RRepoGEO

REPOGEO REPORT · LITE

MoonshotAI/MoBA

Default branch master · commit b5d58363 · scanned 5/13/2026, 8:23:50 PM

GitHub: 2,117 stars · 146 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 MoonshotAI/MoBA, 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 MoBA's identity and purpose in the README's opening

    Why:

    CURRENT
    🚀 Introducing **MoBA Mixture of Block AttentionTrainable Block Sparse Attention**: The full context is divided into blocks, where each query token learns to attend to the most relevant KV blocks, enabling efficient processing of long sequences.
    COPY-PASTE FIX
    MoBA (Mixture of Block Attention) is a novel sparse attention mechanism designed to efficiently process extremely long sequences in large language models (LLMs).
  • highreadme#2
    Explicitly state MoBA's position relative to existing efficient attention mechanisms

    Why:

    COPY-PASTE FIX
    MoBA provides a flexible and trainable block-sparse attention mechanism, offering a novel approach to long-context LLMs compared to existing methods like FlashAttention, Longformer, or BigBird.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2502.13189

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 MoonshotAI/MoBA
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FlashAttention
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. FlashAttention · recommended 2×
  2. Longformer · recommended 2×
  3. Reformer · recommended 2×
  4. BigBird · recommended 2×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How to efficiently process extremely long sequences in large language models?
    you: not recommended
    AI recommended (in order):
    1. FlashAttention
    2. Hugging Face Transformers
    3. Llama 2
    4. Mistral 7B
    5. LongRoPE
    6. Longformer
    7. Reformer
    8. BigBird
    9. Mamba
    10. RWKV
    11. Hiearchical Transformer
    12. Transformer-XL
    13. Compressive Transformer

    AI recommended 13 alternatives but never named MoonshotAI/MoBA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What methods exist for training LLMs with block-sparse attention to extend context windows?
    you: not recommended
    AI recommended (in order):
    1. Longformer
    2. BigBird
    3. GPT-3
    4. OpenAI GPT-3
    5. Reformer
    6. Performer
    7. Sparse Transformers
    8. FlashAttention
    9. FlashAttention-2

    AI recommended 9 alternatives but never named MoonshotAI/MoBA. 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 MoonshotAI/MoBA?
    pass
    AI named MoonshotAI/MoBA explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite