RRepoGEO

REPOGEO REPORT · LITE

bgavran/Category_Theory_Machine_Learning

Default branch master · commit 93038535 · scanned 6/16/2026, 10:18:09 AM

GitHub: 1,524 stars · 101 forks

AI VISIBILITY SCORE
22 /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
1 / 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 bgavran/Category_Theory_Machine_Learning, 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 README's opening sentence to emphasize "curated list of papers"

    Why:

    CURRENT
    This repository aims to list all of the relevant papers, grouped by fields.
    COPY-PASTE FIX
    This repository is a curated list of academic papers studying machine learning through the lens of category theory, grouped by fields.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a suitable open-source license, such as CC-BY-4.0 for content.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a project page, personal website, or related resource) to the "Homepage" field in the repository settings.

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 bgavran/Category_Theory_Machine_Learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Functorial Machine Learning (FML)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Functorial Machine Learning (FML) · recommended 1×
  2. Monoidal Categories · recommended 1×
  3. Topos Theory · recommended 1×
  4. Lenses · recommended 1×
  5. Haskell's `lens` package · recommended 1×
  • CATEGORY QUERY
    How can category theory be applied to understand and improve machine learning models?
    you: not recommended
    AI recommended (in order):
    1. Functorial Machine Learning (FML)
    2. Monoidal Categories
    3. Topos Theory
    4. Lenses
    5. Haskell's `lens` package
    6. Categorical Deep Learning
    7. Applied Category Theory for Machine Learning
    8. Probabilistic Monads
    9. Haskell
    10. TensorFlow
    11. PyTorch

    AI recommended 11 alternatives but never named bgavran/Category_Theory_Machine_Learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find academic papers on category theory applications in deep learning architectures?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. Semantic Scholar
    4. MathOverflow
    5. Cross Validated
    6. NeurIPS
    7. ICML
    8. ICLR
    9. AISTATS
    10. JMLR
    11. TNNLS
    12. PNAS
    13. Nature Machine Intelligence
    14. Applied Category Theory (ACT) Conference

    AI recommended 14 alternatives but never named bgavran/Category_Theory_Machine_Learning. 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 bgavran/Category_Theory_Machine_Learning?
    pass
    AI did not name bgavran/Category_Theory_Machine_Learning — 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?

  • If a team adopts bgavran/Category_Theory_Machine_Learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bgavran/Category_Theory_Machine_Learning 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 bgavran/Category_Theory_Machine_Learning solve, and who is the primary audience?
    pass
    AI did not name bgavran/Category_Theory_Machine_Learning — 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?

Embed your GEO score

Drop this badge into the README of bgavran/Category_Theory_Machine_Learning. 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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MARKDOWN (README)
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bgavran/Category_Theory_Machine_Learning — 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