RRepoGEO

REPOGEO REPORT · LITE

OpenHelix-Team/VLA-Adapter

Default branch main · commit 23fa0c9c · scanned 5/18/2026, 2:43:11 AM

GitHub: 2,162 stars · 195 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 OpenHelix-Team/VLA-Adapter, 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 H1 to define VLA and its purpose

    Why:

    CURRENT
    ### The official implementation of **VLA-Adapter**.
    COPY-PASTE FIX
    ### The official implementation of **VLA-Adapter**.
VLA-Adapter is a novel paradigm for efficiently deploying **Vision-Language-Action (VLA) models** on tiny-scale robotic platforms, enabling adaptable frameworks for real-world robotics applications.
  • mediumabout#2
    Clarify the 'VLA' acronym and application in the repository description

    Why:

    CURRENT
    VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
    COPY-PASTE FIX
    VLA-Adapter: An effective paradigm for tiny-scale **Vision-Language-Action (VLA) models** in embodied AI and robotics, enabling efficient deployment on small platforms.
  • lowreadme#3
    Add a 'What is VLA-Adapter?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, immediately after the introductory paragraph, with a heading like 'What is VLA-Adapter?' or 'Key Features'. Include a sentence like: 'VLA-Adapter is a novel **paradigm and model architecture** designed to overcome the challenges of deploying complex Vision-Language-Action models on resource-constrained robotic platforms, offering an adaptable framework for real-world applications.'

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 OpenHelix-Team/VLA-Adapter
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Jetson Series
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Jetson Series · recommended 1×
  2. NVIDIA TensorRT · recommended 1×
  3. OpenVINO Toolkit · recommended 1×
  4. ONNX Runtime · recommended 1×
  5. TensorRT Execution Provider · recommended 1×
  • CATEGORY QUERY
    How to efficiently deploy vision-language-action models for embodied AI on small-scale robotic platforms?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Jetson Series
    2. NVIDIA TensorRT
    3. OpenVINO Toolkit
    4. ONNX Runtime
    5. TensorRT Execution Provider
    6. OpenVINO Execution Provider
    7. NNAPI Execution Provider
    8. ARM Compute Library Execution Provider
    9. Edge TPU
    10. TensorFlow Lite
    11. GPU delegate
    12. NNAPI delegate
    13. Edge TPU delegate
    14. PyTorch Mobile
    15. LibTorch
    16. TorchScript
    17. Qualcomm Robotics RBx Platforms
    18. Qualcomm Neural Processing SDK (SNPE)

    AI recommended 18 alternatives but never named OpenHelix-Team/VLA-Adapter. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an adaptable framework for integrating vision, language, and action in real-world robotics applications.
    you: not recommended
    AI recommended (in order):
    1. ROS (Robot Operating System)
    2. MoveIt!
    3. OpenCV
    4. PyRobot
    5. Robotics Toolbox for MATLAB
    6. Google Robotics (Robotics at Google)
    7. RT-X (Robotics Transformer X)
    8. TensorFlow
    9. JAX
    10. NVIDIA Isaac Sim
    11. Isaac SDK
    12. OpenAI Gym

    AI recommended 12 alternatives but never named OpenHelix-Team/VLA-Adapter. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 OpenHelix-Team/VLA-Adapter?
    pass
    AI named OpenHelix-Team/VLA-Adapter explicitly

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

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

    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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OpenHelix-Team/VLA-Adapter — 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