RRepoGEO

REPOGEO REPORT · LITE

kuleshov-group/awesome-discrete-diffusion-models

Default branch main · commit 9cfdddb6 · scanned 6/11/2026, 5:28:25 AM

GitHub: 562 stars · 23 forks

AI VISIBILITY SCORE
17 /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
1 / 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 kuleshov-group/awesome-discrete-diffusion-models, 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 relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    awesome-list, discrete-diffusion-models, diffusion-models, machine-learning, deep-learning, ai, research
  • highreadme#2
    Reposition the README's opening sentence to emphasize 'awesome list'

    Why:

    CURRENT
    A curated list of awesome discrete diffusion models resources.
    COPY-PASTE FIX
    This is an **awesome list** of curated resources for discrete diffusion models.
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root.

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 kuleshov-group/awesome-discrete-diffusion-models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Diffusers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Diffusers Library · recommended 1×
  2. PyTorch Geometric (PyG) · recommended 1×
  3. DeepMind's Graph Diffusion Models · recommended 1×
  4. D3PM · recommended 1×
  5. Masked Diffusion Transformer (MDT) · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources to understand and implement discrete diffusion models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers Library
    2. PyTorch Geometric (PyG)
    3. DeepMind's Graph Diffusion Models

    AI recommended 3 alternatives but never named kuleshov-group/awesome-discrete-diffusion-models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest advancements and techniques in discrete diffusion models for various applications?
    you: not recommended
    AI recommended (in order):
    1. D3PM
    2. Masked Diffusion Transformer (MDT)
    3. Discrete Diffusion for Text Generation (DDTG)
    4. VQ-Diffusion
    5. VQ-VAEs
    6. Discrete Latent Diffusion (DLD)
    7. Absorbing Diffusion Models
    8. Generalized Discrete Diffusion (GDD)
    9. DiGress
    10. DiffDock
    11. Graph Diffusion for Molecular Design
    12. Classifier-Free Guidance for Discrete Diffusion

    AI recommended 12 alternatives but never named kuleshov-group/awesome-discrete-diffusion-models. 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 kuleshov-group/awesome-discrete-diffusion-models?
    pass
    AI did not name kuleshov-group/awesome-discrete-diffusion-models — 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?

  • If a team adopts kuleshov-group/awesome-discrete-diffusion-models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kuleshov-group/awesome-discrete-diffusion-models 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 kuleshov-group/awesome-discrete-diffusion-models solve, and who is the primary audience?
    pass
    AI did not name kuleshov-group/awesome-discrete-diffusion-models — 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?

Embed your GEO score

Drop this badge into the README of kuleshov-group/awesome-discrete-diffusion-models. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/kuleshov-group/awesome-discrete-diffusion-models.svg)](https://repogeo.com/en/r/kuleshov-group/awesome-discrete-diffusion-models)
HTML
<a href="https://repogeo.com/en/r/kuleshov-group/awesome-discrete-diffusion-models"><img src="https://repogeo.com/badge/kuleshov-group/awesome-discrete-diffusion-models.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

kuleshov-group/awesome-discrete-diffusion-models — 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