REPOGEO REPORT · LITE

GAIR-NLP/ASI-Arch

Default branch main · commit 3113c51b · scanned 6/19/2026, 4:22:41 PM

GitHub: 1,172 stars · 217 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)

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

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

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

OVERALL DIRECTION

hightopics#1

Add specific topics to improve categorization

Why:

COPY-PASTE FIX

llm, model-architecture-discovery, neural-architecture-search, autonomous-ai, multi-agent-system, deep-learning, nlp, attention-mechanisms

highreadme#2

Reposition README opening to emphasize 'LLM-powered system' for architecture discovery

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 **ASI-Arch, an LLM-powered autonomous multi-agent system** for end-to-end scientific research and discovery of novel deep learning model architectures, specifically focusing on linear attention mechanisms. It represents an 'AlphaGo Moment' for automated model architecture discovery.

mediumabout#3

Update the 'About' description for clarity on function

Why:

CURRENT

AlphaGo Moment for Model Architecture Discovery.

COPY-PASTE FIX

An LLM-powered autonomous multi-agent system for discovering novel deep learning model architectures, particularly 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

Of all named tools, what % are you?

Top rival

PyTorch

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

PyTorch · recommended 2×
TensorFlow · recommended 2×
Ray Tune · recommended 2×
Optuna · recommended 2×
AutoKeras · recommended 2×

CATEGORY QUERY
How can I use LLMs to autonomously discover novel deep learning model architectures?
you: not recommended
AI recommended (in order):
1. NEAT (NeuroEvolution of Augmenting Topologies)
2. DeepNEAT
3. PyTorch
4. TensorFlow
5. OpenAI Codex
6. GitHub Copilot
7. GPT-4
8. Hugging Face Transformers
9. Ray Tune
10. Optuna
11. Stable Baselines3
12. RLlib
13. LangChain
14. LlamaIndex
15. spaCy
16. NLTK
17. Google Cloud AutoML
18. Vertex AI Neural Architecture Search
19. Microsoft NNI (Neural Network Intelligence)
20. AutoKeras
AI recommended 20 alternatives but never named GAIR-NLP/ASI-Arch. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help generate and validate new linear attention mechanisms automatically?
you: not recommended
AI recommended (in order):
1. PyTorch
2. JAX
3. Flax
4. Haiku
5. TensorFlow
6. Keras
7. Optuna
8. Ray Tune
9. AutoKeras
10. NNI
11. gplearn
12. PySR
AI recommended 12 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?

Embed your GEO score

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MARKDOWN (README)

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HTML

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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