RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/DeepSeek-V3.2-Exp

Default branch main · commit 87e509a2 · scanned 5/19/2026, 4:23:46 AM

GitHub: 1,586 stars · 170 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
23 /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
2 / 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 deepseek-ai/DeepSeek-V3.2-Exp, 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
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    An experimental large language model (LLM) featuring DeepSeek Sparse Attention for improved training and inference efficiency in long-context scenarios.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    large-language-model, llm, sparse-attention, efficient-attention, transformer-architecture, deep-learning, ai-research, deepseek
  • mediumreadme#3
    Add a concise summary sentence immediately after the main title

    Why:

    COPY-PASTE FIX
    Add this line directly after `# DeepSeek-V3.2-Exp`: `This experimental release introduces DeepSeek Sparse Attention, a novel mechanism for improving the efficiency of large language models in long-context scenarios.`

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 deepseek-ai/DeepSeek-V3.2-Exp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Longformer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Longformer · recommended 2×
  2. Reformer · recommended 2×
  3. BigBird · recommended 2×
  4. Performer · recommended 2×
  5. Linformer · recommended 2×
  • CATEGORY QUERY
    How to reduce computational cost and memory footprint in large language models using efficient attention?
    you: not recommended
    AI recommended (in order):
    1. Longformer
    2. Reformer
    3. BigBird
    4. Performer
    5. Linformer
    6. Nyströmformer
    7. FlashAttention
    8. DeepSpeed
    9. ZeRO
    10. Megatron-LM

    AI recommended 10 alternatives but never named deepseek-ai/DeepSeek-V3.2-Exp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking experimental large language models or architectures utilizing novel sparse attention mechanisms for research.
    you: not recommended
    AI recommended (in order):
    1. Longformer
    2. Reformer
    3. BigBird
    4. Performer
    5. Linformer
    6. Sparse Transformer
    7. FlashAttention

    AI recommended 7 alternatives but never named deepseek-ai/DeepSeek-V3.2-Exp. 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 deepseek-ai/DeepSeek-V3.2-Exp?
    pass
    AI named deepseek-ai/DeepSeek-V3.2-Exp explicitly

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

  • If a team adopts deepseek-ai/DeepSeek-V3.2-Exp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepseek-ai/DeepSeek-V3.2-Exp 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 deepseek-ai/DeepSeek-V3.2-Exp solve, and who is the primary audience?
    pass
    AI did not name deepseek-ai/DeepSeek-V3.2-Exp — 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?

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deepseek-ai/DeepSeek-V3.2-Exp — 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