RRepoGEO

REPOGEO REPORT · LITE

XiaomiMiMo/MiMo

Default branch main · commit 3a3fe65e · scanned 6/18/2026, 9:54:24 AM

GitHub: 2,256 stars · 102 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 XiaomiMiMo/MiMo, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    large-language-models, llm, reasoning, pretraining, posttraining, mathematics, ai, deep-learning, machine-learning, xiaomi
  • highreadme#2
    Add a direct, concise opening sentence to the README

    Why:

    COPY-PASTE FIX
    MiMo is a research project focused on enhancing the reasoning capabilities of large language models (LLMs) through advanced pretraining and posttraining techniques, with a strong emphasis on mathematical and complex problem-solving.
  • mediumabout#3
    Set the repository homepage to the technical report URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2505.07608

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 XiaomiMiMo/MiMo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 2 · recommended 1×
  2. Mistral · recommended 1×
  3. ShishirPatil/gorilla · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How can I improve the reasoning capabilities of my large language models through advanced training?
    you: not recommended
    AI recommended (in order):
    1. Llama 2
    2. Mistral
    3. Gorilla (ShishirPatil/gorilla)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. DeepMind's AlphaCode
    7. GPT-NeoX (EleutherAI/gpt-neox)
    8. Falcon (tiiuae/falcon-7b)
    9. Alpaca (tatsu-lab/stanford_alpaca)
    10. Vicuna (lmsys/vicuna)
    11. GPT-4
    12. Transformer-XL (kimiyoung/transformer-xl)
    13. Gemini 1.5 Pro
    14. Claude 3 Opus

    AI recommended 14 alternatives but never named XiaomiMiMo/MiMo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for improving LLM performance on mathematical and complex reasoning benchmarks?
    you: not recommended
    AI recommended (in order):
    1. MATH Dataset
    2. GSM8K
    3. AQuA
    4. TheoremQA
    5. Proof-Pile
    6. Lean
    7. Isabelle/HOL
    8. Chain-of-Thought Prompting
    9. Self-Correction/Self-Refinement
    10. Tree-of-Thought (ToT)
    11. Wolfram Alpha
    12. Python Interpreter
    13. Code Interpreter in ChatGPT
    14. Google Search API
    15. Bing Search API
    16. Retrieval-Augmented Generation (RAG)
    17. RAG by Facebook AI
    18. Program-Aided Language Models (PAL)
    19. RLHF
    20. InstructGPT
    21. ChatGPT

    AI recommended 21 alternatives but never named XiaomiMiMo/MiMo. 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 XiaomiMiMo/MiMo?
    pass
    AI named XiaomiMiMo/MiMo explicitly

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

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

    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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MARKDOWN (README)
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XiaomiMiMo/MiMo — 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