REPOGEO REPORT · LITE

OpenHelix-Team/VLA-Adapter

Default branch main · commit 23fa0c9c · scanned 6/29/2026, 8:18:28 AM

GitHub: 2,218 stars · 201 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

Explicitly define 'VLA' in the README's main title/heading

Why:

CURRENT

### The official implementation of **VLA-Adapter**.

COPY-PASTE FIX

### The official implementation of **VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model**.

highreadme#2

Add a concise introductory paragraph defining the project's purpose

Why:

COPY-PASTE FIX

VLA-Adapter introduces a novel approach to developing tiny-scale vision-language-action models, specifically designed for efficient deployment in embodied AI and robotics applications. It addresses the challenge of enabling robots to understand and act upon complex visual and linguistic instructions in real-world environments.

mediumreadme#3

Add a 'Key Features' or 'Why VLA-Adapter?' section

Why:

COPY-PASTE FIX

## :star: Key Features
- **Tiny-Scale Efficiency:** Optimized for resource-constrained environments, enabling deployment on edge devices.
- **Real-World Robotics:** Verified support for real-world robotic control, including ALOHA deployment.
- **Vision-Language-Action Integration:** Seamlessly connects visual perception, language understanding, and physical action for embodied agents.
- **Enhanced Performance:** The Pro version offers optimized implementation conforming to the original paper's pipeline.

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

Of all named tools, what % are you?

Top rival

CLIP

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

CLIP · recommended 2×
RT-1 · recommended 1×
MobileCLIP · recommended 1×
Perceiver IO · recommended 1×
BC-Z · recommended 1×

CATEGORY QUERY
What are effective paradigms for tiny-scale vision-language-action models in embodied AI?
you: not recommended
AI recommended (in order):
1. CLIP
2. RT-1
3. MobileCLIP
4. Perceiver IO
5. BC-Z
6. ViT-Lite
7. TinyViT
AI recommended 7 alternatives but never named OpenHelix-Team/VLA-Adapter. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to deploy vision-language models for real-world robotic control and manipulation?
you: not recommended
AI recommended (in order):
1. OpenVLA
2. RoboCat
3. RT-X
4. CLIP
5. ROS
6. NVIDIA Isaac Sim
7. OWL-ViT
8. MoveIt!
9. Hugging Face Transformers Library
10. LLaVA
11. BLIP-2
12. Diffusion Policy
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?

Embed your GEO score

Drop this badge into the README of OpenHelix-Team/VLA-Adapter. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/OpenHelix-Team/VLA-Adapter.svg)](https://repogeo.com/en/r/OpenHelix-Team/VLA-Adapter)

HTML

<a href="https://repogeo.com/en/r/OpenHelix-Team/VLA-Adapter"><img src="https://repogeo.com/badge/OpenHelix-Team/VLA-Adapter.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

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