RRepoGEO

REPOGEO REPORT · LITE

andyzoujm/representation-engineering

Default branch main · commit 5455d8a3 · scanned 6/1/2026, 10:02:02 AM

GitHub: 1,001 stars · 128 forks

AI VISIBILITY SCORE
28 /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
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 andyzoujm/representation-engineering, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening statement

    Why:

    CURRENT
    # Representation Engineering (RepE)
This is the official repository for "Representation Engineering: A Top-Down Approach to AI Transparency"  
by Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, Zico Kolter, and Dan Hendrycks.
    COPY-PASTE FIX
    # Representation Engineering (RepE)
This repository provides the official code and resources for "Representation Engineering: A Top-Down Approach to AI Transparency." Unlike post-hoc interpretability methods, RepE offers novel techniques to directly monitor and manipulate high-level cognitive phenomena within deep neural networks, enhancing control and safety in large language models.

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 andyzoujm/representation-engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LIME
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LIME · recommended 2×
  2. SHAP · recommended 2×
  3. InterpretML · recommended 1×
  4. Captum · recommended 1×
  5. Ecco · recommended 1×
  • CATEGORY QUERY
    How to achieve greater transparency and interpretability in large language models?
    you: not recommended
    AI recommended (in order):
    1. LIME
    2. SHAP
    3. InterpretML
    4. Captum
    5. Ecco
    6. ELI5

    AI recommended 6 alternatives but never named andyzoujm/representation-engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What methods exist for manipulating high-level cognitive phenomena in deep neural networks?
    you: not recommended
    AI recommended (in order):
    1. CBMs
    2. PCBMs
    3. FGSM
    4. PGD
    5. DeepFool
    6. DeepDream
    7. Lucid
    8. Activation Atlas
    9. Beta-VAE
    10. FactorVAE
    11. InfoGAN
    12. CCE
    13. SHAP
    14. LIME
    15. AlphaGo
    16. NS-CL

    AI recommended 16 alternatives but never named andyzoujm/representation-engineering. 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 andyzoujm/representation-engineering?
    pass
    AI named andyzoujm/representation-engineering explicitly

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

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