RRepoGEO

REPOGEO REPORT · LITE

ML-GSAI/LLaDA

Default branch main · commit b7e6c356 · scanned 6/27/2026, 3:07:37 AM

GitHub: 3,848 stars · 268 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to clarify project domain

    Why:

    CURRENT
    The README currently starts with links and a "News" section, lacking an immediate, clear statement of purpose.
    COPY-PASTE FIX
    Add the following paragraph immediately after the main title `# Large Language Diffusion Models`:
`ML-GSAI/LLaDA is the official PyTorch implementation for Large Language Diffusion Models, a novel approach to generating high-quality text sequences and building efficient large language models. This repository provides code and models for various LLaDA iterations, including LLaDA-V (vision-language), LLaDA 1.5 (preference alignment), LLaDA-MoE (Mixture-of-Experts), and iLLaDA (improved efficiency).`
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ["diffusion-models", "large-language-models", "llm", "pytorch", "generative-ai", "text-generation", "mixture-of-experts", "moe", "vision-language-models"]
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root containing the text of a standard open-source license, such as the MIT License.

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
Diffusion-LM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Diffusion-LM · recommended 1×
  2. Diffusion-BERT · recommended 1×
  3. Masked Diffusion Transformer (MDT) · recommended 1×
  4. VQ-VAE + Diffusion · recommended 1×
  5. DiffuSeq · recommended 1×
  • CATEGORY QUERY
    How can I use diffusion models to generate high-quality text sequences?
    you: not recommended
    AI recommended (in order):
    1. Diffusion-LM
    2. Diffusion-BERT
    3. Masked Diffusion Transformer (MDT)
    4. VQ-VAE + Diffusion
    5. DiffuSeq

    AI recommended 5 alternatives but never named ML-GSAI/LLaDA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are current approaches for building efficient large language models with MoE architecture?
    you: not recommended
    AI recommended (in order):
    1. Fairseq
    2. DeepSpeed
    3. Megatron-LM
    4. Hugging Face Transformers Library
    5. JAX
    6. Flax
    7. OpenMoE
    8. Colossal-AI

    AI recommended 8 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
    fail

    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