RRepoGEO

REPOGEO REPORT · LITE

HITsz-TMG/Awesome-Large-Multimodal-Reasoning-Models

Default branch main · commit 92b4cf22 · scanned 6/16/2026, 1:52:27 AM

GitHub: 613 stars · 22 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 HITsz-TMG/Awesome-Large-Multimodal-Reasoning-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
  • highabout#1
    Clarify "About" description to emphasize it's a survey/list

    Why:

    CURRENT
    The development and future prospects of large multimodal reasoning models.
    COPY-PASTE FIX
    A comprehensive survey and curated list of resources on Large Multimodal Reasoning Models, covering their development and future prospects.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    large-multimodal-models, lmm, multimodal-ai, reasoning-models, ai-survey, awesome-list, machine-learning, deep-learning
  • highlicense#3
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root directory with the MIT License text.

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 HITsz-TMG/Awesome-Large-Multimodal-Reasoning-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. GPT-4V · recommended 1×
  3. Gemini · recommended 1×
  4. LLaVA · recommended 1×
  5. Flamingo · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive overview of large multimodal AI reasoning models?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. GPT-4V
    3. Gemini
    4. LLaVA
    5. Flamingo
    6. Hugging Face
    7. BLIP-2
    8. InstructBLIP
    9. CoCa
    10. arXiv
    11. PaLI-X
    12. Qwen-VL
    13. IDEFICS
    14. Google AI Blog
    15. DeepMind Blog
    16. PaLM-E
    17. The Batch by DeepLearning.AI
    18. Synced Review
    19. Stanford
    20. UC Berkeley
    21. MIT CSAIL

    AI recommended 21 alternatives but never named HITsz-TMG/Awesome-Large-Multimodal-Reasoning-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the cutting-edge large multimodal models for advanced AI reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini 1.5 Pro
    3. Claude 3 Opus
    4. Llama 3
    5. Llama-3-V
    6. Qwen-VL-Max
    7. CogVLM

    AI recommended 7 alternatives but never named HITsz-TMG/Awesome-Large-Multimodal-Reasoning-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 HITsz-TMG/Awesome-Large-Multimodal-Reasoning-Models?
    pass
    AI did not name HITsz-TMG/Awesome-Large-Multimodal-Reasoning-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 HITsz-TMG/Awesome-Large-Multimodal-Reasoning-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HITsz-TMG/Awesome-Large-Multimodal-Reasoning-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 HITsz-TMG/Awesome-Large-Multimodal-Reasoning-Models solve, and who is the primary audience?
    pass
    AI did not name HITsz-TMG/Awesome-Large-Multimodal-Reasoning-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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
HITsz-TMG/Awesome-Large-Multimodal-Reasoning-Models — RepoGEO report