REPOGEO REPORT · LITE

LMD0311/Awesome-World-Model

Default branch main · commit 532932d1 · scanned 6/27/2026, 2:12:45 AM

GitHub: 2,132 stars · 84 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

22 /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

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 LMD0311/Awesome-World-Model, 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

highabout#1

Update 'About' description to clarify repository type

Why:

CURRENT

Collect some World Models for Autonomous Driving (and Robotic, etc.) papers.

COPY-PASTE FIX

A curated list and survey of World Models for Autonomous Driving, Robotics, and related research papers.

highlicense#2
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
MIT License
```

mediumreadme#3

Strengthen README's opening to emphasize 'curated list/survey'

Why:

CURRENT

This repo is used for recording, tracking, and benchmarking several recent World Models (for Autonomous Driving or Robotic) methods, as a supplement to our **survey**.

COPY-PASTE FIX

This repository serves as a comprehensive, curated list and survey for recording, tracking, and benchmarking recent World Models (for Autonomous Driving or Robotic) methods.

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 LMD0311/Awesome-World-Model

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Waymo's Motion Transformer

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Waymo's Motion Transformer · recommended 1×
Google's Trajectory Transformer · recommended 1×
Interaction-aware Prediction Network (IPN) · recommended 1×
Multi-Agent Trajectory Prediction with Graph Convolutional Networks (MATP-GCN) · recommended 1×
Social GAN · recommended 1×

CATEGORY QUERY
What AI models are best for predicting future states in autonomous driving scenarios?
you: not recommended
AI recommended (in order):
1. Waymo's Motion Transformer
2. Google's Trajectory Transformer
3. Interaction-aware Prediction Network (IPN)
4. Multi-Agent Trajectory Prediction with Graph Convolutional Networks (MATP-GCN)
5. Social GAN
6. TrajVAE
7. Social LSTM
8. PredRNN
9. ST-CNN (Spatio-Temporal CNN)
10. Kalman Filters
11. Extended Kalman Filters (EKF)
12. Unscented Kalman Filters (UKF)
AI recommended 12 alternatives but never named LMD0311/Awesome-World-Model. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I build a robust predictive environment model for robotic control systems?
you: not recommended
AI recommended (in order):
1. ROS
2. MoveIt!
3. Navigation Stack
4. OpenCV
5. PCL
6. Gazebo
7. MuJoCo
8. Isaac Sim
9. TensorFlow
10. PyTorch
11. GTSAM
12. Ceres Solver
13. Eigen
AI recommended 13 alternatives but never named LMD0311/Awesome-World-Model. 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 LMD0311/Awesome-World-Model?
pass
AI did not name LMD0311/Awesome-World-Model — 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 LMD0311/Awesome-World-Model in production, what risks or prerequisites should they evaluate first?
pass
AI named LMD0311/Awesome-World-Model 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 LMD0311/Awesome-World-Model solve, and who is the primary audience?
pass
AI did not name LMD0311/Awesome-World-Model — 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

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MARKDOWN (README)

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LMD0311/Awesome-World-Model — 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