RRepoGEO

REPOGEO REPORT · LITE

andyzoujm/representation-engineering

Default branch main · commit 5455d8a3 · scanned 7/1/2026, 10:06:52 AM

GitHub: 1,009 stars · 130 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the README H1 and opening sentence to clarify project type

    Why:

    CURRENT
    # Representation Engineering (RepE)
This is the official repository for "Representation Engineering: A Top-Down Approach to AI Transparency"
    COPY-PASTE FIX
    # Representation Engineering (RepE): A Research Framework for AI Transparency
This is the official repository for the research paper "Representation Engineering: A Top-Down Approach to AI Transparency"
  • mediumreadme#2
    Emphasize the unique 'top-down' approach and distinction from tools in the README introduction

    Why:

    CURRENT
    In this paper, we introduce and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs).
    COPY-PASTE FIX
    In this paper, we introduce and characterize Representation Engineering (RepE), an emerging top-down approach to enhancing AI transparency. Unlike traditional interpretability tools, RepE places population-level representations, rather than individual neurons or circuits, at the center of analysis. This equips us with novel methods for directly monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs), drawing insights from cognitive neuroscience.

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
Captum
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Captum · recommended 2×
  2. LIT (Language Interpretability Tool) · recommended 1×
  3. SHAP (SHapley Additive exPlanations) · recommended 1×
  4. LIME (Local Interpretable Model-agnostic Explanations) · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    What methods help monitor and manipulate high-level cognitive phenomena in large language models?
    you: not recommended
    AI recommended (in order):
    1. LIT (Language Interpretability Tool)
    2. Captum
    3. SHAP (SHapley Additive exPlanations)
    4. LIME (Local Interpretable Model-agnostic Explanations)
    5. LangChain
    6. LlamaIndex
    7. Semantic Kernel
    8. MEMIT (Mass-Editing Memory in a Transformer)
    9. ROME (Rank-One Model Editing)
    10. DeepMind's AlphaCode
    11. TextAttack
    12. CleverHans

    AI recommended 12 alternatives but never named andyzoujm/representation-engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking frameworks to enhance AI transparency and control emergent behaviors in deep neural networks effectively?
    you: not recommended
    AI recommended (in order):
    1. LIME
    2. SHAP
    3. Captum
    4. Microsoft InterpretML
    5. Google What-If Tool
    6. IBM AI Explainability 360
    7. Fiddler AI
    8. Alibi Explain

    AI recommended 8 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?

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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