RRepoGEO

REPOGEO REPORT · LITE

openai/circuit_sparsity

Default branch main · commit dbf1fe0d · scanned 6/4/2026, 6:47:55 AM

GitHub: 525 stars · 53 forks

AI VISIBILITY SCORE
35 /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
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 openai/circuit_sparsity, 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
    mechanistic-interpretability, llm-analysis, circuit-visualization, neural-network-pruning, streamlit, research-tool, openai
  • highreadme#2
    Strengthen README opening to highlight core differentiator and category

    Why:

    CURRENT
    # Circuit Sparsity Visualizer and Models

Tools for inspecting sparse circuit models from Gao et al. 2025. Provides code for running inference as well as a Streamlit dashboard that allows you to interact with task-specific circuits found by pruning.
    COPY-PASTE FIX
    # Circuit Sparsity Visualizer and Models for LLM Mechanistic Interpretability

This repository provides tools for inspecting sparse computational circuits in Large Language Models (LLMs), focusing on the surprising effectiveness of random pruning as demonstrated in Gao et al. 2025. It includes code for running inference and a Streamlit dashboard for interactive exploration of task-specific circuits.
  • mediumreadme#3
    Add explicit statement about research repository nature

    Why:

    COPY-PASTE FIX
    Please note: This repository is primarily a research tool for exploring mechanistic interpretability and is not intended as a production-ready library.

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/circuit_sparsity
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NeuronVis
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NeuronVis · recommended 1×
  2. CircuitsVis · recommended 1×
  3. TensorBoard · recommended 1×
  4. Netron · recommended 1×
  5. Captum · recommended 1×
  • CATEGORY QUERY
    How can I visualize and interactively explore sparse circuits in large language models?
    you: not recommended
    AI recommended (in order):
    1. NeuronVis
    2. CircuitsVis
    3. TensorBoard
    4. Netron
    5. Captum
    6. Plotly/Dash
    7. Graphviz

    AI recommended 7 alternatives but never named openai/circuit_sparsity. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help analyze and understand pruned transformer model behavior and specific circuits?
    you: not recommended
    AI recommended (in order):
    1. TransformerLens (neelnanda-io/TransformerLens)
    2. Captum (pytorch/captum)
    3. Interpret-Community (microsoft/interpret-community)
    4. SHAP (shap/shap)
    5. LIME (marcotcr/lime)
    6. TensorBoard (tensorflow/tensorboard)
    7. einops (arogozhnikov/einops)

    AI recommended 7 alternatives but never named openai/circuit_sparsity. 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 openai/circuit_sparsity?
    pass
    AI named openai/circuit_sparsity 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/circuit_sparsity in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/circuit_sparsity 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/circuit_sparsity solve, and who is the primary audience?
    pass
    AI named openai/circuit_sparsity explicitly

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

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openai/circuit_sparsity — 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