RRepoGEO

REPOGEO REPORT · LITE

UMass-Embodied-AGI/3D-VLA

Default branch main · commit 517680c3 · scanned 6/2/2026, 4:28:30 AM

GitHub: 625 stars · 25 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 UMass-Embodied-AGI/3D-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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ["3d-vision", "language-models", "action-models", "generative-models", "world-models", "embodied-ai", "robotics", "icml-2024", "deep-learning", "machine-learning"]
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License) in the root of the repository.
  • highreadme#3
    Clarify 3D-VLA's role as a model, not a platform, in the README

    Why:

    COPY-PASTE FIX
    Insert the following sentence immediately after the main H1 title: "This repository provides the official implementation of 3D-VLA, a novel generative world model for embodied AI, offering a distinct approach from existing simulation platforms and toolkits."

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 UMass-Embodied-AGI/3D-VLA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Omniverse
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Omniverse · recommended 1×
  2. Unity 3D · recommended 1×
  3. ML-Agents · recommended 1×
  4. Meta's Habitat · recommended 1×
  5. Habitat-Matterport 3D Dataset - HM3D · recommended 1×
  • CATEGORY QUERY
    How to build a generative world model that integrates 3D vision, language, and action?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Omniverse
    2. Unity 3D
    3. ML-Agents
    4. Meta's Habitat
    5. Habitat-Matterport 3D Dataset - HM3D
    6. Google's DeepMind Lab
    7. OpenSpiel
    8. PyTorch3D
    9. TensorFlow Graphics
    10. Hugging Face Transformers
    11. OpenAI Gym
    12. Blender

    AI recommended 12 alternatives but never named UMass-Embodied-AGI/3D-VLA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for embodied AI agents to learn complex tasks in simulated 3D environments.
    you: not recommended
    AI recommended (in order):
    1. Unity ML-Agents Toolkit
    2. Meta AI Habitat
    3. NVIDIA Isaac Sim
    4. DeepMind Lab
    5. AI2-THOR
    6. RoboSchool

    AI recommended 6 alternatives but never named UMass-Embodied-AGI/3D-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
    warn

    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 UMass-Embodied-AGI/3D-VLA?
    pass
    AI did not name UMass-Embodied-AGI/3D-VLA — likely talking about a different project

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

  • If a team adopts UMass-Embodied-AGI/3D-VLA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named UMass-Embodied-AGI/3D-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 UMass-Embodied-AGI/3D-VLA solve, and who is the primary audience?
    pass
    AI named UMass-Embodied-AGI/3D-VLA explicitly

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

Embed your GEO score

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UMass-Embodied-AGI/3D-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