RRepoGEO

REPOGEO REPORT · LITE

ModalMinds/MM-EUREKA

Default branch qwen · commit ef36a84c · scanned 5/25/2026, 4:38:19 AM

GitHub: 772 stars · 30 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 ModalMinds/MM-EUREKA, 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
  • highreadme#1
    Prominently position the project's type and purpose in the README

    Why:

    CURRENT
    # MM-EUREKA

<div align="center">
<p style="text-align: center;">MM-EUREKA: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning<p>
</div>
    COPY-PASTE FIX
    # MM-EUREKA: A Research Framework for Multimodal Reasoning with Rule-based Reinforcement Learning

MM-EUREKA is a cutting-edge research framework designed to explore and enhance the frontiers of multimodal reasoning in Large Language Models (LLMs) through innovative rule-based reinforcement learning techniques.
  • mediumhomepage#2
    Add a homepage link to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/ModalMinds/MM-EUREKA/blob/qwen/MM_EUREKA_Tech_Report.pdf

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 ModalMinds/MM-EUREKA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI's CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI's CLIP · recommended 1×
  2. DALL-E 3 · recommended 1×
  3. GPT-4V · recommended 1×
  4. DeepMind's Gato · recommended 1×
  5. Flamingo · recommended 1×
  • CATEGORY QUERY
    How to improve language model reasoning capabilities using multimodal inputs and reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's CLIP
    2. DALL-E 3
    3. GPT-4V
    4. DeepMind's Gato
    5. Flamingo
    6. Hugging Face Transformers Library (huggingface/transformers)
    7. ViT
    8. BLIP
    9. LLaVA (haotian-liu/LLaVA)
    10. Stable Diffusion (Stability-AI/StableDiffusion)
    11. Stable Baselines3 (DLR-RM/stable-baselines3)
    12. Ray RLlib (ray-project/ray)
    13. CleanRL (vwxyzjn/cleanrl)
    14. PyTorch (pytorch/pytorch)
    15. TensorFlow (tensorflow/tensorflow)

    AI recommended 15 alternatives but never named ModalMinds/MM-EUREKA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods for stable reinforcement learning to enhance large language model performance.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. TRL
    3. Stable Baselines3
    4. PyTorch
    5. TensorFlow
    6. Ray RLlib
    7. Acme
    8. Dopamine

    AI recommended 8 alternatives but never named ModalMinds/MM-EUREKA. 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 ModalMinds/MM-EUREKA?
    pass
    AI named ModalMinds/MM-EUREKA explicitly

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

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

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

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ModalMinds/MM-EUREKA — 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