RRepoGEO

REPOGEO REPORT · LITE

openai/sparse_autoencoder

Default branch main · commit 4965b941 · scanned 5/27/2026, 11:02:22 PM

GitHub: 588 stars · 68 forks

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 openai/sparse_autoencoder, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise project description to the 'About' section

    Why:

    COPY-PASTE FIX
    A Python library for training and visualizing sparse autoencoders on internal activations of large language models (LLMs) to enhance mechanistic interpretability.
  • mediumreadme#2
    Strengthen the README's opening sentence to clarify purpose and audience

    Why:

    CURRENT
    # Sparse autoencoders

This repository hosts:
- sparse autoencoders trained on the GPT2-small model's activations.
- a visualizer for the autoencoders' features
    COPY-PASTE FIX
    # Sparse autoencoders for Mechanistic Interpretability

This repository provides a Python library for training and visualizing sparse autoencoders on the internal activations of large language models (LLMs), specifically designed to enhance mechanistic interpretability. It hosts:
- sparse autoencoders trained on the GPT2-small model's activations.
- a visualizer for the autoencoders' features

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 openai/sparse_autoencoder
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Captum · recommended 1×
  3. LRP Toolbox · recommended 1×
  4. TransformerLens · recommended 1×
  5. TensorBoard · recommended 1×
  • CATEGORY QUERY
    How can I analyze and visualize the internal activations of transformer models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Captum
    3. LRP Toolbox
    4. TransformerLens
    5. TensorBoard
    6. Activation Atlas
    7. Matplotlib
    8. Seaborn

    AI recommended 8 alternatives but never named openai/sparse_autoencoder. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python library to train sparse autoencoders for LLM interpretability.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow/Keras
    3. Hugging Face transformers
    4. scikit-learn
    5. SAELens

    AI recommended 5 alternatives but never named openai/sparse_autoencoder. 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 openai/sparse_autoencoder?
    pass
    AI named openai/sparse_autoencoder explicitly

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

  • If a team adopts openai/sparse_autoencoder in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/sparse_autoencoder 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 openai/sparse_autoencoder solve, and who is the primary audience?
    pass
    AI did not name openai/sparse_autoencoder — 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite