RRepoGEO

REPOGEO REPORT · LITE

QwenLM/Qwen-VLA

Default branch main · commit fa25ab1e · scanned 6/10/2026, 10:12:45 PM

GitHub: 548 stars · 20 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
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 QwenLM/Qwen-VLA, 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

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

OVERALL DIRECTION
  • mediumlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the chosen open-source license (e.g., Apache-2.0, MIT).

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 QwenLM/Qwen-VLA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GATO
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GATO · recommended 2×
  2. RT-X · recommended 1×
  3. PaLM-E · recommended 1×
  4. Open-VLA · recommended 1×
  5. CLIP · recommended 1×
  • CATEGORY QUERY
    What models integrate vision, language, and action for generalist robot control tasks?
    you: not recommended
    AI recommended (in order):
    1. RT-X
    2. PaLM-E
    3. Open-VLA
    4. GATO
    5. CLIP
    6. SayCan

    AI recommended 6 alternatives but never named QwenLM/Qwen-VLA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a unified embodied AI model capable of learning from diverse robot manipulation data.
    you: not recommended
    AI recommended (in order):
    1. Open X-Embodiment
    2. Robotics Transformer
    3. GATO
    4. Diffusion Policy
    5. Perceiver IO
    6. R3M

    AI recommended 6 alternatives but never named QwenLM/Qwen-VLA. 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 QwenLM/Qwen-VLA?
    pass
    AI named QwenLM/Qwen-VLA explicitly

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

  • If a team adopts QwenLM/Qwen-VLA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named QwenLM/Qwen-VLA 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 QwenLM/Qwen-VLA solve, and who is the primary audience?
    pass
    AI named QwenLM/Qwen-VLA explicitly

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

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QwenLM/Qwen-VLA — 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