RRepoGEO

REPOGEO REPORT · LITE

magic-research/bubogpt

Default branch main · commit 17c72b50 · scanned 6/7/2026, 8:18:29 AM

GitHub: 510 stars · 35 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 magic-research/bubogpt, 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 improve categorization

    Why:

    COPY-PASTE FIX
    multi-modal-llm, visual-grounding, large-language-models, computer-vision, nlp, audio-processing, deep-learning, ai, research
  • highreadme#2
    Add a direct, concise opening statement to the README

    Why:

    COPY-PASTE FIX
    This repository presents BuboGPT, an open-source research project for multi-modal large language models (LLMs) with visual grounding capabilities.

# BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs
  • mediumreadme#3
    Add a 'Related Work' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Related Work / Comparison

BuboGPT builds upon recent advancements in multi-modal LLMs, offering unique capabilities in visual grounding. While projects like LLaVA and MiniGPT-4 focus on vision-language understanding, BuboGPT extends this to include audio and emphasizes grounding knowledge directly into visual objects. Our approach differs from foundational models like OpenAI CLIP or Meta's DINOv2 by integrating these capabilities within a generative LLM framework.

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 magic-research/bubogpt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI CLIP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI CLIP · recommended 2×
  2. PyTorch · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. timm · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How to develop an AI model that processes text, vision, and audio with visual grounding?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Hugging Face Transformers
    3. timm
    4. TensorFlow
    5. Keras
    6. TensorFlow Hub
    7. OpenAI CLIP
    8. DALL-E 2
    9. DALL-E 3
    10. MMDetection
    11. MMFlow
    12. Fairseq

    AI recommended 12 alternatives but never named magic-research/bubogpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable large language models to understand and ground knowledge in visual objects?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Meta's DINOv2
    3. Google's PaLM-E
    4. Microsoft's Florence-2
    5. LLaVA
    6. BLIP-2

    AI recommended 6 alternatives but never named magic-research/bubogpt. 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 magic-research/bubogpt?
    pass
    AI did not name magic-research/bubogpt — 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 magic-research/bubogpt in production, what risks or prerequisites should they evaluate first?
    pass
    AI named magic-research/bubogpt 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 magic-research/bubogpt solve, and who is the primary audience?
    pass
    AI named magic-research/bubogpt explicitly

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

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magic-research/bubogpt — 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