RRepoGEO

REPOGEO REPORT · LITE

dongzhuoyao/awesome-flow-matching

Default branch main · commit 485d6867 · scanned 6/8/2026, 8:38:16 AM

GitHub: 678 stars · 22 forks

AI VISIBILITY SCORE
30 /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
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 dongzhuoyao/awesome-flow-matching, 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 improve categorization

    Why:

    COPY-PASTE FIX
    ["flow-matching", "stochastic-interpolants", "generative-models", "deep-learning", "machine-learning", "awesome-list", "research-papers"]
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    (Create a LICENSE file in the repository root. For a list of links, consider a permissive license like MIT, Apache-2.0, or a content-specific license like CC0-1.0 or CC-BY-4.0.)
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/dongzhuoyao/awesome-flow-matching (or a more specific project page if one exists)

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 dongzhuoyao/awesome-flow-matching
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NICE
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NICE · recommended 1×
  2. Real NVP · recommended 1×
  3. Glow · recommended 1×
  4. FFJORD · recommended 1×
  5. VQ-VAE-2 · recommended 1×
  • CATEGORY QUERY
    What are current research trends in generative modeling beyond standard diffusion models?
    you: not recommended
    AI recommended (in order):
    1. NICE
    2. Real NVP
    3. Glow
    4. FFJORD
    5. VQ-VAE-2
    6. DALL-E / DALL-E 2
    7. Imagen
    8. Parti
    9. StyleGAN3
    10. ALAE
    11. StyleGAN-XL
    12. JEM
    13. Implicit Maximum Likelihood Estimation (IMLE)
    14. NCSN
    15. SDE-based Generative Models

    AI recommended 15 alternatives but never named dongzhuoyao/awesome-flow-matching. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find recent papers on flow matching or stochastic interpolants for generative AI?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. Papers With Code
    4. NeurIPS
    5. ICML
    6. ICLR
    7. AAAI
    8. CVPR
    9. OpenReview
    10. Twitter/X
    11. Hugging Face
    12. r/MachineLearning
    13. r/deeplearning
    14. Hugging Face Blog/Research Section

    AI recommended 14 alternatives but never named dongzhuoyao/awesome-flow-matching. 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 dongzhuoyao/awesome-flow-matching?
    pass
    AI named dongzhuoyao/awesome-flow-matching explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of dongzhuoyao/awesome-flow-matching. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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dongzhuoyao/awesome-flow-matching — 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