RRepoGEO

REPOGEO REPORT · LITE

2toinf/X-VLA

Default branch main · commit ccd1992f · scanned 6/9/2026, 12:13:13 AM

GitHub: 671 stars · 62 forks

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 2toinf/X-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

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

OVERALL DIRECTION
  • highabout#1
    Refine the 'About' description for clarity

    Why:

    CURRENT
    [ICLR 2026] The offical Implementation of "Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model"
    COPY-PASTE FIX
    Official implementation of X-VLA, a state-of-the-art Vision-Language-Action (VLA) model for scalable, cross-embodiment robotic manipulation, accepted to ICLR 2026.
  • mediumreadme#2
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled "## 🆚 X-VLA vs. Other VLA Models" or "## 🚀 Why X-VLA?" that briefly highlights how X-VLA's soft-prompt mechanism and cross-embodiment scalability differentiate it from existing approaches like RT-1, RT-2, or Open X-Embodiment.
  • lowreadme#3
    Introduce a 'Key Features' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section titled "## ✨ Key Features" listing concrete benefits like "State-of-the-art generalization across diverse platforms," "Soft-prompt mechanism for multi-domain policy learning," and "Native integration with LeRobot."

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 2toinf/X-VLA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RT-1
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. RT-1 · recommended 2×
  2. RT-2 · recommended 2×
  3. Open X-Embodiment · recommended 1×
  4. RoboCat · recommended 1×
  5. RLBench · recommended 1×
  • CATEGORY QUERY
    How to train a single vision-language-action model across diverse robot platforms?
    you: not recommended
    AI recommended (in order):
    1. Open X-Embodiment
    2. RT-1
    3. RT-2
    4. RoboCat
    5. RLBench
    6. Diffusion Policy
    7. ACT (Action Chunking with Transformers)
    8. ROS 2 (Robot Operating System 2)
    9. MoveIt 2
    10. BehaviorTree.CPP
    11. Habitat 2.0
    12. ViNG (Vision-language Navigation with Gaze)
    13. VIMA (Vision-language Models for Action)
    14. OpenAI Gym
    15. Gymnasium
    16. Stable Baselines3
    17. RLlib

    AI recommended 17 alternatives but never named 2toinf/X-VLA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a foundation model for robotic manipulation using vision and language prompts.
    you: not recommended
    AI recommended (in order):
    1. RT-X
    2. RT-1
    3. RT-2
    4. OpenVLA
    5. PaLM-E
    6. CLIP
    7. ViT
    8. BERT
    9. T5

    AI recommended 9 alternatives but never named 2toinf/X-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
    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 2toinf/X-VLA?
    pass
    AI named 2toinf/X-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 2toinf/X-VLA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 2toinf/X-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 2toinf/X-VLA solve, and who is the primary audience?
    pass
    AI named 2toinf/X-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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite