RRepoGEO

REPOGEO REPORT · LITE

lwpyh/Awesome-MLLM-Reasoning-Collection

Default branch main · commit b428eece · scanned 6/17/2026, 11:48:01 AM

GitHub: 6 stars · 0 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 lwpyh/Awesome-MLLM-Reasoning-Collection, 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 the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of a permissive license like MIT.
  • mediumreadme#2
    Strengthen the README's opening sentence to emphasize unique value

    Why:

    CURRENT
    👏 Welcome to the Awesome-MLLM-Reasoning-Collections repository! This repository is a carefully curated collection of papers, code, datasets, benchmarks, and resources focused on reasoning within Multimodal Large Language Models (MLLMs).
    COPY-PASTE FIX
    Welcome to the Awesome-MLLM-Reasoning-Collection, the definitive curated repository for cutting-edge research, code, datasets, and benchmarks specifically focused on advancing reasoning capabilities within Multimodal Large Language Models (MLLMs).

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 lwpyh/Awesome-MLLM-Reasoning-Collection
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 2×
  2. Hugging Face Hub · recommended 1×
  3. BradyFU/awesome-multimodal-llms · recommended 1×
  4. arXiv.org · recommended 1×
  5. Kaggle · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive collection of resources for multimodal LLM reasoning research?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. Papers With Code
    3. awesome-multimodal-llms (BradyFU/awesome-multimodal-llms)
    4. arXiv.org
    5. Kaggle

    AI recommended 5 alternatives but never named lwpyh/Awesome-MLLM-Reasoning-Collection. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking research papers, datasets, and benchmarks for advanced multimodal AI reasoning models.
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. Hugging Face Datasets
    3. Google AI Blog & Research
    4. Microsoft Research
    5. arXiv
    6. CVPR/ICCV/ECCV/NeurIPS/ICML Proceedings
    7. Awesome Multimodal AI

    AI recommended 7 alternatives but never named lwpyh/Awesome-MLLM-Reasoning-Collection. 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 lwpyh/Awesome-MLLM-Reasoning-Collection?
    pass
    AI did not name lwpyh/Awesome-MLLM-Reasoning-Collection — 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 lwpyh/Awesome-MLLM-Reasoning-Collection in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lwpyh/Awesome-MLLM-Reasoning-Collection 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 lwpyh/Awesome-MLLM-Reasoning-Collection solve, and who is the primary audience?
    pass
    AI did not name lwpyh/Awesome-MLLM-Reasoning-Collection — 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite