RRepoGEO

REPOGEO REPORT · LITE

AdityaNG/kan-gpt

Default branch main · commit 0c6e4c25 · scanned 6/2/2026, 9:11:54 AM

GitHub: 724 stars · 55 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 AdityaNG/kan-gpt, 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
    Strengthen README's opening statement to highlight novelty and core technology

    Why:

    CURRENT
    The PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling
    COPY-PASTE FIX
    KAN-GPT is a novel PyTorch implementation of Generative Pre-trained Transformers (GPTs) that replaces traditional MLPs with Kolmogorov-Arnold Networks (KANs) for enhanced language modeling. This project explores a cutting-edge transformer architecture for text generation.
  • mediumcomparison#2
    Add a 'Why KAN-GPT?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why KAN-GPT?
While other libraries like `kan-torch` provide general Kolmogorov-Arnold Network implementations, KAN-GPT specifically integrates KANs into the Generative Pre-trained Transformer (GPT) architecture. This project offers a unique exploration of KANs as a drop-in replacement for Multi-Layer Perceptrons (MLPs) within a full language model, providing a novel approach to text generation compared to standard GPT models and other experimental transformer designs.
  • lowtopics#3
    Add `pytorch` and `novel-architectures` to repository topics

    Why:

    CURRENT
    gpt, kanformers, kolmogorov-arnold-networks, kolmogorov-arnold-representation, llm, text-generation, transformers
    COPY-PASTE FIX
    gpt, kanformers, kolmogorov-arnold-networks, kolmogorov-arnold-representation, llm, text-generation, transformers, pytorch, novel-architectures

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 AdityaNG/kan-gpt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. kan-torch · recommended 1×
  3. torch_spline_conv · recommended 1×
  4. Hugging Face `transformers` · recommended 1×
  5. einops · recommended 1×
  • CATEGORY QUERY
    Looking for PyTorch libraries to build language models with Kolmogorov-Arnold Networks.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. kan-torch
    3. torch_spline_conv
    4. Hugging Face `transformers`
    5. einops

    AI recommended 5 alternatives but never named AdityaNG/kan-gpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some novel transformer architectures for text generation beyond standard GPT models?
    you: not recommended
    AI recommended (in order):
    1. Diffusion Transformers (DiT)
    2. RecurrentGemma
    3. RetNet (Retentive Network)
    4. RWKV (Receptance Weighted Key Value)
    5. Hyena Hierarchy
    6. Mamba

    AI recommended 6 alternatives but never named AdityaNG/kan-gpt. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 AdityaNG/kan-gpt?
    pass
    AI named AdityaNG/kan-gpt explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite