REPOGEO REPORT · LITE

knightnemo/Awesome-World-Models

Default branch main · commit b8a40f1e · scanned 5/22/2026, 5:18:17 AM

GitHub: 2,857 stars · 116 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

28 /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

2 / 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 knightnemo/Awesome-World-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

highreadme#1

Reposition the README's opening to clarify its role as a curated index vs. raw sources

Why:

CURRENT

**📜 A Curated List of Amazing Works in World Modeling, spanning applications in Embodied AI, Autonomous Driving, Natural Language Processing and Agents.**

COPY-PASTE FIX

**📜 A Curated List of Amazing Works in World Modeling, serving as a one-stop resource to navigate the vast landscape of Embodied AI, Autonomous Driving, Natural Language Processing, and Agents, without sifting through individual papers or platforms.**

highhomepage#2
Add the repository URL as the homepage
Why:
COPY-PASTE FIX
```
https://github.com/knightnemo/Awesome-World-Models
```

mediumtopics#3

Expand topics to include specific application areas mentioned in the README

Why:

CURRENT

awesome-list, dynamical-systems, embodied-ai, generative-model, world-models

COPY-PASTE FIX

awesome-list, dynamical-systems, embodied-ai, generative-model, world-models, autonomous-driving, nlp, natural-language-processing, agents, robotics

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 knightnemo/Awesome-World-Models

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Papers With Code

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Papers With Code · recommended 1×
arXiv.org · recommended 1×
Distill.pub · recommended 1×
OpenAI · recommended 1×
DeepMind · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive resource for world modeling research and applications?
you: not recommended
AI recommended (in order):
1. Papers With Code
2. arXiv.org
3. Distill.pub
4. OpenAI
5. DeepMind
6. World Models by Ha and Schmidhuber (2018)
7. Google Scholar
AI recommended 7 alternatives but never named knightnemo/Awesome-World-Models. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the leading approaches and papers in generative models for embodied AI?
you: not recommended
AI recommended (in order):
1. Diffusion Policy
2. Diffuser
3. RoboDiff
4. GIRAFFE
5. StyleNeRF
6. SimGAN
7. Variational Skill Discovery (DIAYN)
8. Learning Latent Plans from Play
9. Decision Transformer
10. Gato
11. Perceiver IO
AI recommended 11 alternatives but never named knightnemo/Awesome-World-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
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 knightnemo/Awesome-World-Models?
pass
AI named knightnemo/Awesome-World-Models explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts knightnemo/Awesome-World-Models in production, what risks or prerequisites should they evaluate first?
pass
AI named knightnemo/Awesome-World-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 knightnemo/Awesome-World-Models solve, and who is the primary audience?
pass
AI did not name knightnemo/Awesome-World-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?

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MARKDOWN (README)

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Pro

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knightnemo/Awesome-World-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