RRepoGEO

REPOGEO REPORT · LITE

juruobenruo/DexVLA

Default branch main · commit fc21a822 · scanned 6/12/2026, 2:23:30 AM

GitHub: 61 stars · 9 forks

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 juruobenruo/DexVLA, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    DexVLA is a Vision-Language Model (VLM) with a plug-in diffusion expert for visuomotor policy learning, designed for robotic control tasks.
  • mediumreadme#2
    Add a section clarifying the project's license

    Why:

    COPY-PASTE FIX
    ## License
This project is released under the terms specified in the `LICENSE` file. Please refer to that file for full details on the applicable license(s).

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 juruobenruo/DexVLA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 2×
  2. OpenAI's CLIP (Contrastive Language-Image Pre-training) · recommended 1×
  3. Google's PaLM-E (Pathways Language Model Embodied) · recommended 1×
  4. Meta's DINOv2 (Self-supervised Vision Transformer) · recommended 1×
  5. LLaMA · recommended 1×
  • CATEGORY QUERY
    How to train a vision-language model for effective visuomotor policy learning?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's CLIP (Contrastive Language-Image Pre-training)
    2. Google's PaLM-E (Pathways Language Model Embodied)
    3. Meta's DINOv2 (Self-supervised Vision Transformer)
    4. LLaMA
    5. OPT
    6. Hugging Face Transformers Library
    7. PyTorch
    8. TensorFlow
    9. BERT
    10. RoBERTa
    11. T5
    12. LLaMA-2
    13. torchvision.models
    14. timm library
    15. tf.keras.applications
    16. RLBench
    17. PerAct (Perceiver-Actor)
    18. Robotics Transformers (RT-1)
    19. Robotics Transformers (RT-2)

    AI recommended 19 alternatives but never named juruobenruo/DexVLA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools to integrate diffusion models with VLMs for robotic control tasks.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Diffusers
    3. PyTorch Lightning
    4. JAX
    5. Flax
    6. Robotics Operating System (ROS)
    7. ROS 2
    8. OpenAI Gym
    9. Isaac Gym
    10. MuJoCo
    11. TensorFlow
    12. Keras

    AI recommended 12 alternatives but never named juruobenruo/DexVLA. 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 juruobenruo/DexVLA?
    pass
    AI named juruobenruo/DexVLA explicitly

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

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

    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 juruobenruo/DexVLA. 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/juruobenruo/DexVLA.svg)](https://repogeo.com/en/r/juruobenruo/DexVLA)
HTML
<a href="https://repogeo.com/en/r/juruobenruo/DexVLA"><img src="https://repogeo.com/badge/juruobenruo/DexVLA.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

juruobenruo/DexVLA — 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