RRepoGEO

REPOGEO REPORT · LITE

GAIR-NLP/ASI-Arch

Default branch main · commit 3113c51b · scanned 5/9/2026, 6:58:01 PM

GitHub: 1,164 stars · 211 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 GAIR-NLP/ASI-Arch, 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's opening paragraph to differentiate from generic NAS tools

    Why:

    CURRENT
    This is the official repository for our work "AlphaGo Moment for Model Architecture Discovery". We present a highly autonomous, multi-agent framework that empowers a Large Language Model (LLM) to conduct end-to-end scientific research in the challenging domain of linear attention mechanisms.
    COPY-PASTE FIX
    This is the official repository for our work "AlphaGo Moment for Model Architecture Discovery". We introduce ASI-Arch, a highly autonomous, multi-agent framework that empowers a Large Language Model (LLM) to conduct end-to-end scientific research. Unlike traditional Neural Architecture Search (NAS) or AutoML tools, ASI-Arch focuses on the challenging domain of linear attention mechanisms, enabling LLM agents to autonomously hypothesize, implement, and empirically validate novel architectures.
  • mediumabout#2
    Enhance the repository's 'About' description

    Why:

    CURRENT
    AlphaGo Moment for Model Architecture Discovery.
    COPY-PASTE FIX
    AlphaGo Moment for Model Architecture Discovery: An LLM-powered multi-agent framework for autonomous scientific research into novel neural architectures, specifically linear attention mechanisms.

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 GAIR-NLP/ASI-Arch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AutoML-Zero
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AutoML-Zero · recommended 1×
  2. AutoKeras · recommended 1×
  3. NNI · recommended 1×
  4. PyTorch-ENAS / PyTorch-DARTS · recommended 1×
  5. Ray Tune · recommended 1×
  • CATEGORY QUERY
    How can I autonomously discover novel neural network architectures using AI agents?
    you: not recommended
    AI recommended (in order):
    1. AutoML-Zero
    2. AutoKeras
    3. NNI
    4. PyTorch-ENAS / PyTorch-DARTS
    5. Ray Tune
    6. FLAML
    7. DeepMind's AlphaZero/MuZero-inspired approaches

    AI recommended 7 alternatives but never named GAIR-NLP/ASI-Arch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help automate the design and validation of new linear attention mechanisms?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. JAX
    3. TensorFlow
    4. OpenAI Triton
    5. FlashAttention
    6. Optimum Graphcore
    7. TVM

    AI recommended 7 alternatives but never named GAIR-NLP/ASI-Arch. 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 GAIR-NLP/ASI-Arch?
    pass
    AI named GAIR-NLP/ASI-Arch explicitly

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

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

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

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GAIR-NLP/ASI-Arch — 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