RRepoGEO

REPOGEO REPORT · LITE

UMass-Embodied-AGI/3D-LLM

Default branch main · commit 40002901 · scanned 6/30/2026, 1:23:00 PM

GitHub: 1,205 stars · 76 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
35 /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
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 UMass-Embodied-AGI/3D-LLM, 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
  • highhomepage#1
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://vis-www.cs.umass.edu/3dllm/
  • mediumreadme#2
    Clarify README's opening sentence to emphasize "codebase"

    Why:

    CURRENT
    3D-LLM is the first Large Language Model that could take 3D representations as inputs.
    COPY-PASTE FIX
    This repository provides the official codebase for 3D-LLM, the first Large Language Model that could take 3D representations as inputs.

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-LLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI's GPT-4V (Vision)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI's GPT-4V (Vision) · recommended 1×
  2. Google's PaLM-E · recommended 1×
  3. Meta's Segment Anything Model (SAM) · recommended 1×
  4. GPT-4 · recommended 1×
  5. Llama 2 · recommended 1×
  • CATEGORY QUERY
    How can I integrate 3D scene understanding capabilities into large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's GPT-4V (Vision)
    2. Google's PaLM-E
    3. Meta's Segment Anything Model (SAM)
    4. GPT-4
    5. Llama 2
    6. CLIP (Contrastive Language-Image Pre-training)
    7. Open3D
    8. PyTorch3D
    9. Neural Radiance Fields (NeRFs)
    10. Instant NGP
    11. OpenAI's Point-E
    12. Shap-E

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

    Show full AI answer
  • CATEGORY QUERY
    What tools allow large language models to process and interpret 3D object data?
    you: not recommended
    AI recommended (in order):
    1. Open3D (isl-org/Open3D)
    2. OpenAI API
    3. Anthropic API
    4. GPT-4V
    5. PyTorch3D (facebookresearch/pytorch3d)
    6. Kaolin (NVIDIA/kaolin)
    7. PyTorch (pytorch/pytorch)
    8. Gradio (gradio-app/gradio)
    9. Streamlit (streamlit/streamlit)
    10. Hugging Face Transformers (huggingface/transformers)
    11. Blender
    12. Unity
    13. Unreal Engine

    AI recommended 13 alternatives but never named UMass-Embodied-AGI/3D-LLM. 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-LLM?
    pass
    AI named UMass-Embodied-AGI/3D-LLM explicitly

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

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

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UMass-Embodied-AGI/3D-LLM — 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