RRepoGEO

REPOGEO REPORT · LITE

ML-GSAI/LLaDA

Default branch main · commit 570f2903 · scanned 5/16/2026, 7:37:58 AM

GitHub: 3,788 stars · 265 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 ML-GSAI/LLaDA, 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 core definition of LLaDA to the top of the README

    Why:

    CURRENT
    The README's 'Introduction' section appears after 'News' and is truncated, starting with 'We introduce LLaDA (<b>L</b>ar'.
    COPY-PASTE FIX
    Insert this text immediately after the H1 and initial links, before the 'News' section: 'This repository provides the official PyTorch implementation for LLaDA (Large Language Diffusion Models), a cutting-edge research project exploring diffusion-based architectures for natural language generation, including novel Mixture-of-Experts (MoE) models and vision-language extensions.'
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2502.09992

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 ML-GSAI/LLaDA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google's Diffusion-LM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google's Diffusion-LM · recommended 1×
  2. DALL-E 3 · recommended 1×
  3. Stable Diffusion · recommended 1×
  4. GLIDE · recommended 1×
  5. Latent Diffusion Models · recommended 1×
  • CATEGORY QUERY
    What are effective diffusion-based models for generating high-quality natural language text?
    you: not recommended
    AI recommended (in order):
    1. Google's Diffusion-LM
    2. DALL-E 3
    3. Stable Diffusion
    4. GLIDE
    5. Latent Diffusion Models
    6. Hugging Face Diffusers

    AI recommended 6 alternatives but never named ML-GSAI/LLaDA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for efficient language models leveraging mixture-of-experts architecture for scalability.
    you: not recommended
    AI recommended (in order):
    1. Mixtral 8x7B
    2. Google's Switch Transformers
    3. Grok-1
    4. DeepMind's GLaM
    5. Fairseq (facebookresearch/fairseq)

    AI recommended 5 alternatives but never named ML-GSAI/LLaDA. 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 ML-GSAI/LLaDA?
    pass
    AI named ML-GSAI/LLaDA explicitly

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

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

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

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ML-GSAI/LLaDA — 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