 'This repository is a comprehensive, curated collection of resources for unified multimodal models, including surveys, categorized lists of architectures, and benchmarks.'
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of the MIT License.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Set the repository homepage URL to `https://arxiv.org/abs/2505.02567`.

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 ATH-MaaS/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
Hugging Face Hub
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Hub · recommended 1×
  2. Google AI Model Garden (on Vertex AI) · recommended 1×
  3. OpenAI Models (via API) · recommended 1×
  4. Meta AI Research · recommended 1×
  5. PyTorch Hub · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive collection of unified multimodal AI models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. Google AI Model Garden (on Vertex AI)
    3. OpenAI Models (via API)
    4. Meta AI Research
    5. PyTorch Hub
    6. TensorFlow Hub

    AI recommended 6 alternatives but never named ATH-MaaS/Awesome-Unified-Multimodal-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which AI models support any-to-any multimodal processing, including audio and video?
    you: not recommended
    AI recommended (in order):
    1. Google Gemini
    2. OpenAI GPT-4o
    3. Meta's SeamlessM4T / SeamlessExpress
    4. Google's PaLM-E
    5. Microsoft's Florence-2

    AI recommended 5 alternatives but never named ATH-MaaS/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
    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 ATH-MaaS/Awesome-Unified-Multimodal-Models?
    pass
    AI did not name ATH-MaaS/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 ATH-MaaS/Awesome-Unified-Multimodal-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ATH-MaaS/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 ATH-MaaS/Awesome-Unified-Multimodal-Models solve, and who is the primary audience?
    pass
    AI did not name ATH-MaaS/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?

Embed your GEO score

Drop this badge into the README of ATH-MaaS/Awesome-Unified-Multimodal-Models. 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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ATH-MaaS/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