RRepoGEO

REPOGEO REPORT · LITE

Thinklab-SJTU/Awesome-LLM4AD

Default branch main · commit 30f4a577 · scanned 5/17/2026, 6:38:18 AM

GitHub: 1,815 stars · 107 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 Thinklab-SJTU/Awesome-LLM4AD, 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 'awesome-list' topic

    Why:

    CURRENT
    large-language-models, vision-language-action-model, vision-language-model, world-model
    COPY-PASTE FIX
    large-language-models, vision-language-action-model, vision-language-model, world-model, awesome-list
  • highhomepage#2
    Set the repository homepage to the survey paper

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2311.01043
  • mediumabout#3
    Enhance the 'About' description to highlight its utility for finding models

    Why:

    CURRENT
    A curated list of awesome LLM/VLM/VLA/World Model for Autonomous Driving(LLM4AD) resources (continually updated)
    COPY-PASTE FIX
    A curated list of awesome LLM/VLM/VLA/World Model for Autonomous Driving (LLM4AD) resources, helping you discover the best models and research (continually updated).

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 Thinklab-SJTU/Awesome-LLM4AD
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 1×
  2. Claude 3 Opus · recommended 1×
  3. Gemini 1.5 Pro · recommended 1×
  4. Llama 3 · recommended 1×
  5. Mixtral 8x7B · recommended 1×
  • CATEGORY QUERY
    What are the best large language models for autonomous driving applications?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Gemini 1.5 Pro
    4. Llama 3
    5. Mixtral 8x7B
    6. Falcon 180B
    7. Grok-1

    AI recommended 7 alternatives but never named Thinklab-SJTU/Awesome-LLM4AD. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can vision-language models improve decision-making in autonomous vehicle systems?
    you: not recommended
    AI recommended (in order):
    1. GPT-4V (Vision)
    2. Google Gemini (Pro/Ultra)
    3. Meta LLaVA (Large Language and Vision Assistant)
    4. Microsoft Florence-2
    5. OpenAI GPT-4
    6. Google PaLM 2
    7. CLIP (Contrastive Language-Image Pre-training)
    8. DALL-E 3

    AI recommended 8 alternatives but never named Thinklab-SJTU/Awesome-LLM4AD. 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 Thinklab-SJTU/Awesome-LLM4AD?
    pass
    AI named Thinklab-SJTU/Awesome-LLM4AD explicitly

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

  • If a team adopts Thinklab-SJTU/Awesome-LLM4AD in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Thinklab-SJTU/Awesome-LLM4AD 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 Thinklab-SJTU/Awesome-LLM4AD solve, and who is the primary audience?
    pass
    AI did not name Thinklab-SJTU/Awesome-LLM4AD — 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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Thinklab-SJTU/Awesome-LLM4AD — 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