RRepoGEO

REPOGEO REPORT · LITE

jy0205/LaVIT

Default branch main · commit 8cde9b4f · scanned 6/15/2026, 4:32:51 PM

GitHub: 601 stars · 32 forks

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 jy0205/LaVIT, 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:

    CURRENT
    (none)
    COPY-PASTE FIX
    large-language-models, multimodal-llm, vision-language-model, video-language-model, llm-pretraining, generative-ai, computer-vision, natural-language-processing, iclr-2024, icml-2024
  • mediumreadme#2
    Add a clear statement about the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under the terms specified in the `LICENSE` file. Please refer to that file for full details on usage and distribution.
  • lowreadme#3
    Introduce a "Key Features" section in the README

    Why:

    COPY-PASTE FIX
    ## Key Features

*   **Unified Language-Vision Pretraining:** LaVIT and Video-LaVIT offer a single, unified framework for both visual understanding and generation.
*   **Multi-modal Foundation Models:** General-purpose models designed to empower LLMs with comprehensive visual content capabilities.
*   **Dynamic Discrete Visual Tokenization:** (LaVIT) Innovative approach for efficient visual processing.
*   **Decoupled Visual-Motional Tokenization:** (Video-LaVIT) Specialized tokenization for robust video-language pre-training.

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 jy0205/LaVIT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Flamingo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Flamingo · recommended 1×
  2. Video-LLaMA · recommended 1×
  3. BLIP-2 · recommended 1×
  4. Open-VCLIP · recommended 1×
  5. InternVideo · recommended 1×
  • CATEGORY QUERY
    How to empower large language models to understand and generate visual content?
    you: not recommended
    Show full AI answer
  • CATEGORY QUERY
    What are the best unified frameworks for pre-training LLMs with video and language?
    you: not recommended
    AI recommended (in order):
    1. Flamingo
    2. Video-LLaMA
    3. BLIP-2
    4. Open-VCLIP
    5. InternVideo
    6. PandaGPT

    AI recommended 6 alternatives but never named jy0205/LaVIT. 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 jy0205/LaVIT?
    pass
    AI named jy0205/LaVIT explicitly

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

  • If a team adopts jy0205/LaVIT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jy0205/LaVIT 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 jy0205/LaVIT solve, and who is the primary audience?
    pass
    AI named jy0205/LaVIT explicitly

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

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jy0205/LaVIT — 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